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8 Examples of Innovative Digital Transformation Case Studies (2024)

• January 19, 2022

With the rapid pace of technological advancement, every organization needs to undergo digital transformation and, most likely, transform multiple times to stay relevant and competitive. 

However, before you can reap the benefits of new technology, you must first get your customers and employees to adapt to this change successfully—and here lies a significant digital transformation challenge.

Organizations thriving in this digital-first era have developed digital innovation strategies prioritizing the change management mindset. This paradigm shift implies that organizations should continuously explore improving business processes .

8 Best Examples of Digital Transformation Case Studies in 2024

• Amazon Business
• Under Armour
• Internet Brands®
• Michelin Solutions

8 Examples of Inspiring Digital Transformation Case Studies

While digital transformation presents unique opportunities for organizations to innovate and grow, it also presents significant digital transformation challenges . Also, digital maturity and levels of digital transformation by sector vary widely.

If you have the budget, you can consider hiring a digital transformation consulting company to help you plan your digitization. However, the best way to develop an effective digital transformation strategy is to learn by example. 

Here are the 8 inspiring digital transformation case studies to consider when undertaking transformation projects in 2024:

1. Amazon extended the B2C model to embrace B2B transactions with a vision to improve the customer experience.

Overview of the digital transformation initiative

Amazon Business is an example of how a consumer giant transitions to the B2B space to keep up with the digital customer expectations. It provides a marketplace for businesses to purchase from Amazon and third parties. Individuals can also make purchases on behalf of their organizations and integrate order approval workflows and reporting.

The approach

• Amazon created a holistic marketplace for B2B vendors by offering over 250 million products ranging from cleaning supplies to industrial equipment. 
• It introduced free two-day shipping on orders worth $49 or more and exclusive price discounts. It further offered purchase system integration, tax-exemption on purchases from select qualified customers, shared payment methods, order approval workflows, and enhanced order reporting.
• Amazon allowed manufacturers to connect with buyers & answer questions about products in a live expert program.
• Amazon could tap the B2B wholesale market valued between $7.2 and $8.2 trillion in the U.S. alone.
• It began earning revenue by charging sales commissions ranging from 6-15% from third-party sellers, depending on the product category and the order size.
• It could offer more personalized products for an improved customer experience. 

2. Netflix transformed the entertainment industry by offering on-demand subscription-based video services to its customers.

Like the video rental company Blockbuster, Netflix also had a pay-per-rental model, which included DVD sales and rent-by-mail services. However, Netflix anticipated a change in customer demand with rising digitalization and provided online entertainment, thereby wiping out Blockbuster – and the movie rental industry – entirely. 

• In 2007, Netflix launched a video-on-demand streaming service to supplement their DVD rental service without any additional cost to their subscriber base.
• It implemented a simple and scalable business model and infused 10% of its budget in R&D consistently.
• The company has an unparalleled recommendation engine to provide a personalized and relevant customer experience. 
• Netflix is the most popular digital video content provider, leading other streaming giants such as Amazon, Hulu, and Youtube with over 85% market share.
• Netflix added a record 36 million subscribers directly after the start of the COVID-19 pandemic.

3. Tesla uses connected car technology and over-the-air software updates to enhance customer experience, enable cost savings, and reduce carbon emissions.

No digital transformation discussion is complete without acknowledging the unconventional ideas implemented by Elon Musk. Tesla was a huge manifestation of digital transformation as the core motive was to prove that electric cars are better than their gasoline counterparts both in looks and performance. 

Over the years, Tesla has innovated continuously to improve its product, make itself more economical, and reduce its carbon footprint. 

• Tesla is the only auto manufacturer globally, providing automatic over-the-air firmware updates that allow its cars to remotely improve their safety, performance, and infotainment capabilities. For example, the OTA update could fix Tesla’soverheating issues due to power fluctuation. 
• Tesla launched an autopilot feature to control the speed and position of the car when on highways to avoid potential accidents. However, the user still has to hold the wheel; the vehicle controls everything else. This connected car technology has created an intelligent data platform and smart autonomous driving experience.
• Tesla further ventured into a data-driven future, and it uses analytics to obtain actionable insights from demand trends and common complaints. A noteworthy fact is that the company has been collecting driving data from all of its first and second-generation vehicles. So far, Tesla has collected driving data on 8 billion miles while Google’s autonomous car project, Waymo , has accumulated data on 10 million miles.
• Tesla’s over-the-air updates reduce carbon emissions by saving users’ dealer visits. Additionally, these updates save consumers time and money.
• Tesla delivered a record 936,172 vehicles in 2021, an 87 % increase over the 499,550 vehicle deliveries made in 2020.

4. Glassdoor revolutionized the recruitment industry by allowing employees to make informed decisions.

Glassdoor is responsible for increasing transparency in the workplace and helping people find the right job by allowing them to see millions of peer-to-peer reviews on employers, including overall company culture, their CEOs, benefits, salaries, and more. 

• Glassdoor gathers and analyzes employee reviews on employers to provide accurate job recommendations to candidates and vice-versa. It also allows recruitment agencies and organizations to download valuable data points for in-depth analysis & reporting. 
• It further introduced enhanced profiles as a paid program, allowing companies to customize their content on the Glassdoor profiles, including job listings, “Why is it the Best Place to Work” tabs, social media properties, and more. This gives companies a new, innovative way to attract and recruit top talent.
• Glassdoor created the largest pool of interview questions, salary insights, CEO ratings, and organizational culture via a peer-to-peer network, making it one of the most trustworthy, extensive jobs search and recruiting platforms – and one of the most well-recognized review sites
• Glassdoor leverages its collected data for labor market research in the US. Its portfolio of Fortune’s “Best Companies to Work For” companies outperformed the S&P 500 by 84.2%, while the “Best Places to Work” portfolio outperformed the overall market by 115.6%.

5. Under Armour diversified from an athletic apparel company to a new data-driven digital business stream to transform the fitness industry.

Under Armour introduced the concept of “Connected Fitness” by providing a platform to track, analyze and share personal health data directly to its customers’ phones.

• Under Armour acquired several technology-based fitness organizations such as MapMyFitness, MyFitnessPal, and European fitness app Endomondo for a combined $715 million to obtain the required technology and an extensive customer database to get its fitness app up and running. The application provides a stream of information to Under Armour, identifying fitness and health trends. For example, Under Armour (Baltimore) immediately recognized a walking trend that started in Australia, allowing them to deploy localized marketing and distribution efforts way before their competitors knew about it.
• Under Armour merged its physical and digital offerings to provide an immersive customer experience via products such as Armourbox. The company urged its customers to go online and share their training schedule, favorite shoe style, and fitness goals. It used advanced analytics to send customers new shoes or apparel on a subscription basis, offering customers a more significant value over their lifetime.
• It additionally moved to an agile development model and data center footprint with the ERP SAP HANA . 
• Under Armour additionally leveraged Dell EMC’s Data Protection and Dell Technologies to help fuel digital innovation and find peak value from its data.
• Under Armour created a digital brand with a strong consumer focus, agility, and change culture. 
• With the Connected Fitness app, it provided a customer experience tailored to each consumer.

6. Internet Brands® subsidiary Baystone Media leverages Whatfix DAP to drive product adoption of its healthcare businesses.

Baystone Media provides end-to-end marketing solutions for healthcare companies by providing a low-cost, high-value subscription offering of Internet Brands® to promote their practices digitally. Baystone Media empowers its customers by offering a codeless creation of personalized websites. However, as its userbase is less tech-savvy, customers were unable to make the most of their solution. 

The idea was to implement a solution for Baystone Media & its sister companies to enable its clients to navigate its platforms easily. In addition to PDFs and specific training videos, the search was on for a real-time interactive walkthrough solution, culminating with Whatfix .

Baystone media saw a 10% decrease in inbound calls and a 4.17% decrease in support tickets, giving them the runway to spend more time enhancing its service for the clients.

7. Sophos implemented Salesforce to streamline its business and manage customer relations more effectively.

Sophos went live with Salesforce to accelerate its sales process , enhance sales productivity , and increase the number of accounts won. However, the complex interface and regular updates of Salesforce resulted in a decreased ROI. 

• Sophos implemented Whatfix to provide interactive, on-demand training that helped users learn in the flow of work. The 24*7 availability of on-demand self-support, contextual guidance, and smart tips allowed Sophos to manage its new CRM implementation effectively. 
• It unified internal communications using Whatfix content. First, they created walkthroughs for the basic functionality of Salesforce such as lead management, opportunities, etc. Next, they moved to slightly more complex features that their users were uncomfortable with and created guided walkthroughs and smart pop-ups. Sophos also used Whatfix to align the sales and product management teams by embedding videos and other media to unify product communication instead of relying on various communication tools.
• Sophos experienced a reduction in sales operations support tickets globally by 15% (~12,000 tickets). It saved 1070 man-hours and achieved an ROI of 342%. 

8. Michelin Solutions uses IoT & AI to provide customers with a more holistic mobility experience.

The digital strategy of Michelin Solutions has essentially centered around three priorities:

• Creating a personalized relationship with customers and end-users
• Developing new business models
• Improving their existing business processes 
• AI is extensively used in R&D, enabling the digital supply chain driven through digital manufacturing and predictive maintenance. For example, connected bracelets assist machine operators with the manufacturing process. 
• It deployed sophisticated robots to take over the clerical tasks and leveraged advanced analytics to become a data-driven organization. 
• Offerings such as Effifuel & Effitires resulted in significant cost savings and improved overall vehicle efficiency. 
• Michelin Solutions carefully enforced cultural change and launched small pilots before the change implementation . 

• Effifuel led to extra savings for organizations and doubled per-vehicle profits.
• A reduction in fuel consumption by 2.5 L per 100km was observed which translates into annual savings of €3,200 for long-haul transport (at least 2.1% reduction in the total cost of ownership & 8 tonnes in CO2 emissions).
• Michelin Solutions shifted its business model from selling tires to a service guaranteeing performance, helping it achieve higher customer satisfaction, increased loyalty, and raised EBITDA margins.

Each industry & organization faces unique challenges while driving digital transformation initiatives. Each organization must find a personalized solution and the right digital transformation model when implementing new technology. Their challenges can prepare you better for the potential roadblocks, but the specific solutions will need to be personalized according to your business requirements.

Open communication with your customers and employees will help you spot potential issues early on, and you can use case studies like these as a starting point.

If you would like to learn how you can achieve these results by using a digital adoption platform , then schedule a conversation with our experts today.

Request a demo to see how Whatfix empowers organizations to improve end-user adoption and provide on-demand customer support

Home / Resources / ISACA Journal / Issues / 2021 / Volume 5 / Technology Modernization Digital Transformation Readiness and IT Cost Savings

Case study: technology modernization, digital transformation readiness and it cost savings.

“Digital Distinction” is a major trend for growing, medium-sized organizations, with growth requiring a well-executed digital platform enabled by foresight, leadership and accountability that helps ensure that societal needs are addressed with limited input resources. 1

This digital distinction story was performed with limited resources in a multiservice urban Aboriginal agency (the Agency) providing holistic, culture-based programs and services for Aboriginal children and families. The Agency strives to provide a life of quality, well-being, healing, and self-determination for children and families in the Toronto, Ontario, Canada, urban Aboriginal community by implementing a service model that is culture based and respects the values of Aboriginal people, the extended family and the right to self-determination.

The Agency faced considerable technology challenges at the start of the pandemic-induced lockdowns. The mandatory move to a remote service model stressed the existing IT infrastructure to such an extent that it exposed issues such as network bottlenecks, Wi-Fi interruptions and landline unreliability, all of which compromised the ability of social workers to perform their duties. It had become evident to management that the Agency needed significant digital transformation as part of the journey toward the increasing virtualization of social services and a much-needed modernization of its base IT infrastructure.

To be effective, however, digital transformation must build on an IT foundation that ensures reliable and sustainable outcomes. While IT modernization is a necessary condition for digital transformation readiness, 2 it is not a sufficient condition. Readiness must identify and address all IT operating model gaps 3 before innovation; unfortunately, many organizations undertaking transformation are not ready for innovation. 4

An unprepared organization is likely to see its digital transformations flounder;

…barely one in eight are successful. Even worse, only 3 percent of … 1,733 business executives … report any success at sustaining the change required for successful digital transformation…. 5

Thus, the Agency needed improved digital capabilities to support its growth and to increase its agility in response to the pandemic, so it engaged an experienced digital transformation consultancy with one executive from the group serving in the role of interim chief information officer (CIO).

The CIO title of the 1980s 6 has evolved to become one of vision as part of enterprise strategy, of managing risk as part of enterprise risk and of managing a governed high-performance team to sustain today’s ever more complex IT ecosystems. The modern CIO creates new operating models and helps the organization become data-driven. 7 The CIO takes the organization forward “… in ways that extract the maximum value from the information on hand…to make better decisions, faster” 8 as articulated in the new data strategy.

This case study articulates all the listed requirements of the modern CIO from vision to risk management to creating high performance teams as part of IT operating model modernization. Furthermore, down the road, there will be sufficient material for a future case study to document the path of the organization to achieving fit-for-purpose data for data-driven decision-making and improved reporting efficiency.

THE AGENCY’S INTERIM CIO’S FIRST STEP WAS…TO ESTABLISH THE ORGANIZATION’S CURRENT STATE TO DETERMINE ITS STATE OF READINESS FOR THE REQUIRED DIGITAL TRANSFORMATION.

The challenge: assessing the current state.

One cannot create a strategy without knowing the current state. The Agency’s interim CIO’s first step was, therefore, to establish the organization’s current state to determine its state of readiness for the required digital transformation. While tools facilitating readiness include staff surveys, 9 benchmarking and determining the business case for IT change, a survey was selected as the right tool to learn about the organization’s IT challenges (what the users experience), its IT priorities (what the users want fixed first) and its IT value chain performance (how IT creates value for the organization) through the lens of four different levels of stakeholders. The survey was distributed to staff at all levels; the output presented an end-user view of the organization’s current state.

The four key findings from the survey across these categories were:

• The organization’s executives had different perceptions of the frequency of the top IT challenges compared to the rest of the staff complement ( figure 1 ). This could be given that they were more aware of the negative impact of various IT failures on their mandate.
• The frontline staff were the most supportive of prioritizing all of the top items compared to management, who saw the priorities differently ( figure 2 ). This highlights the importance of engaging with people most actively using technology and not to depend only on management feedback for insights in this respect.
• The supervisor level experienced the severity of most of the shortcomings along the IT value chain ( figure 3 ).
• One of the major challenges experienced by end users was that it took too long for IT to fix IT issues, with users perceiving that it was getting worse. The same held for the network; network reliability was decreasing ( figure 4 ).

The fact that the survey highlighted IT challenges such as poor service request and incident management (the service desk item in figure 1 ) is more important than it may seem at first glance. As part of the journey to making IT more approachable and customer-centric, it is important that the service desk works flawlessly, as it is a major driver of staff (customer) satisfaction, which, incidentally, should be a key IT metric for any CIO.

A comparison of the actual ratio with the benchmark ratios above confirmed a historical underinvestment in IT. Reducing underinvestment in IT and addressing the associated risk areas while building future IT capabilities should be high, not only on the CIO’s agenda via IT governance, but on the board’s agenda, given the implications for enterprise governance.

The Solution: Addressing the Priority Current State Shortcomings

As a result of the current state findings, the CIO reconsidered improvements and developments that may impact the entire IT operating model. A restitution strategy was developed to address as many of the identified priority shortcomings as possible in the shortest possible time.

ADDRESSING THE NETWORK SHORTCOMINGS REQUIRED SIGNIFICANT PLANNING AND ACTIVITY, GIVEN THAT THE NETWORK WOULD NEED TO BE MODERNIZED WHILE THE AGENCY WAS STILL PERFORMING ITS MANDATE.

Restitution is about partnerships, though, another modern CIO imperative. Non-IT senior leaders are just as accountable for decisions and the delivery of ongoing IT services. 12 In other words, restitution is an organizational challenge rather than only an IT challenge, a fact that impacted the nature of the stakeholders identified to oversee the initiative. The more a CIO engages in stakeholder relationships with the goal of forging partnerships, the more effective the broad diversity of IT initiatives within the CIO’s portfolio must almost automatically become.

In this case, restitution was performed in 1) a technology stream and 2) an IT governance stream. (A data governance stream was also recently introduced but will not be explored further here.) The relationship between the CIO and IT governance took a major leap forward a decade ago when it was explicitly considered in South Africa’s King III code for corporate governance. 13 However, more than five years later, the focus still tended to be on the use of IT in regulation and compliance, 14 rather than being about the organizational performance and value creation mechanism it is meant to be.

Aligned with digital transformation principles, specifically around the operating model readiness, 15 restitution was not only about technology, but also about other important components of the organization’s operating model, such as people, process and governance.

Technology Stream

From the current state analysis, the Agency’s legacy technology landscape suffered extended maintenance, support, integration, security, and agility risk and constraints. Technology modernization projects ( figure 5 ) were identified for the Agency to address these issues while also addressing most of the user-defined IT priorities identified in the survey.

One of the CIO’s primary objectives was to measure the benefits of each IT intervention, whether they be through enhanced activity, cost savings, risk mitigation or potentially even revenue generation. Cost and activity benefits, where the interventions are complete, are highlighted for the various interventions the Agency undertook.

Network Remediation The annual operating cost of the Agency’s new network is 48 percent of the cost of the old network—savings driven largely by deploying a modern network technology with standardizing network devices using a modern network protocol.

The old network had nonstandard devices that were unmaintained, outdated with no active support, not configured according to industry best practices and had no redundancy. Furthermore, it suffered bottlenecks, single points of failure and cybersecurity vulnerabilities, with costly management implications.

Addressing the network shortcomings required significant planning and activity, given that the network would need to be modernized while the Agency was still performing its mandate. It involved an initial network discovery process that, for example, identified Internet Protocol (IP) addresses, the devices linked to the IP addresses, the functions and roles of various servers, the portfolio of critical applications, and network-based processes that needed to be mapped out and well understood. Backout plans and vendor escalation processes were created. Replacing more than 50 switches and several firewalls within a 36-hour window was challenging, especially for a new network topology in an overall process that took up to a year when including the planning and vendor identification/selection processes.

Network remediation addressed technical cybersecurity vulnerabilities, fault tolerance and failover readiness with redundancy. It also provided greater bandwidth, scalability and manageability, with Software-Defined Wide Area Network (SD-WAN) technology proving to be more secure and providing higher performance compared to the Multiprotocol Label Switching (MPLS) technology it replaced. While bandwidth demand tripled during the pandemic, it was all reliably and seamlessly accommodated within the new network architecture.

Strategically, the organization seeks to share its IT environment with smaller social services agencies that might be insufficiently funded to develop appropriately functional IT platforms. The Platform as a Service (PaaS) aspiration required a network architecture designed to handle traffic at scale and the recognition that an additional network engineer would be needed to bring this aspiration to life.

Human Productivity Tools The annual operating cost of the Agency’s new human productivity tools (HPTs) is 39 percent of the cost of the old HPTs.

The old portfolio of HPTs was a disparate set of vendor solutions that were difficult to support, offered relatively little functionality, challenged the implementation of integrated security, and were costly to manage.

A key consideration was to ensure that all data stayed within Canada. A hybrid approach was followed leveraging Active Directory Federation Services (AD FS) with Azure that allowed for failover from on-premises to the cloud, while moving all users’ mailboxes and enabling the additional functionality into production. This parallel process took six months from planning and vendor identification to deployment.

The Agency’s new Software as a Service (SaaS) HPT offered vast improvements in functionality across multiple end-user devices, such as facilitating engagement and teamwork; application interoperability; and facilitating a single approach to cybersecurity by means of integrated identity and access management. This deployment is a critical lever for successful digital transformation given benefits such as performance, scalability, security, and reliable and integrated support from the vendor. 16

Case Management A single case management system to integrate the agency’s two case management systems was identified ( figure 5 ). Two systems were deployed as a means to address the data collection shortcomings in each. To address this, a thorough business requirements document (BRD) will be created to facilitate a request for proposal (RFP) process to identify whether an integrated case management tool is available. (This will not be discussed further as it is a separate, significantly larger project that has only recently been instantiated.)

Document Management A document and content management system— coupled with appropriate workflows and governance—was needed to manage the intranet; perform as a repository for digitized, historical paper-based case files; perform document management; and provide a basis for operational metadata management and the organization’s data dictionary. A feasible tool and functionality was included in the software package provided for the HPT stream, coming in as a cost saving relative to the next best alternative. A decision was taken to use this tool given this cost benefit. A configuration and deployment plan was not yet in place at the time of writing.

Incident Management An incident management tool had been deployed at the Agency but without supporting processes or governance. There was no ticket escalation process, no ticket auto-allocation process and no feedback loop to the requester that a ticket had been received. The following were established as part of the Agency’s IT department’s emerging ITIL- alignment aspirations to improve incident management performance:

• Defined incident management processes
• Defined incident management responsibilities
• Feedback loops with workflows
• Service-level agreement (SLA)-driven ticket auto-escalation

The operational impact of these changes is evident in figure 6 . Within seven months after implementation and as the subject of continuous improvement during that time and beyond, the average ticket closing time had decreased from 34 days to three days according to the system logs, and the average ticket assignment time had decreased from 140 minutes to nine minutes according to the same logs. There are further initiatives to use more of the functionality of the selected tool in the future.

Additional service desk functionality deployed at the Agency includes IT asset management and a configuration management database.

THE ANNUAL OPERATING COST OF THE AGENCY’S NEW MONITORING AND PATCHING SYSTEM IS 30 PERCENT OF THE COST OF THE OLD VENDOR SOLUTION.

Monitoring and Patching System The annual operating cost of the Agency’s new monitoring and patching system is 30 percent of the cost of the old vendor solution.

Driven by continuity risk factors such as poor outage monitoring and alerting, poor device monitoring, and poor vendor responsiveness, as well as cybersecurity risk factors such as poor patching, the Agency sought and deployed a tool to fulfill these requirements with remote management capability.

The technology was selected based on a review of this specific technology landscape according to various IT research organizations. Then, deploying the monitoring tool required making changes to the firewall to allow agents to communicate. Furthermore, a cache server was set up to reduce the bandwidth implications of all the computers in the Agency requiring similar updates, thereby reducing the possibility of network congestion. Planning, vendor identification and deployment took less than three months.

Cloud The annual operating cost of the Agency’s new cloud data center is 45 percent of the cost of the on-premises data center, driven by the higher support and equipment costs of maintaining an on-premises environment.

THE ANNUAL OPERATING COST OF THE AGENCY’S NEW CLOUD DATA CENTER IS 45 PERCENT OF THE COST OF THE ON-PREMISES DATA CENTER.

The Agency had historically entered into a five-year contract for its data center, with further expenditure required for power to eight servers, hosting facilities and equipment, an uninterruptible power supply, and management time for maintenance and management. The risk of the data center being an operational bottleneck was considerable. The real push for a work-in-progress cloud migration was driven by the pandemic.

The selection of the cloud vendor was based on a review of the findings by various IT research organizations and the need to ensure interoperability between the various tools that were about to be deployed in the cloud. For the software tools, a primary driver was the effectiveness of the solution to serve well in a Software as a Service (SaaS) paradigm, which will be the foundation for the type of incremental transformational functionality envisaged as a strategic driver of future IT at the Agency.

Configuring a cloud infrastructure requires configuration activities such as subscribing to the services, creating virtual machine(s), the virtual private network (VPN) and the VPN gateway. Additional services that were migrated to the cloud or deployed to the cloud include the HPTs, the monitoring and patching services, and the mail system. The planning, vendor identification and deployment was performed within four months.

The operational, scale and cost advantages of the cloud at a stated availability of 99.999 percent were implemented as a desirable alternative to on-premises services, given that the modern CIO’s role is to create an environment that facilitates on-demand technology and related services. 17 The potential of this migration for future Platform as a Service (PaaS) services, virtual computing, storage and on-demand functionality positions the organization well for an enhanced digital future.

Telephony Telephony depends on a stable network, and the organization is now ready to address its telephony shortcomings. An architecture and plan to migrate between the current state and the proposed state for telephony is being developed, with the major goals being scalability as part of the PaaS vision for the organization and redundancy, given, the always- on requirement of child welfare services.

Financial Summary IT underinvestment introduces significant risk and inefficiencies into an organization. The technology modernization stream not only addressed technology risk at the Agency, it also eliminated architectural inefficiencies and high-cost structures, as demonstrated by the annual cost savings achieved ( figure 7 ).

While cost savings of up to 13 percent are expected in technology modernization, 18 savings of 18 percent were realized.

IT Governance Stream

IT governance ensures that IT produces the value expected of it. While IT governance was introduced as a mechanism for CIO oversight of the technology deployments, less tangible activities were also established by means of the IT governance stream to help establish a vision for IT, to reduce IT risk and to extend the people capabilities of the IT department.

The following sections detail the measures taken to help ensure reduced-risk value delivery from IT.

Policies and Processes Procedural and cybersecurity-related updates were made to the Agency’s IT policy. Processes were also co-created with human resources (HR) (e.g., onboarding, offboarding) and with operations (e.g., IT-facilitated process design for the handling of all possibilities of incoming telephone calls) to ensure that handovers to IT and back to HR and operations were clear, and that people had been identified in the process to accept handovers.

If an operational process needs engagement with IT, operations must co-design the process with IT to manage expectations and to reduce operational risk. Failing to do this will result in failed processes, given no awareness or clarity of IT’s role in the process.

AS A RISK CONTROL, A PASSWORD VAULT WAS CREATED FOR ALL APPLICATION AND SYSTEM PASSWORDS, SUPPORTED BY A PROCESS THAT COULD BE ACCESSED BY THE EXECUTIVE TEAM IN AN EMERGENCY.

Risk Management Risk management is a key pillar of effective IT governance. Together with policies and procedures as a critical part of effective risk management, 19 IT implemented a risk management process—Identify, Assess, Respond, Control, Monitor—with a living risk register as a monitoring and communication tool as a means to help minimize potentially negative differences between expected IT outcomes and the actual IT outcomes. The process emphasized assigning responsibility for a risk control at the point where risk is realized. Periodic IT governance meetings were established as a means to monitor changes in IT environment risk and to monitor the effectiveness of the risk controls.

Key administrator passwords held in people’s heads was a major operational and sustainability risk. As a risk control, a password vault was created for all application and system passwords, supported by a process that could be accessed by the executive team in an emergency.

Structure and People People are the most critical part of IT because they determine whether something is done well. To effect and to sustain digital transformation, IT staff must have digital mindsets; 20 be inclined to testing and learning, innovation, and agility; 21 have diverse technology knowledge, deep data skills, rich process skills, and end-to-end mindsets that includes teamwork, courage, and change management. 22

Sustainable digital transformation, thus, requires “t- shaped” people—staff with deep knowledge of their areas of expertise and broad knowledge that they can apply to solve the types of new problems that emerge under transformation. 23 T-shaped people are especially important in small IT teams, where broad knowledge overlap mitigates the continuity risk of a small staff complement.

Digital transformation demands agility—people fluidly structuring around problems or challenges in cross-functional teams 24, 25 rather than constrained within traditional organizational structures. Compromising on IT competence has been described as a subtle and even a dangerous issue in digital transformation. 26

“Build the organization,” “run the organization” and “transform the organization” 27 was adopted as the IT structure paradigm. Bespoke definitions for “run the organization” and “build the organization” were developed to define their purpose and scope for the organization ( figure 8 ).

While the Agency’s IT organization managed day-to-day operations (run) and performed technology modernization projects (build) like those in figure 8 , it had unsustainable transformation. Given the organization’s growth and expansion aspirations, “transform the organization” was established as a full-time role, and an experienced leader was recruited to focus on strategy and architecture to help define the organization’s broader digital capabilities.

Strategy and Architecture The current state of the Agency was such that it had no clear IT strategy and no clear IT architecture. Many different applications had been acquired from a wide variety of vendors over time to serve specific point purposes but with no consideration for aspects such as architectural fit, integrated cybersecurity management and interoperability. The historical approach to IT tended to be tactical, with no consideration of how the tactical deployments would impact the Agency’s overall IT risk profile.

While this worked reasonably well in a low-stress IT environment, the diverse flaws in the approach quickly became apparent at the start of the pandemic—especially to end users who suffered service interruptions—when network volumes escalated significantly under work-from-home orders.

All interventions documented in the Technology Stream section were part of a significantly more architected approach—specifically around cybersecurity and interoperability—that included business cases as part of the supporting documentation and a comparison with next-best technology alternatives.

THE HISTORICAL APPROACH TO IT TENDED TO BE TACTICAL, WITH NO CONSIDERATION OF HOW THE TACTICAL DEPLOYMENTS WOULD IMPACT THE AGENCY’S OVERALL IT RISK PROFILE.

It is useful to note that unarchitected IT is a primary driver of technology debt; 28 an unwelcome gift to current IT management from former IT management as experienced in the Agency’s current IT state. While appropriate IT vendor diversity should be supported in the interest of good IT risk management, this should occur within a strategically architected framework. IT strategy and IT architecture can sustainably reduce IT risk and improve business continuity.

Data Governance Stream Digital transformation consumes data and produces more data that not only serves general reporting and decision-making, but also potentially serves government policy direction. While data were not initially identified as a problem at the Agency, a data strategy has been developed in response to some data issues identified ( figure 9 ), and in line with a vision for data for the organization. (The data strategy will not be covered further in this case study beyond the limited discussion that follows.)

CIOs strive for data consistency, data availability, information resource control and information flow visibility. 29 Not addressing data challenges results in delayed and/or incorrect data-driven decision- making and productivity compromises, and incurs unnecessary IT effort to resolve issues arising from bad data.

As a first step toward addressing data challenges, the Agency articulated its unique perspective of the drivers of a data culture as an output of a facilitated workshop series. Some of the behavioral considerations include:

• Mistrust about what data could communicate; could they show performance levels that are lower than perceived?
• That data have not been seen as something that can add value
• That data are removed from the people whose lives they represent
• That data capture is only seen as a necessary part of getting the job done, rather than as a vital part of the data value chain
• That data are not seen as distinct from IT, with operational and strategic best practices distinct from those applicable to data

It is important that ways to address these behavioral considerations are included in the organization’s data strategy. The implementation of the cultural aspect is an overarching workstream for the data work that needs to be performed over the upcoming years to create an environment rich in fit- for-purpose data. Overall, IT culture is the single greatest risk—and, therefore, critical success factor (CSF)—not only for IT governance, 30 but possibly for data governance, too.

Key Results and Benefits

As outlined, successful digital transformation requires the barriers to an effective digital strategy—processes, technology, people and governance, in that order 31 —to be addressed. Without a sound IT operating model foundation, digital transformation will exacerbate IT operating model shortcomings with predictable consequences. Figure 10 summarizes the major IT outcomes achieved. Note that the column “Technology and/or Governance Intervention” in the figure refers to the relevant item in the Technology Stream section or the Governance Stream section.

Figure 10 item 10 refers to technical cybersecurity vulnerabilities. However, the Desjardins breach in Canada 32, 33 is a shocking reminder of the scale of breach possible in the presence of even the best technological responses. People vulnerabilities are, thus, addressed through the newly established SOC at the Agency, mandated to address people matters such as cybersecurity training and to perform vendor due diligence. This closes the loop on the cybersecurity vulnerabilities identified as part of the network remediation workstream.

Other noteworthy outcomes include digital forms with workflows for efficient forms processing compared to paper forms, and improved secure video conferencing.

What Is Next?

With many of the primary activities in figure 10 having been achieved in six months across nearly 20 regional sites, there is still more work to do, with some of the major considerations being:

• Telephony, as discussed
• Case management, as discussed
• Laptop standardization, all staff
• Addressing stable and reliable power
• Modernizing the data infrastructure as the foundation required for the implementation of an organizationwide data strategy

DIGITAL DISTINCTION’ AND COST SAVINGS WERE ACHIEVED WITH LIMITED RESOURCES IN A LIMITED TIMEFRAME, AN UNUSUAL ACHIEVEMENT IRRESPECTIVE OF ORGANIZATION SIZE OR RESOURCES.

Of these, the data infrastructure will likely be the highest cost future intervention. This will require not only technology, but a full data operating model to support the growing day-to-day requirements for data and reporting in the organization. From a CIO perspective, formally aligned organizational strategy and IT strategy interventions ultimately help minimize digital strategy execution gaps, 34 the difference between what an organization aspires to achieve strategically, and what it actually achieves.

Organizations trust the CIO to ensure that the technology ecosystem is a functional and reliable enabler of the organization’s operations. 35 This means that the role has significant fiduciary responsibilities requiring high performing, t-shaped people. Digital transformation needs executive support and visibility, and credit is due to the head of the organization, the head of finance and administration, and the head of human resources (HR) for their encouragement during some of the darkest hours of this process. Thanks are due also to the extraordinary performance of a small, but mighty and highly motivated IT team willing to go so significantly beyond the extra mile for months on end.

This case study details the types of CIO leadership needed for digital transformation readiness and technology modernization, aligned with an approach published in ISACA ® Journal . 36 “Digital distinction” and cost savings were achieved with limited resources in a limited timeframe, an unusual achievement irrespective of organization size or resources. The organization is now positioned to increasingly redirect IT spend from operations to digital innovation 37 as reward for its courageous efforts.

1 El Tarabishy, A.; “The Top 10 Micro, Small, and Medium Enterprises Trends for 2021,” International Council for Small Business, 6 July 2020, https://icsb.org/toptrends2021 2 Avanade, “IT Modernization: Critical to Digital Transformation,” March 2017, https://www.avanade.com/-/media/asset/white-paper/avanade-it-modernization-whitepaper.pdf 3 Pearce, G.; “Digital Transformation Governance: What Boards Must Know,” Governance Institute of Australia, vol. 72, no. 5, 2020, https://www.governanceinstitute.com.au/resources/governance-directions/volume-72-number-5/digital-transformation-governance-what-boards-must-know/ 4 Bendor-Samuel, P.; “Four Guidelines for Success in Innovation in Digital Transformation,” Forbes , 23 July 2019, https://www.forbes.com/sites/peterbendorsamuel/2019/07/23/four-guidelines-for-success-in-innovation-in-digital-transformation/#61401a511aa9 5 Pearce, G.; “Attaining Digital Transformation Readiness,” ISACA ® Journal , vol. 1, 2020, https://www.isaca.org/archives 6 Rivier University Nashua, New Hampshire, USA, “The Growing Importance of a CIO in Today’s Evolving Business World,” Boston Business Journal , 16 March 2020, https://www.bizjournals.com/boston/news/2020/03/16/the-growing-importance-of-a-cio-in-today-s.html 7 Op cit McLaughlin 8 Op cit Rivier University 9 Ibid. 10 Morley, L.; “How Much Should a Company Spend on IT?,” Techvera, https://blog.techvera.com/company-it-spend 11 Avasant Research; “IT Spending as a Percentage of Revenue by Industry, Company Size, and Region,” Computer Economics , https://www.computereconomics.com/article.cfm?id=2626 12 CIO Journal , “The Role of Senior Leaders in IT Governance,” The Wall Street Journal , 22 June 2015, https://deloitte.wsj.com/articles/the-role-of-senior-leaders-in-it-governance-1434945783?tesla=y 13 IT Governance Network; “The CIO and IT Governance,” https://www.itgovernance.co.za/3/index.php/general-articles/176-the-cio-and-it-governance 14 De Haes, S.; A. Joshi; T. Huygh; S. Jansen; Board Level IT Governance Research Project , Antwerp Management School, Belgium, September 2016, https://assets.kpmg/content/dam/kpmg/be/pdf/2018/05/Corporate_Governance_Codes_and_Digital_leadership.pdf 15 Op cit Pearce, “Attaining Digital Transformation Readiness” 16 Sharma, A.; “Application Modernization: One of the Critical Levers of Digital Transformation,” CIO , 30 July 2020, https://cio.economictimes.indiatimes.com/news/strategy-and-management/application-modernization-one-of-the-critical-levers-of-digital-transformation/77253867 17 Dogan, C.; From the Basement to the Cloud: The Role of the CIO Over Four Decades , Deloitte Consulting, USA, 2018, https://www2.deloitte.com/content/dam/Deloitte/ar/Documents/technology/THE-ROLE-OF-THE-CIO-OVERF-OUR-DECADES.pdf 18 Op cit Avanade 19 Amadei, L.; “Why Policies and Procedures Matter,” Risk Management , 1 November 2016, http://www.rmmagazine.com/2016/11/01/why-policies-and-procedures-matter/ 20 Op cit Dogan 21 Annacone, A.; “The Four Types of Digital Transformation,” TechNexus on Linkedin, 19 June 2019, https://www.linkedin.com/pulse/4-types-digital-transformation-andrew-annacone/ 22 Davenport, T. H.; T. C. Redman; “Digital Transformation Comes Down to Talent in Four Key Areas,” Harvard Business Review , 21 May 2020, https://hbr.org/2020/05/digital-transformation-comes-down-to-talent-in-4-key-areas 23 Rowles, D.; T. Brown; Building Digital Culture , Kogan Page, United Kingdom, 2017 24 Ghosh, A.; “Digital Transformation of the Workplace,” India Inc., 19 November 2020, https://indiaincgroup.com/digital-transformation-of-the-workplace/ 25 Penfold, P.; “HR Strategies That Help Digital Transformation Succeed,” People Matters, 22 November 2019, https://www.peoplemattersglobal.com/article/hr-technology/hr-strategies-that-help-digital-transformation-succeed-23829 26 Op cit Rowles and Brown 27 Apptio, IT Financial Metrics Primer , USA, https://dsimg.ubm-us.net/envelope/151893/296392/1390318118_WP_-_Apptio_IT_Financial_Metrics_Primer.pdf 28 Dalal, V.; R. Patenge; K. Krishnakanthan; “Tech Debt: Reclaiming Tech Equity,” McKinsey Digital, 6 October 2020, https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/tech-debt-reclaiming-tech-equity# 29 Op cit Dogan 30 Pearce, G.; “The Sheer Gravity of Underestimating Culture as an IT Governance Risk,” ISACA Journal , vol. 3, 2019, https://www.isaca.org/archives 31 Op cit Pearce, “Attaining Digital Transformation Readiness” 32 The Canadian Press, “Desjardins Says Employee Who Stole Personal Data Also Accessed Credit Card Info,” BNN Bloomberg, 10 December 2019, https://www.bnnbloomberg.ca/desjardins-says-employee-who-stole-personal-data-also-accessed-credit-card-info-1.1360652 33 The Canadian Press, “Series of Gaps Allowed Massive Desjardins Data Breach, Privacy Watchdog Says,” CTV News, 14 December 2020, https://www.ctvnews.ca/business/series-of-gaps-allowed-massive-desjardins-data-breach-privacy-watchdog-says-1.5230179 34 Pearce, G.; “Digital Governance: Closing the Digital Strategy Execution Gap,” ISACA Journal , vol. 2, 2020, https://www.isaca.org/archives 35 Edelman, D. J.; “CIO in Focus: A Global Study,” USA, 2020, https://www.edelman.com/expertise/technology/cio-in-focus 36 Op cit Pearce, “Attaining Digital Transformation Readiness” 37 Halfteck, D.; “Six Steps to Ensure IT Readiness to Drive Digital Transformation,” Access IT Automation, 16 May 2019

Guy Pearce, CGEIT, CDPSE

Has served on governance boards in banking, financial services and a not-for-profit, and as chief executive officer (CEO) of a financial services organization. He has taken an active role in digital transformation since 1999, experiences that led him to create a digital transformation course for the University of Toronto School of Continuing Studies (Ontario, Canada) in 2019. Consulting in digital transformation and governance, Pearce shares more than a decade of experience in data governance and IT governance as an author and as a speaker. He was awarded the ISACA® 2019 Michael Cangemi Best Author award for contributions to IT governance, and he is chief digital officer and chief data officer at Convergence.Tech.

Richard Fullerton, AWS CSA, ITIL, MCAAA, VCP-DCV

Is the IT manager at Native Child and Family Services of Toronto, Ontario, Canada. He is a solutions-oriented IT professional with more than 20 years of experience in the organization and delivery of end-to-end IT projects involving data migrations, server upgrades and configurations, and enterprise-scale software and hardware installations. His areas of expertise include cloud (AWS, Azure, Office 365), virtualization (VMware, Hyper-V, Citrix), and identity and access management. Fullerton is an experienced technical team leader in matrix organizations. He is the recipient of multiple Distinguished Service and Project Leadership awards, and the recipient of a Service Excellence award.

10 Strategies of Change Management for Technology Implementation

In the times of rapid technological advancement, business organizations need to continually evolve and embrace smooth implementation of technology.

Whether it’s the adoption of cutting-edge software, the integration of automation processes, or the implementation of innovative digital platforms, technology is the driving force behind today’s corporate landscape.

However, beneath the allure of technological innovation lies a complex challenge of how to successfully implement technology.

The successful deployment of technology is not merely a matter of selecting the right tools; it hinges on the ability to manage and guide your organization through the often tumultuous waters of change. 

We’ll explore this topic and explain how change management helps for technology implementation.

In this comprehensive guide, we will delve into the strategies and real-world examples that will empower your business to not only embrace technological evolution but also thrive in a constantly evolving digital world. 

Join us as we embark on a journey to understand the art and science of change management in the context of technology adoption.

What is Change Management?

Change management refers to the systematic approach, strategies, and processes used to prepare, support, and guide individuals, teams, and organizations through significant transitions or transformations. 

These changes can encompass a wide range of initiatives, including the implementation of new technologies, organizational restructuring, process improvements, cultural shifts, or any substantial alterations to the status quo within an organization. 

Change management seeks to minimize resistance to change, ensure successful adoption, and achieve the desired outcomes by addressing the psychological, emotional, and operational aspects of change.

Why Change Management is Important for technology implementation?

Change management is critically important in technology implementation for several compelling reasons:

• Minimizing Resistance:  Technology changes can disrupt established routines and processes, leading to resistance from employees who may be comfortable with the status quo. Change management helps identify potential sources of resistance and employs strategies to address them, making it more likely that employees will embrace the new technology.
• Ensuring User Adoption:  A successful technology implementation is not solely about getting the technology up and running; it’s about ensuring that users effectively adopt and use it. Change management focuses on creating user buy-in, providing training and support, and helping individuals and teams transition smoothly to the new technology.
• Maximizing ROI:  Organizations invest significant resources in acquiring and implementing new technologies. To realize a return on investment (ROI), it’s crucial that the technology is not just deployed but is used effectively and efficiently. Change management helps optimize technology usage, ensuring that the organization gets the most value from its investment.
• Reducing Disruptions:  Technology implementations can cause disruptions in workflow and productivity if not managed properly. Change management strategies anticipate potential disruptions and help minimize their impact, ensuring that the business can continue to operate smoothly during the transition.
• Aligning with Business Goals:  Technology implementations are typically driven by specific business objectives, such as improving customer service, increasing efficiency, or staying competitive. Change management ensures that the technology adoption process aligns with these goals, helping the organization achieve its strategic objectives.
• Enhancing Communication:   Effective communication is a cornerstone of change management. It ensures that all stakeholders are well-informed about the technology change, its benefits, and its impact. Clear and transparent communication fosters trust and reduces anxiety and uncertainty among employees.
• Managing Expectations:  Change management helps set realistic expectations about the technology implementation process. It can communicate potential challenges and setbacks, as well as the timeline for achieving desired outcomes, helping to prevent disillusionment or frustration among employees and stakeholders.
• Mitigating Risks:  Technology implementations often involve risks, such as data security issues, integration challenges, or unforeseen technical problems. Change management assesses these risks and develops strategies to mitigate them, reducing the likelihood of project failure.
• Supporting Continuous Improvement:  Change management doesn’t end once the technology is implemented. It includes post-implementation evaluation and feedback mechanisms to identify areas for improvement. This iterative process ensures that the technology continues to evolve to meet the organization’s changing needs.
• Preserving Employee Well-being :  Change can be stressful for employees. Change management acknowledges and addresses the emotional and psychological aspects of change, promoting employee well-being and reducing the negative impacts of change-related stress.

10  Strategies for Successful Technology Implementation Through Change Management 

We’ve listed 10 strategies of change management that help organizations to enhance likelihood of successful implementation of new technology.

Let’s learn about these strategies:

1. Leadership Alignment

Leadership alignment is the foundation of successful technology implementation. When top leaders visibly support and champion the change, it sends a clear message throughout the organization that the technology adoption is a priority. Their commitment provides a model for others to follow, fostering a culture of change acceptance and innovation.

2. Stakeholder Engagement

Engaging stakeholders involves identifying and involving all relevant parties affected by the technology implementation. These stakeholders can include employees, customers, suppliers, and external partners. By actively seeking their input, concerns, and feedback, you create a sense of shared ownership in the change, making it more likely to succeed.

3. Change Champions

Change champions are enthusiastic individuals who advocate for the new technology within the organization. They play a pivotal role in guiding and motivating their peers through the change process. These champions are often early adopters who help bridge the gap between apprehensive employees and the benefits of the technology.

4. Clear Communication

Effective communication is essential for change management. A well-defined communication plan ensures that everyone in the organization understands the purpose, scope, and timeline of the technology change. Transparency in communication helps dispel uncertainties and fosters trust.

5. Training and Skill Development

Providing comprehensive training and skill development programs is vital to ensure that employees have the knowledge and competence to use the new technology effectively. Tailored training programs based on roles and responsibilities are key to building confidence and reducing resistance.

6. Pilot Testing

Before rolling out the technology organization-wide, conducting pilot tests or small-scale deployments allows for real-world testing and feedback collection. This phase enables you to identify and address any issues or challenges that may arise, enhancing the chances of a smoother full-scale implementation.

7. Change Impact Assessment

Assessing the impact of the technology change on various aspects of the organization, such as processes, job roles, and company culture, helps anticipate and manage potential sources of resistance. This assessment informs the development of mitigation strategies to address these concerns proactively.

8. Feedback Mechanisms

Establishing feedback mechanisms ensures that employees have a voice throughout the technology implementation. Regularly collecting and acting on feedback demonstrates that their opinions are valued and that the organization is responsive to their needs.

9. Incentives and Recognition

Recognizing and rewarding employees who adapt well to the technology or contribute positively to the change process can boost morale and motivation. Incentives and recognition programs encourage employees to actively engage with the new technology.

10. Change Roadmap

A clear and well-communicated change roadmap provides a structured path for the technology implementation. It outlines key milestones, timelines, and responsibilities, allowing everyone in the organization to track progress and understand the journey ahead.

Real-world examples of successful technology implementation using change management

Several organizations have successfully implemented technology changes by employing effective change management strategies.

Here are a few real-world examples:

Ford Motor Company – Agile Transformation

Ford embarked on a major agile transformation to modernize its software development processes and accelerate innovation. They adopted the Agile methodology across their global IT organization, focusing on clear communication, stakeholder engagement, and extensive training for their teams. This change management approach allowed Ford to become more responsive to market demands, reducing development time and delivering software updates faster.

Netflix – Transition to Cloud Computing

Netflix, the streaming giant, transitioned its entire infrastructure to the cloud. This shift allowed them to scale their services rapidly and improve customer experiences. Change management was integral to this process, ensuring that employees understood the benefits and implications of moving to the cloud. Clear communication, training programs, and involving stakeholders helped ensure a smooth transition.

Do check out detailed case study of Netflix change management

Procter & Gamble (P&G) – SAP Implementation

P&G, a consumer goods company, implemented a comprehensive SAP enterprise resource planning (ERP) system. They recognized the complexity of the change and employed a change management strategy that included extensive employee training, change champions in various departments, and regular feedback mechanisms. This approach helped P&G manage a significant technological shift without major disruptions to their operations.

General Electric (GE) – Digital Transformation

GE, a conglomerate, underwent a digital transformation to harness the power of the Industrial Internet of Things (IIoT). They used change management to align leaders, engage employees, and build a culture that embraced digital innovation. GE invested in employee training and emphasized the importance of data-driven decision-making, enabling them to optimize operations and deliver better products and services.

McDonald’s – Self-Service Kiosks

 McDonald’s introduced self-service kiosks in its restaurants to enhance customer experience and streamline order processing. Change management strategies were pivotal in ensuring a smooth rollout. This included providing training to employees to operate the kiosks and placing a strong emphasis on customer engagement, as employees shifted their roles from order-takers to customer-service advocates.

Do check out detailed case study of McDonald’s change management

The University of California, Berkeley – Campuswide IT Upgrade

UC Berkeley embarked on a large-scale IT upgrade, which impacted various departments and thousands of users. The university implemented change management techniques to engage faculty, staff, and students. They established clear communication channels, offered extensive training sessions, and utilized change champions within each department to address concerns and facilitate a successful transition to the new IT systems.

Common Pitfalls to Avoid 

Certainly, here’s an explanation of each of the common pitfalls to avoid in change management:

A.  Overlooking the human element

• Explanation:  One of the most common pitfalls in change management is failing to recognize the significant impact that changes can have on people within an organization. Change can generate fear, resistance, and uncertainty among employees. Overlooking the human element means neglecting the emotional and psychological aspects of change. When organizations focus solely on the technical or operational aspects of a change initiative, they risk encountering substantial resistance and a lack of employee buy-in.
• Consequences:  Overlooking the human element can lead to increased resistance, decreased morale, decreased productivity, and even project failure if employees are not properly engaged and supported during the change process.
• Mitigation:  Mitigating this pitfall involves actively addressing the emotional side of change, showing empathy, involving employees in decision-making, providing training and support, and maintaining open lines of communication to address concerns and uncertainties.

B.  Poor planning and lack of clear goals

• Explanation:  Poor planning and a lack of clear, well-defined goals are major contributors to change management failures. Without a solid plan, organizations may encounter unexpected challenges, budget overruns, and missed deadlines. Similarly, unclear goals can lead to confusion, ambiguity, and a lack of direction, making it difficult for employees to understand the purpose and expected outcomes of the change.
• Consequences:  Poor planning and unclear goals can result in project delays, scope creep, wasted resources, and a loss of confidence among stakeholders. It can also cause employees to feel disoriented and uncertain about the change’s objectives.
• Mitigation:  To address this pitfall, organizations should invest time in comprehensive project planning, define clear and measurable goals, create a detailed roadmap, allocate resources effectively, and regularly review and adjust the plan as needed throughout the change initiative.

C. Insufficient communication and engagement

• Explanation:  Inadequate communication and engagement with stakeholders, including employees, are common pitfalls in change management. When organizations fail to communicate the why, what, when, and how of the change effectively, they leave room for rumors, misinformation, and skepticism to take hold. Insufficient engagement means not involving key stakeholders in the decision-making process or not seeking their input and feedback.
• Consequences:  Inadequate communication and engagement can result in confusion, resistance, mistrust, and decreased morale among employees. It can also hinder the adoption of the change and lead to project setbacks.
• Mitigation:  To avoid this pitfall, organizations should develop a robust communication plan that includes regular updates, transparent messaging, and multiple communication channels. They should actively involve stakeholders, solicit feedback, address concerns promptly, and ensure that all parties feel included and informed throughout the change process.

Final Words 

Change management is extremely important for successful technology implementation and organizational transformation. It serves as the bridge between the technical aspects of change and the human elements within an organization. By acknowledging and addressing the emotional, psychological, and cultural aspects of change, organizations can navigate transitions with greater ease and maximize the benefits of technological advancements.

However, it is essential to remain vigilant against common pitfalls like overlooking the human element, poor planning, and insufficient communication. These pitfalls, if left unaddressed, can derail even the most promising change initiatives.

About The Author

Tahir Abbas

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Technology Implementation Case Studies: Lincus Software as a Service

• First Online: 06 September 2017

Cite this chapter

• Adie Blanchard 4 ,
• Faye Prior 4 ,
• Laura Gilbert 4 &
• Tom Dawson 4  

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Stakeholders have often failed to foresee, identify and address implementation barriers when introducing new technologies into health and social care settings. As a result, many technologies have not been adopted by organisations or have failed to be used to their full potential. This chapter is written from the perspective of a small-sized technology enterprise in the UK and critically discusses the experiences of deploying a novel ‘software as a service system’ in the UK health and social care sectors. The chapter will specifically discuss the barriers that have been faced throughout implementations and how they were addressed in a series of case studies. We propose that the lessons learned from these implementations will be just as relevant in the future as they were at the time of writing.

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Anderson, J. G. (2007). Social, ethical and legal barriers to e-health. International Journal of Medical Informatics, 76 (5), 480–483.

Google Scholar  

Anderson, G. F., Frogner, B. K., Johns, R. A., & Reinhardt, U. E. (2006). Health care spending and use of information technology in OECD countries. Health Affairs, 25 (3), 819–831.

Article   Google Scholar  

Barlow, J., Wright, C., Sheasby, J., Turner, A., & Hainsworth, J. (2002). Self-management approaches for people with chronic conditions: A review. Patient Education and Counseling, 48 (2), 177–187.

Bayer, S., Barlow, J., & Curry, R. (2007). Assessing the impact of a care innovation: Telecare. System Dynamics Review, 23 (1), 61–80.

Beuscart-Zéphir, M. C., Anceaux, F., Crinquette, V., & Renard, J. M. (2001). Integrating users’ activity modeling in the design and assessment of hospital electronic patient records: The example of anesthesia. International Journal of Medical Informatics, 64 (2), 157–171.

Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Affairs, 30 (3), 464–471.

Care Quality Commission. (2015). The state of health care and adult social care in England . [Online]. Available at: http://www.cqc.org.uk/sites/default/files/documents/6227_cqc_soc2009-10_tag.pdf . Accessed November 2016.

Cashen, M. S., Dykes, P., & Gerber, B. (2004). eHealth technology and internet resources: Barriers for vulnerable populations. Journal of Cardiovascular Nursing, 19 (3), 209–214.

Chismar, W.G., & Wiley-Patton, S. (2002). Test of the technology acceptance model for the internet in pediatrics. In Proceedings of the AMIA Symposium (p. 155). American Medical Informatics Association.

Cresswell, K., & Sheikh, A. (2009). The NHS Care Record Service (NHS CRS): Recommendations from the literature on successful implementation and adoption. Journal of Innovation in Health Informatics, 17 (3), 153–160.

Cresswell, K., & Sheikh, A. (2013). Organizational issues in the implementation and adoption of health information technology innovations: An interpretative review. International Journal of Medical Informatics, 82 (5), e73–e86.

Cresswell, K., Coleman, J., Slee, A., Williams, R., & Sheikh, A. (2013a). Investigating and learning lessons from early experiences of implementing ePrescribing systems into NHS hospitals: A questionnaire study. PloS One, 8 (1), e53369.

Cresswell, K. M., Bates, D. W., & Sheikh, A. (2013b). Ten key considerations for the successful implementation and adoption of large-scale health information technology. Journal of the American Medical Informatics Association, 20 (e1), e9–e13.

Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4 (1), 1.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 , 319–340.

Edmondson, A. C., Bohmer, R. M., & Pisano, G. P. (2001). Disrupted routines: Team learning and new technology implementation in hospitals. Administrative Science Quarterly, 46 (4), 685–716.

Forouzanfar, M. H., Alexander, L., Anderson, H. R., Bachman, V. F., Biryukov, S., Brauer, M., Burnett, R., Casey, D., Coates, M. M., Cohen, A., Delwiche, K., et al. (2015). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 386 (10010), 2287–2323.

Free, C., Phillips, G., Galli, L., Watson, L., Felix, L., Edwards, P., Patel V., & Haines, A. (2013). The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: A systematic review. PLoS Medicine, 10 (1), e1001362.

Gagnon, M. P., Duplantie, J., Fortin, J. P., & Landry, R. (2006). Implementing telehealth to support medical practice in rural/remote regions: What are the conditions for success? Implementation Science, 1 (1), 1.

Gagnon, M. P., Desmartis, M., Labrecque, M., Car, J., Pagliari, C., Pluye, P., Frémont, P., Gagnon, J., Tremblay, N., & Légaré, F. (2012). Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. Journal of Medical Systems, 36 (1), 241–277.

van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011). A holistic framework to improve the uptake and impact of eHealth technologies. Journal of Medical Internet Research, 13 (4), e111.

Gilbert, L., & Dawson, T. (2014). One precious life: Technology report . Rescon Internal Publication. https://lincus.rescontechnologies.com/ .

Gilbert, L., Blanchard, A., Barton, J., & Dawson, T. (2014). Lincus: An adaptation and pilot usability study on individuals with learning disabilities . Rescon Internal Publication. https://lincus.rescontechnologies.com/ .

Glied, S., & Lleras-Muney, A. (2008). Technological innovation and inequality in health. Demography, 45 (3), 741–761.

Grant, R., Adams, A. S., Trinacty, C. M., Zhang, F., Kleinman, K., Soumerai, S. B., Meigs, J. B., & Ross-Degnan, D. (2007). Relationship between patient medication adherence and subsequent clinical inertia in type 2 diabetes glycemic management. Diabetes Care, 30 (4), 807–812.

Gruber, D., Cummings, G. G., Leblanc, L., & Smith, D. L. (2009). Factors influencing outcomes of clinical information systems implementation: A systematic review. CIN: Computers, Informatics, Nursing, 27 (3), 151–163.

Hall, B.H., & Khan, B. (2003). Adoption of new technology (No. w9730). National Bureau of Economic Research.

Ham, C. (2010). The ten characteristics of the high-performing chronic care system. Health Economics, Policy, and Law, 5 (01), 71–90.

Hartswood, M., Procter, R., Rouncefield, M., & Sharpe, M. (2000). Being there and doing IT in the workplace: A case study of a co-development approach in healthcare. In Proceedings of the 2000 Participatory Design Conference .

Henderson, C., Knapp, M., Fernández, J. L., Beecham, J., Hirani, S. P., Cartwright, M., Rixon, L., Beynon, M., Rogers, A., Bower, P., Doll, H., Fitzpatrick, R., Steventon, A., Bardsley, M., Hendy, J., Newman, S. P; Whole System Demonstrator evaluation team. (2013). Cost effectiveness of telehealth for patients with long term conditions (Whole Systems Demonstrator telehealth questionnaire study): Nested economic evaluation in a pragmatic, cluster randomised controlled trial. British Medical Journal, 346, f1035 .

House of Commons Health Committee. (2014). Managing the care of people with long-term conditions . [Online]. Available at: http://www.publications.parliament.uk/pa/cm201415/cmselect/cmhealth/401/401.pdf . Accessed November 2016.

Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16 (2), 91–112.

Ipsos MORI. (2015). GP patient survey – National summary report . [Online]. Available at: http://gp-survey-production.s3.amazonaws.com/archive/2015/July/July%202015%20National%20Summary%20Report.pdf . Accessed November 2016.

Jha, A. K., DesRoches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris, T. G., Shields, A., Rosenbaum, S., & Blumenthal, D. (2009). Use of electronic health records in US hospitals. New England Journal of Medicine, 360 (16), 1628–1638.

Jimison, H., Gorman, P., Woods, S., Nygren, P., Walker, M., Norris, S., & Hersh, W. (2008). Barriers and drivers of health information technology use for the elderly, chronically III, and underserved . Evidence Reports/Technology Assessments , No. 175.

Keller, M. C., Fredrickson, B. L., Ybarra, O., Côté, S., Johnson, K., Mikels, J., Conway, A., & Wager, T. (2005). A warm heart and a clear head the contingent effects of weather on mood and cognition. Psychological Science, 16 (9), 724–731.

Kemper, A. R., Uren, R. L., & Clark, S. J. (2006). Adoption of electronic health records in primary care pediatric practices. Pediatrics, 118 (1), e20–e24.

Keshavjee, K., Bosomworth, J., Copen, J., Lai, J., Kucukyazici, B., et al. (2006). Best practices in EMR implementation: A systematic review. American Medical Informatics Association Annual Symposium Proceedings, 2006 , 982.

Kessels, R. P. (2003). Patients’ memory for medical information. Journal of the Royal Society of Medicine, 96 (5), 219–222.

Ketikidis, P., Dimitrovski, T., Lazuras, L., & Bath, P. A. (2012). Acceptance of health information technology in health professionals: An application of the revised technology acceptance model. Health Informatics Journal, 18 (2), 124–134.

Kings Fund. (2008). Technology in the NHS. Transforming the patient’s experience of care . [Online]. Available at: https://www.kingsfund.org.uk/sites/files/kf/Technology-in-the-NHS-Transforming-patients-experience-of-care-Liddell-Adshead-and-Burgess-Kings-Fund-October-2008_0.pdf . Accessed November 2016.

Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., Riley, W. T., Shar, A., Spring, B., Spruijt-Metz, D., & Hedeker, D. (2013). Mobile health technology evaluation: The mHealth evidence workshop. American Journal of Preventive Medicine, 45 (2), 228–236.

Leonard-Barton, D., & Deschamps, I. (1988). Managerial influence in the implementation of new technology. Management Science, 34 (10), 1252–1265.

Lorenzi, N. M., & Riley, R. T. (2000). Managing change. Journal of the American Medical Informatics Association, 7 (2), 116–124.

Ludwick, D. A., & Doucette, J. (2009). Adopting electronic medical records in primary care: Lessons learned from health information systems implementation experience in seven countries. International Journal of Medical Informatics, 78 (1), 22–31.

Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42 (6), 505.

Mair, F. S., May, C., Finch, T., Murray, E., Anderson, G., Sullivan, F., O’Donnell, C., Wallace, P., & Epstein, O. (2007). Understanding the implementation and integration of e-health services. Journal of Telemedicine and Telecare, 13 (suppl 1), 36–37.

Martin, T. (2012). Assessing mHealth: Opportunities and barriers to patient engagement. Journal of Health Care for the Poor and Underserved, 23 (3), 935–941.

McGinn, C. A., Grenier, S., Duplantie, J., Shaw, N., Sicotte, C., Mathieu, L., Leduc, Y., Légaré, F., & Gagnon, M. P. (2011). Comparison of user groups’ perspectives of barriers and facilitators to implementing electronic health records: A systematic review. BMC Medicine, 9 (1), 1.

Mitzner, T. L., Boron, J. B., Fausset, C. B., Adams, A. E., Charness, N., Czaja, S. J., Dijkstra, K., Fisk, A. D., Rogers, W. A., & Sharit, J. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26 (6), 1710–1721.

NHS England. (2013). The NHS belongs to the people: A call to action . [Online]. Available at: https://www.england.nhs.uk/wp-content/uploads/2013/07/nhs_belongs.pdf . Accessed November 2016.

NHS England. (2016). A and E attendances and emergency admissions . [Online]. Available at: https://www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2016/06/Monthly-AE-Report-August-16.pdf . Accessed November 2016.

NHS England Analytical Services. (2016). Referral to treatment (RTT) waiting times statistics for consultant-led elective care . [Online]. Available at: https://www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2013/04/RTT-Annual-Report-2015-16-v3_final.pdf . Accessed November 2016.

NICE Shared Learning Database. (2013). Lincus Trial: YMCA Liverpool pilot study for individuals with multiple and complex needs . [Online]. Available at: https://www.nice.org.uk/sharedlearning/lincus-trial-ymca-liverpool-pilot-study-for-individuals-with-multiple-and-complex-needs . Accessed November 2016.

Operational Information for Commissioning. (2016). Delayed Transfers of Care Statistics for England 2015/16 . [Online]. Available at: https://www.england.nhs.uk/statistics/wp-content/uploads/sites/2/2013/04/2015-16-Delayed-Transfers-of-Care-Annual-Report-1.pdf . Accessed November 2016.

Or, C. K., Karsh, B. T., Severtson, D. J., Burke, L. J., Brown, R. L., & Brennan, P. F. (2011). Factors affecting home care patients’ acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association, 18 (1), 51–59.

Øvretveit, J., Scott, T., Rundall, T. G., Shortell, S. M., & Brommels, M. (2007). Improving quality through effective implementation of information technology in healthcare. International Journal for Quality in Health Care, 19 (5), 259–266.

Poon, E. G., Blumenthal, D., Jaggi, T., Honour, M. M., Bates, D. W., & Kaushal, R. (2004). Overcoming barriers to adopting and implementing computerized physician order entry systems in US hospitals. Health Affairs, 23 (4), 184–190.

Prior, F., & Dawson, T. (2016). Development of a holistic health economic evaluation tool leveraging patient self-report. IEEE . In Press .

Rogers, A., Kirk, S., Gately, C., May, C. R., & Finch, T. (2011). Established users and the making of telecare work in long term condition management: Implications for health policy. Social Science and Medicine, 72 (7), 1077–1084.

Sanders, C., Rogers, A., Bowen, R., Bower, P., Hirani, S., Cartwright, M., Fitzpatrick, R., Knapp, M., Barlow, J., Hendy, J., & Chrysanthaki, T. (2012). Exploring barriers to participation and adoption of telehealth and telecare within the whole system demonstrator trial: A qualitative study. BMC Health Services Research, 12 (1), 1.

Southon, F. C. G., Sauer, C., & Dampney, C. N. G. K. (1997). Information technology in complex health services. Journal of the American Medical Informatics Association, 4 (2), 112–124.

Steventon, A., Bardsley, M., Billings. J., Dixon. J., Doll, H., Hirani, S., Cartwright, M., Rixon, L., Knapp, M., Henderson, C., Rogers, A., Fitzpatrick, R., Hendy, J., Newman, S; Whole System Demonstrator Evaluation Team. (2012). Effect of telehealth on use of secondary care and mortality: Findings from the Whole System Demonstrator cluster randomised trial. British Medical Journal, 344 , e3874.

Steventon, A., Bardsley, M., Billings, J., Dixon, J., Doll, H., Beynon, M., Hirani, S., Cartwright, M., Rixon, L., Knapp, M., Henderson, C., Rogers, A., Hendy, J., Fitzpatrick, R., & Newman, S. (2013). Effect of telecare on use of health and social care services: Findings from the Whole Systems Demonstrator cluster randomised trial. Age and Ageing, 42 (4), 501–508.

Sudore, R. L., Landefeld, C. S., Pérez-Stable, E. J., Bibbins-Domingo, K., Williams, B. A., & Schillinger, D. (2009). Unraveling the relationship between literacy, language proficiency, and patient–physician communication. Patient Education and Counseling, 75 (3), 398–402.

The Kings Fund. (2016). A digital NHS? An introduction to the digital agenda and plans for implementation . [Online]. Available at: https://www.kingsfund.org.uk/sites/files/kf/field/field_publication_file/A_digital_NHS_Kings_Fund_Sep_2016.pdf . Accessed November 2016.

Thiru, K., Hassey, A., & Sullivan, F. (2003). Systematic review of scope and quality of electronic patient record data in primary care. British Medical Journal, 326 (7398), 1070.

UK Department of Health. (2011). Whole system demonstrator programme . [Online]. Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/215264/dh_131689.pdf . Accessed November 2016.

UK Department of Health. (2012). Long term conditions compendium of information: 3rd edition . [Online]. Available at: https://www.gov.uk/government/publications/long-term-conditions-compendium-of-information-third-edition . Accessed November 2016.

UK Department of Health. (2016). Making IT work: Harnessing the power of health information technology to improve care in England . [Online]. Available at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/550866/Wachter_Review_Accessible.pdf . Accessed November 2016.

Ward, R., Stevens, C., Brentnall, P., & Briddon, J. (2008). The attitudes of health care staff to information technology: A comprehensive review of the research literature. Health Information and Libraries Journal, 25 (2), 81–97.

Whittaker, R. (2012). Issues in mHealth: Findings from key informant interviews. Journal of Medical Internet Research, 14 (5), e129.

World Health Organization. (2013). Global action plan for the prevention and control of NCDs 2013–2020 . [Online]. Available at: http://www.who.int/nmh/publications/ncd-action-plan/en/ . Accessed November 2016.

York Health Economics Consortium. (2009). Organisational and behavioural barriers to medical technology adoption (pp. 1–144). Coventry: York Health Economics Consortium.

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Blanchard, A., Prior, F., Gilbert, L., Dawson, T. (2018). Technology Implementation Case Studies: Lincus Software as a Service. In: Dastbaz, M., Arabnia, H., Akhgar, B. (eds) Technology for Smart Futures. Springer, Cham. https://doi.org/10.1007/978-3-319-60137-3_7

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12 Successful CRM Implementation Case Studies to Learn From

CRM implementation can seem like a monumental task to complete. From knowing which CRM to choose, to understanding how to fit it in with the rest of your sales stack , there’s a lot involved from pricing to convincing decision-makers to making sure it works well from the start.

If you’re looking for CRM implementation case studies to give you ideas and confidence to get started, then look no further.

12 CRM Case Studies

Want to get this done right (the first time)? Learn from the CRM case studies of companies that implemented a new CRM successfully to improve the customer experience, drive customer engagement , and increase revenue.

1. How Customer.io Uses Automated Handoffs to Enable Smarter Sales

Company: Customer.io

Customer.io is an established martech provider that needed a CRM to work better with both an inbound and outbound sales process. Plus, they needed it to fit well with their current tool stack and give them automated workflows.

This case study interview with Alex Patton, Director of Marketing and operations at Customer.io, digs deeper into the technical setup the company uses with its CRM platform and how that process maximizes the team’s time and productivity.

2. 6 Tips for Assessing Your CRM + Optimizing Workflow—from a Revenue Coach

Company/Organization: High Kick Sales

Kyle Stremme’s consulting firm, High Kick Sales, enables sales teams to create an optimized process and tech stack. This case study explores the insights Kyle gained from helping B2B and B2C companies develop their CRM systems and processes, plus details on how he helps managers analyze their current CRM and decide on a better system.

3. Implementing Customer Relationship Management (CRM) in Hotel Industry from an Organizational Culture Perspective

Company: Anonymous UK hotel chain

This study, done by the International Journal of Contemporary Hospitality Management, examined a hotel chain in the UK as it implemented a new CRM, noting what worked and what didn't about its implementation process.

They administered a questionnaire to 346 hotel chain managers and found that organizational culture readiness was one of the most determining factors in the success of a CRM implementation.

4. Choosing and Implementing a CRM for Small Business

Company: Bean Ninjas

Bean Ninjas is an accounting firm for eCommerce businesses. Their tech stack was dissonant and unconnected, and their ‘CRM’ (actually a project management tool) didn’t even have email built-in. The lack of functionality was impacting their business.

Their self-written case study goes through choosing the right CRM, implementing the system into a more optimized sales workflow , technical integrations, and the end results.

5. How AAXIS Digital is Saving an Estimated $250,000 by Switching from Salesforce to the HubSpot CRM Platform

Company: AAXIS

This CRM implementation case study focuses on how an enterprise company migrated from one extensive CRM to another, saving them lots of money on a system they weren’t using to the full.

The case study explores how they chose their new CRM and their accomplishments with it, including increasing marketing automation and better aligning sales and marketing. For specific Salesforce resources, check out our list of CRM implementation resources .

6. Replacing HubSpot with Close: Scaling Trufan in a CRM Reps Love

Company: Trufan

Trufan (now Surf for Brands) is a fast-growing SaaS startup with a tech-savvy target market. So, they needed a CRM that could move quickly alongside their team, helping them build well-constructed automation that could scale as they grew.

This CRM implementation case study shows how a wrong decision slowed their progress and how a new solution helped them scale faster.

7. A Successful CRM Implementation Project in a Service Company

Company: Anonymous service company from Slovenia

This academic case study by Piskar Franka and Armand Faganel examines the process a service company in Slovenia followed alongside CRM consultants to implement a new solution.

They concluded that a proper CRM implementation can improve customer relationships , achieve greater information sharing between employees, and lead to better strategic decisions. This is mostly interesting for historical purposes, as it gives some insight into the complexity involved in implementing a CRM into a larger company in 2007.

8. Hownd Cut CRM Costs by 80 percent in 2 Weeks—While Saving SMBs During COVID

Company: Hownd

Hownd works with brick-and-mortar businesses to get more foot traffic, and their mission since the start of the pandemic is to help SMBs get back on their feet and recover. They needed a CRM that would help them cut their costs to help others and help them move quickly to fill the needs of their customers.

This case study/COVID success story shows how Hownd found the right CRM for their business, cut costs, streamlined their process, and continues to help SMBs survive through hard times.

9. The Ultimate Team Effort: How the Close Sales Team Joins Forces to Build More Solid Deals

Company: Close

This unique case study is the story of our CRM software company and how we’ve implemented our CRM tool into our sales stack. It digs into the nitty-gritty of technical setups and integrations, API, and how it all works together for a smooth, profitable process.

10. Switching to HubSpot Adds up for Casio

Company: Casio

This enterprise CRM implementation case study shows how consumer electronics company Casio switched from a custom-built CRM to one that was more inclusive for their marketing and sales teams. It shows how they updated their inbound marketing process and increased their new customer sales by 26 percent.

11. The Unique Sales Process ResQ Club Uses to Power It's Mission to Zero Food Waste

Company: ResQ Club

ResQ Club, a Finnish company on a mission to make zero food waste a reality, needed a CRM solution that would help them track customers and partners and scale quickly.

This case study shows how they used Close to build their own custom processes with Custom Fields , email sequences that are personalized to different European cities, and Smart Views that keep sales reps focused on the right deals.

12. Strategic Issues in Customer Relationship Management (CRM) Implementation

Company: Anonymous UK manufacturing company

This paper from 2003 by Christopher Bull from the Department of Business Information Technology at Manchester Metropolitan University Business School discusses the effects of a strategic customer relationship management process and how it affected this manufacturing company.

The results of this study highlighted that CRM implementations frequently failed. It also referenced a study of 202 CRM projects, which concluded that only 30.7 percent of organizations said the CRM implementation improved how they sell to and service customers.

Testimonials that Highlight the Benefits of CRM Implementation

What kind of benefits should you expect once you’ve implemented a new CRM ? It depends on your company and current pain points. If you are considering switching to a new CRM or implementing one for the first time, here’s what real CRM users say:

1. Nick Parker, Founder at FTOCloud

“With Close, we're able to keep track of hundreds of deals and clients over multiple months while simultaneously unifying our team's communication.”

2. Tim Griffin, Founder & CEO at Cloosiv

“ We didn’t start getting traction until we started using Close. I don’t know if the company would still be here if we hadn’t implemented it.”

Read the whole story here.

3. Maryl Johnston, CEO at Bean Ninjas

“The real benefit of Close is less about sales admin time and more about closing more deals. Because Close makes it very easy to stay in touch with customers and allows Sales to manage their pipeline without needing a sales admin, we can now go into Close and see all the leads in a broad view.”

‎4. Aimee Creighton, Sales Administrator at Bean Ninjas

“ The biggest win for me is the cut-down in labor time of setting up leads in our task management system (not designed for lead management) and ensuring all fields are filled out. It significantly reduced the time-intensive manual process of documenting leads. I feel like Close has completely cut that down, and everything is right there from the dashboard. I think it’s been worth the investment.”

5. Monika Tudja, Business Development Manager at Now Technologies

“ I can't imagine my work-life without Close - I've been using it at my previous company and I 'demanded' implementing it on my first day at the current one. I'm useless without Close. Seriously thinking about getting an account for my personal life.”

6. Sara Archer, Director of Sales and Marketing

“ Since we've started using Close, we've QUADRUPLED our average revenue per user.”

Read how they did it here.

7. Sarah Haselkorn, Head of Sales at MakeSpace

“ You guys [at Close] have been a HUGE part of our growth so far, and with your support I have so much confidence that our sales team is set up to scale.”

8. Duncan Burns, VeggiDome

“I am able to stay on top of my outreach, correspondence, and follow-up seamlessly AND relax enough to do a better job, knowing that I'm not missing a beat!”

9. Michael Grady, Lazarus

“ This is a CRM that is all about focus with no bloat which is exactly what inside sales needs.”

10. Aubrey Lim, ThreeTrees

“My first time using a CRM. 8 months in and it's frictionless to use. My favorite features: being able to pull up colleagues' emails to a particular lead, bulk-uploading contacts, email templates.”

‎11. Timothy Corey, Director of Sales at Commonwealth Joe

“Close allows us to see where we should spend our time and effort. We can look at our sales for the same quarter last year and know what worked well and what didn’t -- this allows me to know where to put my energy, on what companies, and in what markets.”

Ready to Write Your Own CRM Implementation Success Story?

The right CRM helps you easily access customer information, track contacts, qualify leads, improve conversion rates, and more. If you're ready to implement a CRM, we can help.

For a successful CRM implementation , you need a clear plan to follow. That’s why we’ve given you the right resources to make a better decision. Get our CRM implementation guide here:

ACCESS OUR CRM IMPLEMENTATION GUIDE →

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Introducing technological change into an organization presents a different set of challenges to management than does the work of competent project administration. Frequently, however, the managers responsible for shepherding a technical innovation into routine use are much better equipped by education and experience to guide that innovation’s development than to manage its implementation.

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15 Real-Life Case Study Examples & Best Practices

Written by: Oghale Olori

Case studies are more than just success stories.

They are powerful tools that demonstrate the practical value of your product or service. Case studies help attract attention to your products, build trust with potential customers and ultimately drive sales.

It’s no wonder that 73% of successful content marketers utilize case studies as part of their content strategy. Plus, buyers spend 54% of their time reviewing case studies before they make a buying decision.

To ensure you’re making the most of your case studies, we’ve put together 15 real-life case study examples to inspire you. These examples span a variety of industries and formats. We’ve also included best practices, design tips and templates to inspire you.

Let’s dive in!

Table of Contents

What is a case study, 15 real-life case study examples, sales case study examples, saas case study examples, product case study examples, marketing case study examples, business case study examples, case study faqs.

• A case study is a compelling narrative that showcases how your product or service has positively impacted a real business or individual. 
• Case studies delve into your customer's challenges, how your solution addressed them and the quantifiable results they achieved.
• Your case study should have an attention-grabbing headline, great visuals and a relevant call to action. Other key elements include an introduction, problems and result section.
• Visme provides easy-to-use tools, professionally designed templates and features for creating attractive and engaging case studies.

A case study is a real-life scenario where your company helped a person or business solve their unique challenges. It provides a detailed analysis of the positive outcomes achieved as a result of implementing your solution.

Case studies are an effective way to showcase the value of your product or service to potential customers without overt selling. By sharing how your company transformed a business, you can attract customers seeking similar solutions and results.

Case studies are not only about your company's capabilities; they are primarily about the benefits customers and clients have experienced from using your product.

Every great case study is made up of key elements. They are;

• Attention-grabbing headline: Write a compelling headline that grabs attention and tells your reader what the case study is about. For example, "How a CRM System Helped a B2B Company Increase Revenue by 225%.
• Introduction/Executive Summary: Include a brief overview of your case study, including your customer’s problem, the solution they implemented and the results they achieved.
• Problem/Challenge: Case studies with solutions offer a powerful way to connect with potential customers. In this section, explain how your product or service specifically addressed your customer's challenges.
• Solution: Explain how your product or service specifically addressed your customer's challenges.
• Results/Achievements : Give a detailed account of the positive impact of your product. Quantify the benefits achieved using metrics such as increased sales, improved efficiency, reduced costs or enhanced customer satisfaction.
• Graphics/Visuals: Include professional designs, high-quality photos and videos to make your case study more engaging and visually appealing.
• Quotes/Testimonials: Incorporate written or video quotes from your clients to boost your credibility.
• Relevant CTA: Insert a call to action (CTA) that encourages the reader to take action. For example, visiting your website or contacting you for more information. Your CTA can be a link to a landing page, a contact form or your social media handle and should be related to the product or service you highlighted in your case study.

Now that you understand what a case study is, let’s look at real-life case study examples. Among these, you'll find some simple case study examples that break down complex ideas into easily understandable solutions.

In this section, we’ll explore SaaS, marketing, sales, product and business case study examples with solutions. Take note of how these companies structured their case studies and included the key elements.

We’ve also included professionally designed case study templates to inspire you.

1. Georgia Tech Athletics Increase Season Ticket Sales by 80%

Georgia Tech Athletics, with its 8,000 football season ticket holders, sought for a way to increase efficiency and customer engagement.

Their initial sales process involved making multiple outbound phone calls per day with no real targeting or guidelines. Georgia Tech believed that targeting communications will enable them to reach more people in real time.

Salesloft improved Georgia Tech’s sales process with an inbound structure. This enabled sales reps to connect with their customers on a more targeted level. The use of dynamic fields and filters when importing lists ensured prospects received the right information, while communication with existing fans became faster with automation.

As a result, Georgia Tech Athletics recorded an 80% increase in season ticket sales as relationships with season ticket holders significantly improved. Employee engagement increased as employees became more energized to connect and communicate with fans.

Why Does This Case Study Work?

In this case study example , Salesloft utilized the key elements of a good case study. Their introduction gave an overview of their customers' challenges and the results they enjoyed after using them. After which they categorized the case study into three main sections: challenge, solution and result.

Salesloft utilized a case study video to increase engagement and invoke human connection.

Incorporating videos in your case study has a lot of benefits. Wyzol’s 2023 state of video marketing report showed a direct correlation between videos and an 87% increase in sales.

The beautiful thing is that creating videos for your case study doesn’t have to be daunting.

With an easy-to-use platform like Visme, you can create top-notch testimonial videos that will connect with your audience. Within the Visme editor, you can access over 1 million stock photos , video templates, animated graphics and more. These tools and resources will significantly improve the design and engagement of your case study.

Simplify content creation and brand management for your team

• Collaborate on designs , mockups and wireframes with your non-design colleagues
• Lock down your branding to maintain brand consistency throughout your designs
• Why start from scratch? Save time with 1000s of professional branded templates

Sign up. It’s free.

2. WeightWatchers Completely Revamped their Enterprise Sales Process with HubSpot

WeightWatchers, a 60-year-old wellness company, sought a CRM solution that increased the efficiency of their sales process. With their previous system, Weightwatchers had limited automation. They would copy-paste message templates from word documents or recreate one email for a batch of customers.

This required a huge effort from sales reps, account managers and leadership, as they were unable to track leads or pull customized reports for planning and growth.

WeightWatchers transformed their B2B sales strategy by leveraging HubSpot's robust marketing and sales workflows. They utilized HubSpot’s deal pipeline and automation features to streamline lead qualification. And the customized dashboard gave leadership valuable insights.

As a result, WeightWatchers generated seven figures in annual contract value and boosted recurring revenue. Hubspot’s impact resulted in 100% adoption across all sales, marketing, client success and operations teams.

Hubspot structured its case study into separate sections, demonstrating the specific benefits of their products to various aspects of the customer's business. Additionally, they integrated direct customer quotes in each section to boost credibility, resulting in a more compelling case study.

Getting insight from your customer about their challenges is one thing. But writing about their process and achievements in a concise and relatable way is another. If you find yourself constantly experiencing writer’s block, Visme’s AI writer is perfect for you.

Visme created this AI text generator tool to take your ideas and transform them into a great draft. So whether you need help writing your first draft or editing your final case study, Visme is ready for you.

3. Immi’s Ram Fam Helps to Drive Over $200k in Sales

Immi embarked on a mission to recreate healthier ramen recipes that were nutritious and delicious. After 2 years of tireless trials, Immi finally found the perfect ramen recipe. However, they envisioned a community of passionate ramen enthusiasts to fuel their business growth.

This vision propelled them to partner with Shopify Collabs. Shopify Collabs successfully cultivated and managed Immi’s Ramen community of ambassadors and creators.

As a result of their partnership, Immi’s community grew to more than 400 dedicated members, generating over $200,000 in total affiliate sales.

The power of data-driven headlines cannot be overemphasized. Chili Piper strategically incorporates quantifiable results in their headlines. This instantly sparks curiosity and interest in readers.

While not every customer success story may boast headline-grabbing figures, quantifying achievements in percentages is still effective. For example, you can highlight a 50% revenue increase with the implementation of your product.

Take a look at the beautiful case study template below. Just like in the example above, the figures in the headline instantly grab attention and entice your reader to click through.

Having a case study document is a key factor in boosting engagement. This makes it easy to promote your case study in multiple ways. With Visme, you can easily publish, download and share your case study with your customers in a variety of formats, including PDF, PPTX, JPG and more!

4. How WOW! is Saving Nearly 79% in Time and Cost With Visme

This case study discusses how Visme helped WOW! save time and money by providing user-friendly tools to create interactive and quality training materials for their employees. Find out what your team can do with Visme. Request a Demo

WOW!'s learning and development team creates high-quality training materials for new and existing employees. Previous tools and platforms they used had plain templates, little to no interactivity features, and limited flexibility—that is, until they discovered Visme.

Now, the learning and development team at WOW! use Visme to create engaging infographics, training videos, slide decks and other training materials.

This has directly reduced the company's turnover rate, saving them money spent on recruiting and training new employees. It has also saved them a significant amount of time, which they can now allocate to other important tasks.

Visme's customer testimonials spark an emotional connection with the reader, leaving a profound impact. Upon reading this case study, prospective customers will be blown away by the remarkable efficiency achieved by Visme's clients after switching from PowerPoint.

Visme’s interactivity feature was a game changer for WOW! and one of the primary reasons they chose Visme.

“Previously we were using PowerPoint, which is fine, but the interactivity you can get with Visme is so much more robust that we’ve all steered away from PowerPoint.” - Kendra, L&D team, Wow!

Visme’s interactive feature allowed them to animate their infographics, include clickable links on their PowerPoint designs and even embed polls and quizzes their employees could interact with.

By embedding the slide decks, infographics and other training materials WOW! created with Visme, potential customers get a taste of what they can create with the tool. This is much more effective than describing the features of Visme because it allows potential customers to see the tool in action.

To top it all off, this case study utilized relevant data and figures. For example, one part of the case study said, “In Visme, where Kendra’s team has access to hundreds of templates, a brand kit, and millions of design assets at their disposal, their team can create presentations in 80% less time.”

Who wouldn't want that?

Including relevant figures and graphics in your case study is a sure way to convince your potential customers why you’re a great fit for their brand. The case study template below is a great example of integrating relevant figures and data.

This colorful template begins with a captivating headline. But that is not the best part; this template extensively showcases the results their customer had using relevant figures.

The arrangement of the results makes it fun and attractive. Instead of just putting figures in a plain table, you can find interesting shapes in your Visme editor to take your case study to the next level.

5. Lyte Reduces Customer Churn To Just 3% With Hubspot CRM

While Lyte was redefining the ticketing industry, it had no definite CRM system . Lyte utilized 12–15 different SaaS solutions across various departments, which led to a lack of alignment between teams, duplication of work and overlapping tasks.

Customer data was spread across these platforms, making it difficult to effectively track their customer journey. As a result, their churn rate increased along with customer dissatisfaction.

Through Fuelius , Lyte founded and implemented Hubspot CRM. Lyte's productivity skyrocketed after incorporating Hubspot's all-in-one CRM tool. With improved efficiency, better teamwork and stronger client relationships, sales figures soared.

The case study title page and executive summary act as compelling entry points for both existing and potential customers. This overview provides a clear understanding of the case study and also strategically incorporates key details like the client's industry, location and relevant background information.

Having a good summary of your case study can prompt your readers to engage further. You can achieve this with a simple but effective case study one-pager that highlights your customer’s problems, process and achievements, just like this case study did in the beginning.

Moreover, you can easily distribute your case study one-pager and use it as a lead magnet to draw prospective customers to your company.

Take a look at this case study one-pager template below.

This template includes key aspects of your case study, such as the introduction, key findings, conclusion and more, without overcrowding the page. The use of multiple shades of blue gives it a clean and dynamic layout.

Our favorite part of this template is where the age group is visualized.

With Visme’s data visualization tool , you can present your data in tables, graphs, progress bars, maps and so much more. All you need to do is choose your preferred data visualization widget, input or import your data and click enter!

6. How Workato Converts 75% of Their Qualified Leads

Workato wanted to improve their inbound leads and increase their conversion rate, which ranged from 40-55%.

At first, Workato searched for a simple scheduling tool. They soon discovered that they needed a tool that provided advanced routing capabilities based on zip code and other criteria. Luckily, they found and implemented Chili Piper.

As a result of implementing Chili Piper, Workato achieved a remarkable 75–80% conversion rate and improved show rates. This led to a substantial revenue boost, with a 10-15% increase in revenue attributed to Chili Piper's impact on lead conversion.

This case study example utilizes the power of video testimonials to drive the impact of their product.

Chili Piper incorporates screenshots and clips of their tool in use. This is a great strategy because it helps your viewers become familiar with how your product works, making onboarding new customers much easier.

In this case study example, we see the importance of efficient Workflow Management Systems (WMS). Without a WMS, you manually assign tasks to your team members and engage in multiple emails for regular updates on progress.

However, when crafting and designing your case study, you should prioritize having a good WMS.

Visme has an outstanding Workflow Management System feature that keeps you on top of all your projects and designs. This feature makes it much easier to assign roles, ensure accuracy across documents, and track progress and deadlines.

Visme’s WMS feature allows you to limit access to your entire document by assigning specific slides or pages to individual members of your team. At the end of the day, your team members are not overwhelmed or distracted by the whole document but can focus on their tasks.

7. Rush Order Helps Vogmask Scale-Up During a Pandemic

Vomask's reliance on third-party fulfillment companies became a challenge as demand for their masks grew. Seeking a reliable fulfillment partner, they found Rush Order and entrusted them with their entire inventory.

Vomask's partnership with Rush Order proved to be a lifesaver during the COVID-19 pandemic. Rush Order's agility, efficiency and commitment to customer satisfaction helped Vogmask navigate the unprecedented demand and maintain its reputation for quality and service.

Rush Order’s comprehensive support enabled Vogmask to scale up its order processing by a staggering 900% while maintaining a remarkable customer satisfaction rate of 92%.

Rush Order chose one event where their impact mattered the most to their customer and shared that story.

While pandemics don't happen every day, you can look through your customer’s journey and highlight a specific time or scenario where your product or service saved their business.

The story of Vogmask and Rush Order is compelling, but it simply is not enough. The case study format and design attract readers' attention and make them want to know more. Rush Order uses consistent colors throughout the case study, starting with the logo, bold square blocks, pictures, and even headers.

Take a look at this product case study template below.

Just like our example, this case study template utilizes bold colors and large squares to attract and maintain the reader’s attention. It provides enough room for you to write about your customers' backgrounds/introductions, challenges, goals and results.

The right combination of shapes and colors adds a level of professionalism to this case study template.

8. AMR Hair & Beauty leverages B2B functionality to boost sales by 200%

With limits on website customization, slow page loading and multiple website crashes during peak events, it wasn't long before AMR Hair & Beauty began looking for a new e-commerce solution.

Their existing platform lacked effective search and filtering options, a seamless checkout process and the data analytics capabilities needed for informed decision-making. This led to a significant number of abandoned carts.

Upon switching to Shopify Plus, AMR immediately saw improvements in page loading speed and average session duration. They added better search and filtering options for their wholesale customers and customized their checkout process.

Due to this, AMR witnessed a 200% increase in sales and a 77% rise in B2B average order value. AMR Hair & Beauty is now poised for further expansion and growth.

This case study example showcases the power of a concise and impactful narrative.

To make their case analysis more effective, Shopify focused on the most relevant aspects of the customer's journey. While there may have been other challenges the customer faced, they only included those that directly related to their solutions.

Take a look at this case study template below. It is perfect if you want to create a concise but effective case study. Without including unnecessary details, you can outline the challenges, solutions and results your customers experienced from using your product.

Don’t forget to include a strong CTA within your case study. By incorporating a link, sidebar pop-up or an exit pop-up into your case study, you can prompt your readers and prospective clients to connect with you.

9. How a Marketing Agency Uses Visme to Create Engaging Content With Infographics

SmartBox Dental , a marketing agency specializing in dental practices, sought ways to make dental advice more interesting and easier to read. However, they lacked the design skills to do so effectively.

Visme's wide range of templates and features made it easy for the team to create high-quality content quickly and efficiently. SmartBox Dental enjoyed creating infographics in as little as 10-15 minutes, compared to one hour before Visme was implemented.

By leveraging Visme, SmartBox Dental successfully transformed dental content into a more enjoyable and informative experience for their clients' patients. Therefore enhancing its reputation as a marketing partner that goes the extra mile to deliver value to its clients.

Visme creatively incorporates testimonials In this case study example.

By showcasing infographics and designs created by their clients, they leverage the power of social proof in a visually compelling way. This way, potential customers gain immediate insight into the creative possibilities Visme offers as a design tool.

This example effectively showcases a product's versatility and impact, and we can learn a lot about writing a case study from it. Instead of focusing on one tool or feature per customer, Visme took a more comprehensive approach.

Within each section of their case study, Visme explained how a particular tool or feature played a key role in solving the customer's challenges.

For example, this case study highlighted Visme’s collaboration tool . With Visme’s tool, the SmartBox Dental content team fostered teamwork, accountability and effective supervision.

Visme also achieved a versatile case study by including relevant quotes to showcase each tool or feature. Take a look at some examples;

Visme’s collaboration tool: “We really like the collaboration tool. Being able to see what a co-worker is working on and borrow their ideas or collaborate on a project to make sure we get the best end result really helps us out.”

Visme’s library of stock photos and animated characters: “I really love the images and the look those give to an infographic. I also really like the animated little guys and the animated pictures. That’s added a lot of fun to our designs.”

Visme’s interactivity feature: “You can add URLs and phone number links directly into the infographic so they can just click and call or go to another page on the website and I really like adding those hyperlinks in.”

You can ask your customers to talk about the different products or features that helped them achieve their business success and draw quotes from each one.

10. Jasper Grows Blog Organic Sessions 810% and Blog-Attributed User Signups 400X

Jasper, an AI writing tool, lacked a scalable content strategy to drive organic traffic and user growth. They needed help creating content that converted visitors into users. Especially when a looming domain migration threatened organic traffic.

To address these challenges, Jasper partnered with Omniscient Digital. Their goal was to turn their content into a growth channel and drive organic growth. Omniscient Digital developed a full content strategy for Jasper AI, which included a content audit, competitive analysis, and keyword discovery.

Through their collaboration, Jasper’s organic blog sessions increased by 810%, despite the domain migration. They also witnessed a 400X increase in blog-attributed signups. And more importantly, the content program contributed to over $4 million in annual recurring revenue.

The combination of storytelling and video testimonials within the case study example makes this a real winner. But there’s a twist to it. Omniscient segmented the video testimonials and placed them in different sections of the case study.

Video marketing , especially in case studies, works wonders. Research shows us that 42% of people prefer video testimonials because they show real customers with real success stories. So if you haven't thought of it before, incorporate video testimonials into your case study.

Take a look at this stunning video testimonial template. With its simple design, you can input the picture, name and quote of your customer within your case study in a fun and engaging way.

Try it yourself! Customize this template with your customer’s testimonial and add it to your case study!

11. How Meliá Became One of the Most Influential Hotel Chains on Social Media

Meliá Hotels needed help managing their growing social media customer service needs. Despite having over 500 social accounts, they lacked a unified response protocol and detailed reporting. This largely hindered efficiency and brand consistency.

Meliá partnered with Hootsuite to build an in-house social customer care team. Implementing Hootsuite's tools enabled Meliá to decrease response times from 24 hours to 12.4 hours while also leveraging smart automation.

In addition to that, Meliá resolved over 133,000 conversations, booking 330 inquiries per week through Hootsuite Inbox. They significantly improved brand consistency, response time and customer satisfaction.

The need for a good case study design cannot be over-emphasized.

As soon as anyone lands on this case study example, they are mesmerized by a beautiful case study design. This alone raises the interest of readers and keeps them engaged till the end.

If you’re currently saying to yourself, “ I can write great case studies, but I don’t have the time or skill to turn it into a beautiful document.” Say no more.

Visme’s amazing AI document generator can take your text and transform it into a stunning and professional document in minutes! Not only do you save time, but you also get inspired by the design.

With Visme’s document generator, you can create PDFs, case study presentations , infographics and more!

Take a look at this case study template below. Just like our case study example, it captures readers' attention with its beautiful design. Its dynamic blend of colors and fonts helps to segment each element of the case study beautifully.

12. Tea’s Me Cafe: Tamika Catchings is Brewing Glory

Tamika's journey began when she purchased Tea's Me Cafe in 2017, saving it from closure. She recognized the potential of the cafe as a community hub and hosted regular events centered on social issues and youth empowerment.

One of Tamika’s business goals was to automate her business. She sought to streamline business processes across various aspects of her business. One of the ways she achieves this goal is through Constant Contact.

Constant Contact became an integral part of Tamika's marketing strategy. They provided an automated and centralized platform for managing email newsletters, event registrations, social media scheduling and more.

This allowed Tamika and her team to collaborate efficiently and focus on engaging with their audience. They effectively utilized features like WooCommerce integration, text-to-join and the survey builder to grow their email list, segment their audience and gather valuable feedback.

The case study example utilizes the power of storytelling to form a connection with readers. Constant Contact takes a humble approach in this case study. They spotlight their customers' efforts as the reason for their achievements and growth, establishing trust and credibility.

This case study is also visually appealing, filled with high-quality photos of their customer. While this is a great way to foster originality, it can prove challenging if your customer sends you blurry or low-quality photos.

If you find yourself in that dilemma, you can use Visme’s AI image edit tool to touch up your photos. With Visme’s AI tool, you can remove unwanted backgrounds, erase unwanted objects, unblur low-quality pictures and upscale any photo without losing the quality.

Constant Contact offers its readers various formats to engage with their case study. Including an audio podcast and PDF.

In its PDF version, Constant Contact utilized its brand colors to create a stunning case study design.  With this, they increase brand awareness and, in turn, brand recognition with anyone who comes across their case study.

With Visme’s brand wizard tool , you can seamlessly incorporate your brand assets into any design or document you create. By inputting your URL, Visme’s AI integration will take note of your brand colors, brand fonts and more and create branded templates for you automatically.

You don't need to worry about spending hours customizing templates to fit your brand anymore. You can focus on writing amazing case studies that promote your company.

13. How Breakwater Kitchens Achieved a 7% Growth in Sales With Thryv

Breakwater Kitchens struggled with managing their business operations efficiently. They spent a lot of time on manual tasks, such as scheduling appointments and managing client communication. This made it difficult for them to grow their business and provide the best possible service to their customers.

David, the owner, discovered Thryv. With Thryv, Breakwater Kitchens was able to automate many of their manual tasks. Additionally, Thryv integrated social media management. This enabled Breakwater Kitchens to deliver a consistent brand message, captivate its audience and foster online growth.

As a result, Breakwater Kitchens achieved increased efficiency, reduced missed appointments and a 7% growth in sales.

This case study example uses a concise format and strong verbs, which make it easy for readers to absorb the information.

At the top of the case study, Thryv immediately builds trust by presenting their customer's complete profile, including their name, company details and website. This allows potential customers to verify the case study's legitimacy, making them more likely to believe in Thryv's services.

However, manually copying and pasting customer information across multiple pages of your case study can be time-consuming.

To save time and effort, you can utilize Visme's dynamic field feature . Dynamic fields automatically insert reusable information into your designs.  So you don’t have to type it out multiple times.

14. Zoom’s Creative Team Saves Over 4,000 Hours With Brandfolder

Zoom experienced rapid growth with the advent of remote work and the rise of the COVID-19 pandemic. Such growth called for agility and resilience to scale through.

At the time, Zoom’s assets were disorganized which made retrieving brand information a burden. Zoom’s creative manager spent no less than 10 hours per week finding and retrieving brand assets for internal teams.

Zoom needed a more sustainable approach to organizing and retrieving brand information and came across Brandfolder. Brandfolder simplified and accelerated Zoom’s email localization and webpage development. It also enhanced the creation and storage of Zoom virtual backgrounds.

With Brandfolder, Zoom now saves 4,000+ hours every year. The company also centralized its assets in Brandfolder, which allowed 6,800+ employees and 20-30 vendors to quickly access them.

Brandfolder infused its case study with compelling data and backed it up with verifiable sources. This data-driven approach boosts credibility and increases the impact of their story.

Bradfolder's case study goes the extra mile by providing a downloadable PDF version, making it convenient for readers to access the information on their own time. Their dedication to crafting stunning visuals is evident in every aspect of the project.

From the vibrant colors to the seamless navigation, everything has been meticulously designed to leave a lasting impression on the viewer. And with clickable links that make exploring the content a breeze, the user experience is guaranteed to be nothing short of exceptional.

The thing is, your case study presentation won’t always sit on your website. There are instances where you may need to do a case study presentation for clients, partners or potential investors.

Visme has a rich library of templates you can tap into. But if you’re racing against the clock, Visme’s AI presentation maker is your best ally.

15. How Cents of Style Made $1.7M+ in Affiliate Sales with LeadDyno

Cents of Style had a successful affiliate and influencer marketing strategy. However, their existing affiliate marketing platform was not intuitive, customizable or transparent enough to meet the needs of their influencers.

Cents of Styles needed an easy-to-use affiliate marketing platform that gave them more freedom to customize their program and implement a multi-tier commission program.

After exploring their options, Cents of Style decided on LeadDyno.

LeadDyno provided more flexibility, allowing them to customize commission rates and implement their multi-tier commission structure, switching from monthly to weekly payouts.

Also, integrations with PayPal made payments smoother And features like newsletters and leaderboards added to the platform's success by keeping things transparent and engaging.

As a result, Cents of Style witnessed an impressive $1.7 million in revenue from affiliate sales with a substantial increase in web sales by 80%.

LeadDyno strategically placed a compelling CTA in the middle of their case study layout, maximizing its impact. At this point, readers are already invested in the customer's story and may be considering implementing similar strategies.

A well-placed CTA offers them a direct path to learn more and take action.

LeadDyno also utilized the power of quotes to strengthen their case study. They didn't just embed these quotes seamlessly into the text; instead, they emphasized each one with distinct blocks.

Are you looking for an easier and quicker solution to create a case study and other business documents? Try Visme's AI designer ! This powerful tool allows you to generate complete documents, such as case studies, reports, whitepapers and more, just by providing text prompts. Simply explain your requirements to the tool, and it will produce the document for you, complete with text, images, design assets and more.

Still have more questions about case studies? Let's look at some frequently asked questions.

How to Write a Case Study?

• Choose a compelling story: Not all case studies are created equal. Pick one that is relevant to your target audience and demonstrates the specific benefits of your product or service.
• Outline your case study: Create a case study outline and highlight how you will structure your case study to include the introduction, problem, solution and achievements of your customer.
• Choose a case study template: After you outline your case study, choose a case study template . Visme has stunning templates that can inspire your case study design.
• Craft a compelling headline: Include figures or percentages that draw attention to your case study.
• Work on the first draft: Your case study should be easy to read and understand. Use clear and concise language and avoid jargon.
• Include high-quality visual aids: Visuals can help to make your case study more engaging and easier to read. Consider adding high-quality photos, screenshots or videos.
• Include a relevant CTA: Tell prospective customers how to reach you for questions or sign-ups.

What Are the Stages of a Case Study?

The stages of a case study are;

• Planning & Preparation: Highlight your goals for writing the case study. Plan the case study format, length and audience you wish to target.
• Interview the Client: Reach out to the company you want to showcase and ask relevant questions about their journey and achievements.
• Revision & Editing: Review your case study and ask for feedback. Include relevant quotes and CTAs to your case study.
• Publication & Distribution: Publish and share your case study on your website, social media channels and email list!
• Marketing & Repurposing: Turn your case study into a podcast, PDF, case study presentation and more. Share these materials with your sales and marketing team.

What Are the Advantages and Disadvantages of a Case Study?

Advantages of a case study:

• Case studies showcase a specific solution and outcome for specific customer challenges.
• It attracts potential customers with similar challenges.
• It builds trust and credibility with potential customers.
• It provides an in-depth analysis of your company’s problem-solving process.

Disadvantages of a case study:

• Limited applicability. Case studies are tailored to specific cases and may not apply to other businesses.
• It relies heavily on customer cooperation and willingness to share information.
• It stands a risk of becoming outdated as industries and customer needs evolve.

What Are the Types of Case Studies?

There are 7 main types of case studies. They include;

• Illustrative case study.
• Instrumental case study.
• Intrinsic case study.
• Descriptive case study.
• Explanatory case study.
• Exploratory case study.
• Collective case study.

How Long Should a Case Study Be?

The ideal length of your case study is between 500 - 1500 words or 1-3 pages. Certain factors like your target audience, goal or the amount of detail you want to share may influence the length of your case study. This infographic has powerful tips for designing winning case studies

What Is the Difference Between a Case Study and an Example?

Case studies provide a detailed narrative of how your product or service was used to solve a problem. Examples are general illustrations and are not necessarily real-life scenarios.

Case studies are often used for marketing purposes, attracting potential customers and building trust. Examples, on the other hand, are primarily used to simplify or clarify complex concepts.

Where Can I Find Case Study Examples?

You can easily find many case study examples online and in industry publications. Many companies, including Visme, share case studies on their websites to showcase how their products or services have helped clients achieve success. You can also search online libraries and professional organizations for case studies related to your specific industry or field.

If you need professionally-designed, customizable case study templates to create your own, Visme's template library is one of the best places to look. These templates include all the essential sections of a case study and high-quality content to help you create case studies that position your business as an industry leader.

Get More Out Of Your Case Studies With Visme

Case studies are an essential tool for converting potential customers into paying customers. By following the tips in this article, you can create compelling case studies that will help you build trust, establish credibility and drive sales.

Visme can help you create stunning case studies and other relevant marketing materials. With our easy-to-use platform, interactive features and analytics tools , you can increase your content creation game in no time.

There is no limit to what you can achieve with Visme. Connect with Sales to discover how Visme can boost your business goals.

Easily create beautiful case studies and more with Visme

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• Volume 2, Issue 2
• Technology adoption and implementation in organisations: comparative case studies of 12 English NHS Trusts
• Article Text
• Article info
• Citation Tools
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• Yiannis Kyratsis ,
• Raheelah Ahmad ,
• Alison Holmes
• Department of Infectious Diseases, National Centre for Infection Prevention and Management, Faculty of Medicine, Imperial College London, London, UK
• Correspondence to Dr Yiannis Kyratsis; y.kyratsis{at}imperial.ac.uk

Objectives To understand organisational technology adoption (initiation, adoption decision, implementation) by looking at the different types of innovation knowledge used during this process.

Design Qualitative, multisite, comparative case study design.

Setting One primary care and 11 acute care organisations (trusts) across all health regions in England in the context of infection prevention and control.

Participants and data analysis 121 semistructured individual and group interviews with 109 informants, involving clinical and non-clinical staff from all organisational levels and various professional groups. Documentary evidence and field notes were also used. 38 technology adoption processes were analysed using an integrated approach combining inductive and deductive reasoning.

Main findings Those involved in the process variably accessed three types of innovation knowledge: ‘awareness’ (information that an innovation exists), ‘principles’ (information about an innovation's functioning principles) and ‘how-to’ (information required to use an innovation properly at individual and organisational levels). Centralised (national, government-led) and local sources were used to obtain this knowledge. Localised professional networks were preferred sources for all three types of knowledge. Professional backgrounds influenced an asymmetric attention to different types of innovation knowledge. When less attention was given to ‘how-to’ compared with ‘principles’ knowledge at the early stages of the process, this contributed to 12 cases of incomplete implementation or discontinuance after initial adoption.

Conclusions Potential adopters and change agents often overlooked or undervalued ‘how-to’ knowledge. Balancing ‘principles’ and ‘how-to’ knowledge early in the innovation process enhanced successful technology adoption and implementation by considering efficacy as well as strategic, structural and cultural fit with the organisation's context. This learning is critical given the policy emphasis for health organisations to be innovation-ready.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

https://doi.org/10.1136/bmjopen-2012-000872

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Article summary

Article focus.

Despite policy support and the development of a dedicated evidence dissemination infrastructure in the NHS, why is technology adoption and implementation still a challenge?

We need to understand better how the innovation process unfolds in organisations to build on what we know about individual behaviours. In particular, how the use of different types of knowledge about an innovation impacts its adoption and implementation.

Key messages

In our study, centralised dissemination of evidence had minimal to moderate impact on organisational innovation adoption decisions. Practice-based, peer-mediated and local dissemination systems were perceived more relevant.

In contrast to technology adoption by individuals, organisational adoption required a wider multifaceted conceptualisation of ‘how-to’ knowledge in line with the more complex dynamics in organisations. When ‘how-to’ knowledge was undervalued and considered late, important strategic, structural and cultural elements of the trust's context were overlooked. This had negative implications for technology adoption and implementation.

Professional backgrounds of those involved in the process influenced the types of innovation knowledge considered, which had implications for implementation. The involvement of diverse professionals in decision-making improves the chances of successful implementation through a balanced consideration of the strength of scientific evidence and practical application.

Strengths and limitations of this study

The scale of the study, its real time and longitudinal nature provide a rich data set. Our study is theory driven and comprises multisite comparative case studies, which enhance the generalisability of findings beyond the context of the studied trusts.

We explicitly studied cases of non-adoption and discontinuation after initial adoption to provide important learning often missing from innovation diffusion research.

On limitations, we were not able to follow implementation past the end of August 2010 and therefore do not have information on routinised use of the implemented technologies.

Introduction

The recent focus on quality and efficiency in healthcare by policy makers 1 highlights the need to harness new healthcare technologies and innovation to improve quality of patient care and health system productivity. 2 3 The uptake and implementation of new technologies in healthcare has often proved challenging and in some cases very slow. 4–6 In the UK, the significant ‘research to practice’ knowledge gap and the suboptimal implementation of new ideas and technologies into clinical practice have been emphasised in several recent policy documents. 7–9 Policy and academic systematic reviews 6 10 consistently show that there remains a poor understanding of the mechanisms and processes that encourage the adoption of new interventions. Specifically, attention to the processes by which organisational members access and use implementation and clinical evidence during decision-making is required. 9 11 As regards technology adoption in the NHS, a recent systematic review 12 has found that there has been little research in this area.

In the last decade, government-funded agencies have been created to encourage innovation uptake and promote the use of evidence-based innovations in the NHS 1 13 ; such predominately centralised evidence dissemination structures include the NHS Institute for Innovation and Improvement, the National Institute for Health and Clinical Excellence with the launch of the NHS Evidence online portal and the NHS Technology Adoption Centre, which works to speed-up the adoption of proven technologies by NHS organisations. Despite these initiatives, the challenges of adopting novel technologies in the NHS persist.

Our study addresses this research gap and is well grounded in innovation change and diffusion theories. 14–16 Specifically, our study unpacks the innovation processes in organisations—in contrast to individuals—by investigating in detail the interplay between the types and sources of innovation knowledge used. We empirically focus our investigation on infection prevention and control (IPC) as it represents a cross-cutting priority area in healthcare with application to primary and acute care, surgery and medicine alike. While there has been increasing public and policy attention to address healthcare-associated infections (HCAIs) ( box 1 ), the uptake and implementation of new technologies in IPC varies and remains slow. 24 This empirical setting, therefore, offers opportunities to generate transferable lessons.

Healthcare-associated infections (HCAIs) initiatives in the NHS

HCAIs are a worldwide problem causing high mortality and morbidity with significant cost implications for health systems. 17–22 Both developing and more developed countries face the challenge, 18 and there is intense media and public attention on the issue. In the UK, a range of infection prevention and control policies have been introduced to help tackle the problem, including legislation, performance targets and clinical guidelines. In England, the reporting of meticillin-resistant Staphylococcus aureus bloodstream infections and Clostridium difficile infections are mandatory, and there are national and local targets for reduction as well as national evidence-based guidelines. 23 The development of effective technology interventions to complement good infection control practice is viewed as central to tackling HCAIs, and a range of evidence-based innovations have been developed. Government-funded programmes, such as the Department of Health ‘HCAI Technology Innovation Programme’, 24 have been created to fast-track the innovation process. Programme work streams span development to procurement and implementation processes and include: ‘Smart Ideas’, ‘Design Bugs Out’, ‘Smart Solutions’, ‘Product Surgeries’ and ‘Showcase Hospitals’, the latter focusing on the in-use value of HCAI technologies. In addition, the Health Protection Agency Rapid Review Panel was set up in 2004 to review new HCAI-related technologies providing a prompt assessment of new and novel equipment, materials and other products or protocols that may be of value to the NHS to help reduce HCAI rates; recommendation statements about the novel products are given to suppliers and NHS bodies (‘Recommendation 1’ being the highest, encouraging adoption by the NHS).

Design and theoretical approach

This article reports on findings from a larger innovation adoption study in the area of HCAIs commissioned by the Department of Health (DH). 25 We employed a multiple case study research design to build theory inductively 26 covering the decision-making, procurement and implementation processes by NHS organisations when introducing innovative technologies. We undertook comparative case studies 27 across 12 NHS trusts in England with each trust and technology adoption decisions as units of analysis. Consistent with our research aims, we employed interpretive methods of enquiry, which allows description, interpretation and explanation of a phenomenon rather than estimation of its prevalence. 28

Damanpour and Schneider 14 suggest that the process of innovation adoption in organisations can be divided into three broad phases of ‘pre-adoption’, ‘adoption decision’ and ‘post-adoption’, also referred to in the literature as ‘initiation’, ‘adoption (decision)’ and ‘implementation’. 13 15 26 In this article, we use the latter terminology. Adoption is viewed as a process in which organisational members analyse the potential benefits and negative aspects of an innovation on the basis of gathered knowledge. During this process, three types of innovation knowledge are important in moving potential adopters from ‘ignorance’ through awareness, attitude formation, evaluation and on to adoption—‘the decision to make full use of the innovation as the best course of action available’ 15 :

Awareness knowledge—the awareness that an innovation exists and knowledge of its key properties.

How-to knowledge—the information necessary to use an innovation properly.

Principles knowledge—information dealing with the functioning principles underlying how the innovation works.

The above definitions of innovation knowledge may be relatively simple and consistent when applied to technology adoption by individuals, while they become ambiguous when applied to the organisational setting in which the process is complex and contested. 12 29 Evidence is a form of knowledge and in this article comprises empirical, theoretical and experiential ways of knowing. 30

Sampling and settings

The study comprised one primary and 11 acute care organisations (NHS trusts), across all 10 Strategic Health Authorities in England. The trusts included in the study sample were diverse in geography, size and type ( table 1 ). The sample was predefined with one attribute in common as recipients of DH's ‘HCAI Technology Innovation Award for outstanding contributions to fighting infections 2009’. The trusts were nominated by each Strategic Health Authority on the basis of having excelled in either turnaround or ‘best in class’ concerning infection prevention performance in the fiscal year 2008/2009. The trusts were given free reign to use the sum to procure technologies that could help reduce HCAIs (awarded in February 2009).

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Case study sites characteristics

Data collection and participants

We collected data from secondary sources to provide a historical dimension to better situate the studied decision-making processes.

Data from primary sources comprised 121 semistructured individual and group interviews carried out during the 18 months (July 2009–August 2010). On average, this equates to 10 hour-long interviews per trust. Twelve informants were interviewed more than once. Depending on the number and scope of technologies, we conducted between two to five visits per trust. Within each of the trust sites, we purposively sampled a diverse range of informants involved in the technology adoption or implementation, reflecting various perspectives, professional and organisational roles. Our participants included clinical and non-clinical managers, members of trusts' executive boards, health professionals, staff from estates and facilities and IPC teams comprising: Director of Infection Prevention & Control (DIPC), deputy DIPC, medical microbiologist, infection doctor, infection control nurses (the most populous group), surveillance staff, decontamination lead. Some IPC teams included a pharmacist or infection control matrons.

Interviews explored individuals' perceptions, experiences and views on the technology selection decisions, procurement and implementation processes. In the first visit, the ongoing decision-making process was captured, and in follow-up visits, technology selection outcome and implementation experiences were explored. Field notes were taken during observation of technologies in-use and relevant meetings. Observation was used to familiarise with technologies and context, and triangulate interview data. For example, in one trust, a technology reported in interview accounts as ‘fully implemented’ was not verified as such during observation visits to implementation wards. A total of 20 h of observation were completed, on average 30 min per technology. Data collection at each site continued until all aspects of the decision process had been accounted for by a diverse sample of informants.

Data analysis

We analysed data using an integrated approach. 31 Development of codes was initially derived from the primary data (‘ground-up’), subsequently complemented with an organising conceptual framework for the adoption of complex health innovations. 31 This framework has been previously employed to understand multilevel innovation adoption. 32 Data analysis was conducted in parallel to ongoing data collection to feed emerging findings and ‘test’ these in subsequent interviews. The Qualitative Data Analysis computer software package N-Vivo 8 (QSR International) was used to systematically code the data and assist analysis, especially in cataloguing and linking concepts and codes. In line with recommendations by qualitative methodologists, 33–35 Yiannis Kyratsis (author 1) and Raheelah Ahmad (author 2) independently coded all data. The three authors met to review discrepancies, 31 enhancing internal validity. 35–37 Comparative cases were analysed in two stages: first each of the technologies within each trust, producing individual trust case studies; second, a comparative analysis across the trusts. Summary tables were used to reduce the volume of primary data and to make analytical inferences by comparing and contrasting pairs and groups of cases. 26 We defined the outcomes of the technology adoption process as follows: ‘successful adoption’—the organisational executive decision to make full use of a technology, which results in procurement; ‘successful implementation’—the technology is put into use and operationalised.

Main findings

The organisational innovation process and outcomes.

Of the 38 organisational technology adoption decisions made during the period of the study, 22 technologies were successfully adopted and implemented, while 12 were discontinued after initial adoption or only partially implemented ( table 2 ). There was no clear outcome within the time frame of the study for four technologies. The nature of technologies is described in detail elsewhere. 25 A general typology of technologies isolated from context did not provide insights to likelihood of adoption. As illustrated in table 2 , the same technologies (ie, the Hydrogen Peroxide Vapour System or the ATP Hygiene Monitoring System) in diverse trusts and at different stages of the innovation process resulted in differential outcomes. Most informants reported that they went through a series of evaluations, choices and actions over time as the adopting trusts principally engaged in a problem-solving exercise. The process was dynamic, iterative and not always linear. The IPC team and some wider staff were involved in adoption decisions. While the formal executive decision lay with the DIPCs, they were not always the key decision makers across the cases. The size and professional composition of the IPC teams, and the professional background of the DIPC ( table 1 ), varied. We found that the majority of technology decisions were led by a perceived need—an area of priority in IPC had been identified by trusts first and then relevant technologies were sought (‘need pull’). A minority of technology adoption decisions were characterised by selecting a technology in the first instance and exploring how this might fit with strategic plans and service needs (‘technology push’).

The stage when ‘how-to’ knowledge was first considered in the process and associated outcome

Use of innovation knowledge in the organisational setting

Trusts variably accessed and prioritised the three types of innovation knowledge in the organisational setting, and these comprised a much broader, multidimensional definition compared with a simpler definition when the potential user is an individual. 15 Under ‘awareness’ knowledge, the trusts considered the range of technologies available to address a particular problem, as well as key features and potential cost implications of such technologies. In seeking ‘principles’ knowledge, the trusts sought primarily evidence of the technologies' technical efficacy based on the scientific principles behind the technology. They assessed the validity of claims made by commercial suppliers. In the ‘how-to’ knowledge, the trusts sought knowledge about the practical application of the technologies in local healthcare settings with nine trusts trialling the technologies. This included users' experience with the technologies, aesthetics, functionality as well as compatibility with strategic, structural and cultural elements of the trust's context. A more detailed estimation of the short-term and long-term associated costs also constituted ‘how-to’ knowledge. Cost and effectiveness issues permeated the three types of innovation knowledge. The definition of effectiveness was broader when both ‘principles’ and ‘how-to’ knowledge were given sufficient attention and this ranged from local opinion, including patient perceptions, ease of use by staff, to experimental controlled trials data. The majority of informants from all trusts noted that no particular technology could be solely or directly attributable to reducing HCAIs, and impact was attributable to ongoing multifaceted approaches.

Centralised and local dissemination of innovation knowledge

Those involved in decisions used a wide range of sources to get information on the three types of innovation knowledge ( table 3 ). Peer review journals and commercial suppliers were used in all trusts to source ‘principles’ knowledge. Supplier information was reported as compact and easy to access for practitioners; however, this source was viewed as less credible. Of the government-funded centralised evidence dissemination structures, DH Showcase Hospitals Programme was widely used by trusts for obtaining ‘awareness’ and ‘how-to’ knowledge, but none of the trusts used it for ‘principles’ knowledge. Local expert advice was preferred to the dedicated central expert panel (Rapid Review Panel) for obtaining ‘principles’ knowledge, while guidelines were used by only three trusts. Professional networks consistently featured among the top sources for all three types of innovation knowledge. The latter were used to exchange experiences on the use of the same or similar technologies, spreading information horizontally via networks of peers and local experts.

Type and sources of innovation knowledge used in the technology adoption process per trust

Critical timing of innovation knowledge use

We found that at the earlier stages of the process, ‘principles’ knowledge was given more attention overlooking important aspects of ‘how-to’ knowledge. When ‘how-to’ knowledge was considered late, there were negative implications for the adoption and implementation of the technologies ( table 2 ). For example, ‘how-to’ knowledge was not considered early on in Trust 4 for the ultraviolet light air sterilisation units, and consequently, the technology was discontinued after initial adoption. Hidden running costs, such as replacing costly bulbs and filters regularly, as well as the practicality of assembling units on site, were overlooked. Conversely, when ‘how-to’ knowledge was considered earlier by decision makers, successful technology adoption and implementation was evident. The 14 technology cases for which ‘how-to’ knowledge was first considered during the ‘initiation’ stage were all adopted and implemented successfully. The 10 technology cases for which ‘how-to’ knowledge was first considered during the ‘adoption decision’ stage, mainly during pre-adoption evaluation trial, resulted in informed organisational decisions to either adopt or reject technologies; and for those technologies adopted led to subsequent successful implementation. For the 10 technology cases where ‘how-to’ knowledge was first considered during ‘implementation’, uptake was challenging leading to unsuccessful implementation following initial adoption.

Looking in more detail at an example where ‘how-to’ knowledge was inadequately considered in the early stages of the process is that of ultrasonic cleaning tanks in Trust 5: [the technology] was very definitely sold as a replacement for manual cleaning…we embarked in the belief that using the tank would mean that when the equipment came out at the other end and was dried it would be safe to use on the next patient…we didn't feel comfortable [after having tested the tanks for bacteria levels in water after cleaning] and we felt that to make these pieces of equipment safe we would then manually go over them with a disinfectant…and this means additional workload [Senior IPC Nurse]

Important aspects of structural incompatibility only came to light during implementation. The water in the tanks needed to be replaced after each cleaning session, refilled and water heated overnight. This added to the hospital staff workload. The tanks needed to be hardwired for electricity, which meant no manoeuvrability—the initial plan had been to move the tanks around the hospital rather than shift dirty and bulky items to the tanks. The technology though purchased by the trust, resulted in becoming obsolete; the tanks were housed by estates in a storage area on the top floor of the hospital and used in a very different way from the original plan.

An example where detailed attention was given to ‘how-to’ knowledge during the ‘adoption decision’ stage informed subsequent purchases of infection control computer keyboards and mice (fully enclosed and flat design enabling quick and thorough cleaning) used with Picture Archiving and Communication Systems in clinical areas. In Trust 8, feedback from chest consultants (principal users of the technology) resulted in appropriate procurement of computer devices, which were consistent with working practices as well as compliant with infection prevention guidelines: Had we not changed [the newly introduced] flat computer mouse to replace it with one that has got a push scrolling button, the targeted users would not have used it at all; it is highly likely that they would have replaced them with normal computer mouse instead… [Trust 8]

The influence of professional background and organisational type

We found variation in the priority given to the type of innovation knowledge across professional groups. Nurse professionals involved in adoption decisions reported taking an approach where careful focus on ‘principles’ knowledge was balanced with adequate attention to ‘how-to’ knowledge. Conversely, medical professionals always prioritised ‘principles’ knowledge. Consistently across the trusts, consultant microbiologists, clinical matrons and infection control nurses looked at the same technologies differently and came to divergent decisions regarding the value of specific technologies. Specifically in T4, T6, T7, T10, T11, the clinical microbiologists valued almost exclusively ‘principles’ knowledge to judge the effectiveness and appropriateness of technologies for the trusts. Clinical microbiologists across trusts, looked primarily at peer-reviewed published articles for such information. In contrast, clinical matrons preferred more applied information about technology effectiveness and would discount solely technical accounts, as the following quote illustrates: “You don't want such jargonistic information. You need to make it very simple to say this is how it works. These are the benefits, blah, blah, blah, rather than going to such, you know, higher level of microbiology” [Clinical Matron].

An IPC nurse in the same trust highlighted the importance of combining ‘how-to’ and ‘principles’ knowledge to assess effectiveness and appropriateness of the technologies: You need both evidence from [peer review] papers and the practicality of using the product [in the local context]. It's very important [IPC Nurse].

Trusts affiliated with universities, comprising research active organisations (T3, T4, T5, T8, T10, T11—also see table 1 ), prioritised and systematically searched for scientifically produced ‘principles’ knowledge. This attitude was mirrored across professional groups, though was more pronounced in accounts by respondents from the medical profession.

We found the technology adoption process to be highly dynamic and iterative. Adoption decisions entailed the acquisition and processing of new knowledge by organisational members who sought to reduce uncertainty about an innovation. Trying to find solutions to problems was the key motivator for sourcing evidence across the cases.

Scientifically produced ‘principles’ knowledge was prioritised by those involved in decisions to judge effectiveness of technologies. Empirical and experiential types of knowing were also widely used to judge the effectiveness and appropriateness of the technologies in the local setting but were often assessed later in the process. This late consideration of ‘how-to’ knowledge had implications for successful adoption and implementation. In the cases where ‘how-to’ knowledge was given least priority during the early stages of ‘initiation’ and ‘adoption decision’, issues that should have been picked up when adoption decisions were being made came up at implementation trial and even once trust-wide implementation had begun. This resulted in (a) increased likelihood of technology rejection or protracted procurement decision at the ‘adoption decision’ stage, (b) delayed or incomplete implementation or discontinuance (following initial adoption) during the stage of ‘implementation’.

Commercial suppliers and peer review publications were used as often as each other for ‘principles’ knowledge while noting potential supplier bias. Suppliers responded to preferences for theoretical knowledge by a highly professionalised user group. This is in contrast to individual consumers where marketing, as well as consumer interest is focused on ‘awareness’ and ‘how-to’ knowledge. 15 Centralised (health system) structures were particularly underused as sources for ‘principles’ knowledge and were reported as less accessible and less relevant to the local context. Professional networks were widely used and comprised practice-based peer-mediated information about the innovations' relevance to the local setting.

The priority given to the three types of innovation knowledge depended on: (a) type of trust—teaching hospitals or research active organisations prioritised ‘principles’ knowledge; (b) professional background of those involved in adoption decisions—members of the medical profession tended to prioritise ‘principles’ and often ignored ‘how-to’ knowledge, while members of the nursing profession tended to balance the use of ‘principles’ and ‘how-to’ knowledge.

Strengths and weaknesses

The scale of the study and the real-time nature of investigating 38 adoption and implementation processes over a period of 18 months provided a rich data set. Our study is theory driven and comprises multisite comparative case studies, which overall enhance the generalisability of findings beyond the context of the specific sites studied. 27 We explicitly studied cases of non-adoption and discontinuation after initial adoption, which are rarely included in innovation diffusion studies. We looked at centralised, organisational, professional and local influences in the process.

On limitations, the predefined sample in our study was not exhaustive by trust type, though sufficiently diverse ( table 1 ). At the same time, a common barrier to adoption (availability of funding) was ‘controlled for’ in this sample, allowing other factors during adoption decision to be explored. We were not able to follow implementation past the end of August 2010 and therefore do not have information on routinised use of the implemented technologies.

Important differences in results with other studies

While innovation literature in commercial sectors considers the types of innovation knowledge in technology adoption by individuals, 15 the role of these types of knowledge in organisational decisions within the highly professionalised context of a healthcare system is missing. The types of trusts, and the professional background of those involved in technology adoption decisions influenced how technologies were adopted and implemented in our study. These factors had bearing on the type of innovation knowledge used and timing of this knowledge utilisation. These findings build on literature that identifies interactions between the innovation, local actors, leadership and multilevel contextual factors 10 12 38–40 shaping the technology adoption process. Furthermore, our study demonstrates an impact of variable use of knowledge on ‘successful’ adoption decisions. The role of professional backgrounds in this process builds on work by Ferlie and colleagues 5 who looked at the adoption of guidelines in four areas of clinical care and found that there are cognitive, social and epistemic barriers to knowledge flow among health professionals.

Data from all cases show that ‘how-to’ knowledge was important in the innovation process, not only operationally but also strategically, spanning issues of structural and cultural compatibility, and sustainability. This broader conceptualisation better aligns the construct with the complex adjustments that are often needed in organisational settings. 6 29 Our findings suggest a more prominent focus for ‘how-to’ knowledge in the future, by both practitioners and researchers. 41 42

Implications for clinicians and policymakers

Health systems remain to fully exploit patient benefit through sustainable use of evidence-based technologies. 43 44 Balancing ‘principles’ and ‘how-to’ knowledge at the early stages of the innovation process will provide decision makers with clinical and financial justification for innovations, as well as practical implementation guidance. Learning from discontinued adoption or failed implementation of technologies is as important as success stories.

Data from all our cases show that acceptance of innovation knowledge depended on the perceived credibility of the source. Current health policy practice, as outlined in the introduction, is implicitly founded on the notion that health professionals do access primarily centralised sources to acquire knowledge about innovative technologies. Our findings differ, emphasising a more prominent role of local and peer-mediated sources, such as professional associations, local practice trials, experiences of peers and local experts. Given the patterns of knowledge exchange among our respondents, investing in horizontal knowledge exchange to complement ‘top down’ knowledge transfer is indicated. Appraising the local environment for structural and cultural compatibility of the technologies is essential along with evidence for efficacy and cost-effectiveness to avoid waste of valuable resources and potential to cause inadvertent harm from inappropriate implementation.

There are implications here of who is involved in the innovation adoption process and the role played by key decision makers. Since healthcare services are increasingly configured as multiprofessional team activities 45 organisational innovation adoption decisions need also to account for local attitudes to evidence of different professional groups. Policy makers need to reconcile the need for central guidance and quality standards with locally relevant practice-based evidence to contextualise the research in line with practical needs.

Future research and unanswered questions

More work is needed to understand how organisational priorities shape the perspective of organisational leaders and other key decision makers as regards innovation knowledge. In particular, a better understanding of the dynamics in the late stages of the innovation process in organisations (implementation and routinisation) is needed. A study in progress funded by National Institute for Health Research Service Delivery & Organisation (NIHR SDO) considers such issues in depth. 46

A number of other questions remain unanswered. Future studies need to account for individual and organisational motivation to source evidence. Also, given that different professionals view different sources and types of evidence differently, how can these differences be reconciled? And who can play the role of ‘evidence broker’? Finally, we need to account for wider influences of different health system structures (centralised tax based versus disaggregated ‘market’ systems) and how these shape use of evidence and, ultimately, innovation uptake.

Acknowledgments

We thank the participating hospitals for supporting this work, and we thankfully acknowledge all respondents who participated in the study and kindly gave their time. We are also grateful to Sue Smalley from the Department of Health for project support and constructive feedback and Professor Charles Vincent from Imperial College Centre for Patient Safety and Service Quality for reviewing the draft manuscript. The authors acknowledge the United Kingdom Clinical Research Collaboration (UKCRC) who fund the National Centre for Infection Prevention and Management and the National Institute for Health Research Biomedical Research Centre at Imperial College London.

• Blumenthal D
• Fitzgerald L ,
• Greenhalgh T ,
• Macfarlane F ,
• ↵ Department of Health . Report of the High Level Group on Clinical Effectiveness . London : Department of Health , 2007 .
• ↵ Clinical Effectiveness Research Agenda Group . An Implementation Research Agenda: A Report Prepared for the High Level Group on Clinical Effectiveness by the Clinical Effectiveness Research Agenda Group . London, UK : Clinical Effectiveness Research Agenda Group , 2008 .
• Kimberly JR
• Nutley SM ,
• MacFarlane F ,
• ↵ Department of Health . Innovation Health and Wealth, Accelerating Adoption and Diffusion in the NHS . Leeds, West Yorkshire : Department of health NHS Improvement & efficiency Directorate innovation and service Improvement , 2011 : 36 .
• Damanpour F ,
• Schneider M
• Van de Ven AH ,
• Polley DE ,
• Umscheid CA ,
• Mitchell MD ,
• Klevens RM ,
• Edwards JR ,
• Richards CL Jr . ,
• McIlvenny G ,
• Enstone JE ,
• ↵ Office for National Statistics . Health Statistics Quarterly 39 . Surrey, UK : Palgrave Macmillan , 2008 .
• Pellowe C ,
• Loveday HP ,
• ↵ Department of Health . Clean Safe Care: Reducing Infections and Saving Lives . London, UK : Department of Health , 2008 : 40 .
• Kyratsis Y ,
• Eisenhardt KM
• Brechin A ,
• Bradley EH ,
• Kyratsis I ,
• Ziebland S ,
• Britten N ,
• FitzGerald L ,
• Langley A ,
• Gladwin J ,
• Halladay M ,
• Cornford T ,
• Ashcroft J ,

To cite: Kyratsis Y, Ahmad R, Holmes A. Technology adoption and implementation in organisations: comparative case studies of 12 English NHS Trusts. BMJ Open 2012; 2 :e000872. doi: 10.1136/bmjopen-2012-000872

Contributors YK and RA conceived the idea for the paper, and collected and systematically analysed all data. All three authors interpreted the data. YK designed the initial study and drafted the article, RA contributed to study design and all three authors revised it critically for important intellectual content. All three authors approve the content of the manuscript submitted.

Funding This study is an independent study that was funded by the DH. The DH influenced the execution of the study as follows: the award from DH came with an agreement in principle with the trusts to participate in the study. Access to the participating trusts in the first instance was via DH by means of an introductory letter sent by Sue Smalley from DH. The trusts were then approached by a member of our research team. The researchers did provide update to DH on progress regards access and spend on technologies. The DH had no other interference in the design, execution and interpretation of findings.

Competing interests All authors have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

Ethics approval Ethical approval was not required for the study under NHS research governance arrangements (letter dated 23 April 2009 by Hammersmith and Queen Charlotte's and Chelsea Research Ethics Committee). The research was classed as service evaluation by the chairman of the committee. Access to the participating trusts was via DH in the first instance through an introductory letter. The trusts were then approached by a member of our research team. The project lead and IPC teams in each trust further facilitated access to those involved in the decision-making, procurement and implementation of the selected technologies. Prior informed consent to join the study was obtained in writing by participating individuals. Yiannis Kyratsis (Author 1) and Raheelah Ahmad (Author 2) conducted the interviews, both experienced qualitative researchers with no prior relationship with the informants. Interviews were guided by a topic guide. All interviews, but one, were audio-recorded. Audio-recorded interviews were transcribed verbatim by professional transcribers and then checked by the researchers for accuracy. Primary data were anonymised and stored securely on password-protected computers prior to processing.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement There are no additional unpublished data available.

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Hacking the Case Interview

If you’re interviewing with technology consulting firms, such as Accenture or Cognizant , you will almost definitely be given a few technology case interviews during the interview process. To successfully land a technology consulting job offer, you’ll have to nail each and every case interview.

While technology case interviews may seem intimidating and challenging, they can be consistently solved with the right strategies and practice. In this article, we’ll cover:

• What is a technology case interview?
• The 6 steps to solve any technology case
• Technology case interview frameworks
• 7 technology case interview examples to practice
• Recommended technology case interview resources

If you’re looking for a step-by-step shortcut to learn case interviews quickly, enroll in our case interview course . These insider strategies from a former Bain interviewer helped 30,000+ land consulting offers while saving hundreds of hours of prep time.

What is a Technology Consulting Case Interview?

Technology consulting is a specialized type of consulting that focuses on helping companies use technology better to be more productive and profitable. Just as with any consulting firm, technology consulting firms use case interviews to identify candidates that have the potential to become great consultants.

As you would expect, technology consulting case interviews focus on business problems that center around a company’s use of technology. Technology cases place you in a hypothetical business situation in which you will work with the interviewer to develop a recommendation or solution to a technology problem.

Types of business situations that you could expect to see in technology consulting cases include:

• Deciding whether a company should buy or build a particular technology solution
• Deciding which vendor a company should partner with for their technology solution
• Deciding whether a company should develop technology in-house or outsource development elsewhere
• Determining whether outsourcing of technology should be done onshore or offshore

Technology consulting firms use case interviews because they assess a variety of different qualities and traits in just a 20- to 30-minute exercise. There are five major qualities that technology case interviews assess:

Logical, structured thinking : Can you structure complex problems in a clear, simple way? Can you use logic and reason to make appropriate conclusions?

Analytical problem solving : Can you read and interpret data well? Can you conduct the right analyses to draw the right conclusions?

Business acumen : Do you have a basic understanding of fundamental business and technology concepts? Do your recommendations make sense from a feasibility perspective?

Communication skills : Can you communicate in a clear, concise way? Are you articulate in what you are saying?

Personality and cultural fit : Are you coachable and easy to work with? Are you pleasant to be around?

The 6 Steps to Solve Any Technology Case Interview

The approach to solving technology consulting cases is generally the same as traditional case interviews. Generally, you’ll want to follow these six steps.

1. Understand the case

Your technology case interview will begin with the interviewer giving you the case background information. While the interviewer is speaking, make sure that you are taking meticulous notes on the most important pieces of information. Focus on understanding the context of the situation and the objective of the case.

Don’t be afraid to ask clarifying questions if you do not understand something. You may want to summarize the case background information back to the interviewer to confirm your understanding of the case.

The most important part of this step is to verify the objective of the case. Not answering the right business question is the quickest way to fail a case interview.

2. Structure the problem

The next step is to develop a framework to help you solve the case. A framework is a tool that helps you structure and break down complex problems into smaller, more manageable components. Another way to think about frameworks is brainstorming different ideas and organizing them into different categories.

Before you start developing your framework, it is completely acceptable to ask the interviewer for a few minutes so that you can collect your thoughts and think about the problem.

Once you have identified the major issues or areas that you need to explore, walk the interviewer through your framework. They may ask a few questions or provide some feedback.

For a complete guide on how to create tailored and unique frameworks for each case, check out our article on case interview frameworks .

3. Kick off the case

Once you have finished presenting your framework, you’ll start diving into different areas of your framework to begin solving the case. How this process will start depends on whether the case interview is candidate-led or interviewer-led .

If the case interview is a candidate-led case, you’ll be expected to propose what area of your framework to start investigating. So, propose an area and provide a reason for why you want to start with that area. There is generally no right or wrong area of your framework to pick first.

If the case interview is interviewer-led, the interviewer will tell you what area of the framework to start in or directly give you a question to answer.

4. Solve quantitative problems

Technology cases typically have some quantitative aspect to them. For example, you may be asked to calculate a certain profitability or financial metric.

The key to solving quantitative problems is to lay out a structure or approach upfront with the interviewer before doing any math calculations. If you lay out and present your structure to solve the quantitative problem and the interviewer approves of it, the rest of the problem is simple execution of math.

5. Answer qualitative questions

Technology case interviews will also typically have qualitative aspects to them. You may be asked to brainstorm a list of potential ideas. You could also be asked to provide your opinion on a particular business issue or situation.

The key to answering qualitative questions is to structure your answer. When brainstorming a list of ideas, develop a structure to help you neatly categorize all of your ideas. When giving your opinion on a business issue or situation, provide a summary of your stance or position and then enumerate the reasons that support it.

6. Deliver a recommendation

In the last step of the tech case interview, you’ll present your recommendation and provide the major reasons that support it. You do not need to recap everything that you have done in the case, so focus on only summarizing the facts that are most important.

It is also good practice to include potential next steps that you would take if you had more time or data. These can be areas of your framework that you did not have time to explore or lingering questions that you do not have great answers for.

Technology Case Interview Frameworks

While the approach to solving technology case interviews is typically the same as traditional case interviews, there are some frameworks you should be familiar with that are specific to technology issues.

Some of these frameworks are more technical than others.

Generally, if you have a strong IT or technology background and are interviewing for a more senior role, you should expect your technology case interviews to be more technical. However, if you are interviewing for an entry level technology consulting role, you’ll likely not need to know many of these frameworks.

PPT Framework

The PPT framework stands for people, process, and technology. These are the three components that are necessary for organizational transformation and management. To achieve organizational efficiency, a company needs to have all three of these components streamlined.

People : Do employees have the right skills, experience, and attitude for the job? Do they have clear roles and responsibilities? Does the project have buy-in from the right people?

Process : Are the right processes in place? Are these processes run smoothly and efficiently? Are there potential bottlenecks or roadblocks?

Technology : Are the right technologies being used? Are these technologies being used to their maximum potential?

Factors to Evaluate Technology Framework

Often, you’ll need to use a framework to evaluate different pieces of technology or different potential technology vendors to work with. One of the most common ways of doing this is by assessing each option on the basis of the following three factors.

Ability to meet requirements : Does the technology or vendor satisfy all of the requirements?

Cost of project : What is the fully-loaded cost of the project? Do the costs meet the designated budget?

Time to launch : How long will it take to launch and implement the solution? Does this timeline satisfy goals and expectations?

ITIL Framework

ITIL stands for Information Technology Infrastructure Library. It is the first of our more technical frameworks for technology case interviews.

The ITIL framework is one of the most widely used approaches for managing IT services. IT services use the ITIL framework to ensure that their services are delivered in a customer-focused, high-quality, and economical way.

There are five stages in the lifecycle of information technology.

Service Strategy : Decide on a strategy to serve customers by starting with an assessment of customer needs and the market place. Determine which services the IT organization should offer and what capabilities need to be developed.

Service Design : Design new IT services, which includes making changes and improvements to existing services.

Service Transition : Build and deploy IT services. Ensure that changes to services are carried out in a coordinated way.

Service Operation : Ensure that IT services are delivered effectively and efficiently. This includes fulfilling user requests, resolving service failures, fixing problems, and carrying out routine operational tasks.

Continual Service Improvement : Learn from past successes and failures to continually improve the effectiveness and efficiency of IT processes and services.

TOGAF Framework

TOGAF stands for The Open Group Architecture Framework. It provides an approach for designing, planning, implementing, and governing an enterprise information technology architecture.

TOGAF is based on four areas of specialization called architecture domains:

Business architecture : The business strategy, governance, organization, and key business processes of the organization

Data architecture : The structure of an organization’s logical and physical data assets and the associated data management resources

Applications architecture : The blueprint for the individual systems to be deployed, the interactions between application systems, and their relationships to the core business processes of the organization

Technical architecture : The hardware, software, and network infrastructure needed to support the deployment of core and mission-critical applications

CMMI Framework

CMMI stands for Capability Maturity Model Integration and is used to guide process improvement across a project, division, or entire organization. CMMI defines five maturity levels for processes.

Level 1: Initial : Processes are unpredictable, poorly controlled, and reactive.

Level 2: Managed : Processes are characterized for projects and are often reactive.

Level 3: Defined : Processes are characterized for the organization and are proactive.

Level 4: Quantitatively Managed : Processes are measured and controlled.

Level 5: Optimizing : Processes are not only measured and controlled, but also focused on process improvement.

Technology Case Interview Examples

There are much fewer technology practice cases available online compared to traditional case interview cases. However, Deloitte’s case interview website offers two technology consulting cases that you can work through on your own.

• MedX: The Smart Pill Bottle (business technology case)
• Architecture Strategy: Federal Finance Agency (business technology case)

For more practice, check out our article on 23 MBA consulting casebooks with 700+ free practice cases .

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• Open access
• Published: 22 July 2024

Unveiling the heterogeneous utilisation of the same digital patient management platform: case studies in primary healthcare in Sweden

• Susanne Frennert 1 ,
• Christofer Rydenfält 1 ,
• Mirella Muhic 2 &
• Gudbjörg Erlingsdóttir 1  

BMC Health Services Research volume  24 , Article number:  831 ( 2024 ) Cite this article

Metrics details

The utilisation of digital technology in primary healthcare, particularly digital patient management platforms, has gained prominence, notably due to the global pandemic. These platforms are positioned as substitutes for face-to-face consultations and telephone triage. They are seen as a potential solution to the escalating costs associated with an aging population, increasing chronic conditions, and a shrinking healthcare workforce. However, a significant knowledge gap exists concerning the practical aspects of their implementation and their effect on the utilisation of digital patient management in primary healthcare.

This study addresses this gap by conducting a comprehensive analysis of three case studies involving the implementation of a specific digital patient management platform. Over a period of three years, we examine how the practicalities of implementation shape the adoption and utilisation of a digital patient management platform in three different clinics.

Our findings revealed that differences in implementation strategies directly influenced variations in utilisation. The successful utilisation of the platform was achieved through a bottom-up decision-making process that involved the employees of the primary healthcare clinics. Onsite training, close collaboration with the eHealth provider, and a structured patient onboarding process played crucial roles in this utilisation. In contrast, a top-down approach at two of the primary healthcare clinics led to limited utilisation of the platform into daily workflows. Furthermore, making the platform a part of everyday work meant putting accessibility, by working as a team of physicians, at the forefront of continuity of care, with patients being managed by their designated physician. Additionally, it was observed that digital patient management proved most effective for addressing simple patient issues such as skin rashes, rather than complex cases, and did not reduce the demand for phone triage.

Only one of the three clinics studied effectively integrated digital patient management into its daily operations, and did so by aligning objectives among management and all categories of healthcare professionals, employing a bottom-up decision-making process, collaborating with the eHealth service provider for regular platform adjustments to clinic needs, and implementing active patient onboarding. This sociotechnical integration resulted in high platform utilisation. In contrast, the other two clinics faced challenges due to incoherent objectives among diverse healthcare professional employees and top management, a top-down decision-making approach during implementation, limited collaboration with the eHealth service provider, and passive patient onboarding. The findings indicate that these factors negatively affected utilisation and led to low platform adoption as well as disrupted the sociotechnical balance.

Peer Review reports

The potential to use digital patient management, such as digital triaging and video consultations, as a substitute for in-person primary healthcare consultations and phone triaging has been widely acknowledged [ 1 ]. The digital transformation of primary healthcare is portrayed as a solution to address the rising costs associated with an aging population, increasing chronic conditions, and reduced workforce. It represents a dominant sociotechnical imaginary [ 2 ], and many countries have promoted and invested in the digital care imaginary (e.g., United Kingdom, Australia, Denmark and more) [ 3 , 4 , 5 , 6 ]. One of these countries is Sweden, which since 2006 has had three national eHealth strategies [ 7 , 8 , 9 ]. All these strategies express similar values and arguments: innovation, patient-centred care, patient engagement, efficiency, availability, accessibility, equality and privacy [ 10 ]. The most recent eHealth strategy, in 2016, states that Sweden by 2025 ‘will be best in the world at using the opportunities offered by digitisation and eHealth, making it easier for people to achieve good and equal health and welfare, and to develop and strengthen their own resources for increased independence’ [ 9 ].

Amidst this digital transformative landscape, our study aimed to investigate a fundamental question that has received limited attention: why does the practical application of digital patient management result in varying patterns of usage across different primary healthcare practices? This question is of utmost importance, as the outcomes of these implementations can deeply affect patient care, healthcare professionals’ working environment, and the healthcare system as a whole.

Despite the prominence of eHealth strategies, and the acceleration of digital technologies in healthcare due to the recent global pandemic [ 11 , 12 ], a critical gap persists in our understanding of technology implementation in primary healthcare. A scoping review revealed that there is limited evidence on the actual effects of digital patient management in primary care on the working environment [ 13 , 14 ]. What we do know is that phone triaging, predominantly performed by nurses, has often been the source of considerable stress [ 15 ]. In contrast, studies on digital patient management have generated contradictory results: digital patient management has been credited for providing nurses with greater flexibility and autonomy [ 16 ], while it has been criticised for increasing nurses’ workload [ 17 ], and loss of job control [ 18 ].

This apparent contradiction may be explained by the fact that digital patient management platforms are not isolated entities but part of broader sociotechnical systems, including not only technology, but also people, organisations, norms and values [ 19 ]. According to sociotechnical systems theory, the same technology can yield different outcomes because technology and technical aspects interrelate with social and organisational aspects. As such, working with digital patient management is a social process in which healthcare professionals need to match the problem (sick individuals) to a non-standardised solution, as dealing with sick individuals requires ad hoc and pragmatic responses, which often entails collaboration with other healthcare professionals [ 20 ]. When new technology is introduced, healthcare professionals as well as patients need to make sense of the technology and negotiate regarding the ways in which it fits their needs [ 21 ]. Theories on technology-in-practice, show that healthcare technologies are not only shaped by healthcare professionals but also mediate care praxis [ 22 ]. Thus, a growing body of literature on digital patient management highlights the importance of extending the scope beyond the impact of digital patient management per se, to understand what factors facilitate its implementation [ 23 , 24 , 25 , 26 ] and healthcare professionals’ lived experiences of its impact on their work and working environment [ 27 ].

Thus, the purpose of this paper is to offer an in-depth exploration of the implementation of a common digital patient management platform at three primary healthcare clinics – a study extending over a three-year period. In one clinic, digital patient management became seamlessly integrated into daily care practices, while in the other two, it remained underutilised. The heart of our investigation seeks to unravel the reasons behind these disparate patterns of utilisation. By doing so, we hope to provide valuable insights for healthcare practitioners, guiding them to circumvent potential pitfalls and facilitate the successful adoption of digital patient management in the realm of primary care.

This qualitative study applied a multiple case study design with three different cases [ 28 ]. Below we will first present the digital patient management platform being implemented and give a brief overview of the case organisations. We will then describe the data collection and analysis process.

The digital patient management platform

The digital patient management platform includes automatic triage, e-consultations (via chat and video), and case management [ 29 , 30 , 31 ]. The patients access the platform online via a computer, tablet or smartphone, describe their symptoms and receive automated questions depending on their input; the system finalises a medical report that can be viewed by the healthcare professionals at the primary healthcare clinic. Initially the medical report is reviewed by a nurse, who either handles the patient request or forwards it to a physician, psychologist or physiotherapist. Healthcare professionals have an internal chat function in which they can discuss patient cases and actions needed. Communication with patients, as well as appointments, can occur synchronously or asynchronously in the form of digital or physical meetings with different categories of healthcare professionals.

The case organisations

The cases are three primary healthcare clinics located in southern Sweden. Two are owned by a private healthcare provider and (see Table  1 , clinics B & C) turned out to be very similar, especially in regard to the objectives of implementing the platform, communication about the platform with patients, training, responsibility of the change team, leadership engagement, and how the nurses’ and physicians’ work was organised, while the other clinic belongs to a Christian foundation (see Table  1 , clinic A). This primary healthcare clinic differed from the other two, especially in terms of coherence with its overall communication with patients about the digital patient management platform, responsibility of the change team, training, leadership engagement, and how the nurses’ and physicians’ work was organised.

Data collection

The multiple-case study was carried out between 2020 and 2023 using a series of in-depth semistructured interviews and observations (see Table  1 and Appendix A ). The selection of participants was facilitated by the head of management at each clinic. The inclusion criteria were healthcare professionals who worked with patients and would use the digital patient management platform when implemented. The researchers attended an introductory meeting with the head of management and the clinic’s employees, during which the project and methodology were presented. All employees were asked about their willingness to participate and to inform their managers of their decision. The managers then provided the researchers with contact details of those who wanted to be interviewed and the nurses who agreed to be observed. Subsequently, the participants received written information about the project and signed informed consent forms. As the project spanned over three years, many of the initial participants left the clinics and we had to contact the head of management throughout the years for contact details of potential new participants, whom we then contacted via email. Those who agreed to participate were interviewed.

Over the course of the three-year project, we conducted interviews with psychologists, physicians, rehabilitation coordinators, heads of management, line managers, and medical secretaries before, during, and after the deployment of the digital patient management platform. The interviews lasted between 15 min and one hour, depending on the healthcare professionals’ experience with the platform. In total, 75 interviews were conducted. All interviews were recorded and transcribed.

In Spring 2020, Clinic A implemented the digital patient management platform. The first round of interviews was conducted at the beginning of 2020 via videoconferencing. Due to pandemic restrictions, observations could not be conducted prior to the platform’s implementation. The second round of interviews at Clinic A was conducted in Spring 2021 through videoconferencing and the third round of interviews took place in 2022. In Spring 2022, observations were conducted involving three nurses who worked half a day with phone triage and half a day with patient errands on the digital platform.

Clinics B and C implemented the digital patient management platform in Autumn 2021. The first round of interviews at clinics B and C was carried out in Spring 2021 via videoconferencing. Observations were performed in September 2021, before the platform’s implementation, coinciding with the lifting of pandemic-related restrictions in Sweden [ 32 ]. Three nurses were observed at both clinic B and clinic C. At clinic B, each nurse was observed for half days, whereas at clinic C, observations lasted full days. The reason for half days at clinic B was due to the management’s decision on how long the observations were allowed to last. The second and third rounds of interviews at clinics B and C were conducted in Spring 2022 and Spring 2023. A second round of observations was conducted in 2023 at both clinics. The objective was to observe the same three nurses who were initially observed prior to the platform’s implementation while engaged in phone triage, but in this round of observations, they were using the digital patient management platform. At clinic B, all nurses from the initial observations had departed and we had to recruit new nurses. These new nurses were observed first while performing phone triage (over four half days) and subsequently while using the digital platform (over another four half days). At Clinic C, the same three nurses from the initial round of observations were observed again.

During the phone triage observations, focus was on nurses’ verbal communication with patients, the frequency and nature of interruptions, interactions with colleagues, time for handling patient errands, and observable emotional responses such as frustration and satisfaction. The digital triage observations focused on the same factors as with the phone triage observations but with an additional emphasis on written communication with patients and the usage of the digital platform. The observations resulted in fieldnotes.

We employed hermeneutic interpretive phenomenology to make sense of how healthcare professionals experienced the implementation of the digital patient platform in different clinics [ 33 ]. The primary focus of our analysis was to gain insights into the practical aspects of the implementation from the healthcare professionals’ point of view. The substantial volume of data was sequentially analysed throughout the three years (see Table  1 ). We wrote summaries of central concerns, which were redefined based on the data collected during subsequent interviews and observations. We reviewed and refined these summaries throughout the study, identifying key aspects, such as how patients learned about the digital patient management platform at the healthcare clinic, implementation strategies, such as forming a team of super users, the type of training received by employees, engagement from management, and how nurses and physicians planned to incorporate digital patient management. These aspects formed the foundation for case descriptions. We selected quotations to illustrate the lived experiences of healthcare professionals. The core findings concerning the practicalities of implementation and their variations were shared with stakeholders at national conferences, and further refined through discussions with stakeholders.

Description of the cases

The implementation process at the three primary clinics in our study varied. Table  2 provides descriptions of the clinics, illustrating the similarities and differences in terms of objectives for implementing the digital patient management platform, patient communication about the platform, change team, training, leadership engagement, nurses’ work and physicians’ work.

Primary healthcare clinic A

Clinic A, a middle-sized clinic with approximately 9000 listed patients and 35 employees, began exploring ways to utilise digital technology for their patient interactions in 2019. They identified phone triaging as a source of stress, particularly for nurses, as they received many calls and had a backlog of patients. In response, one of the physicians suggested piloting a digital patient management platform. After conducting exploration and consulting with an eHealth service provider, the healthcare professionals and the clinic manager jointly decided to pilot the digital patient management platform for several months to evaluate its effectiveness and user experience.

Prior to the pilot, the manager received implementation materials from the eHealth service provider, based on Kotter’s approach to change management [ 34 ]. The eHealth service provider stressed the need to establish a clear vision for the digital patient management platform’s implementation, focusing on optimising patient flow, relieving nurses of phone triage duties, and enhancing knowledge exchange among healthcare professionals. They also recommended forming a team of ‛super users’, comprising different professions, to streamline workflows. Aligning the clinic’s schedule with the platform and providing resources for staff adaptation were key priorities. Effective communication channels for issue resolution and patient promotion of the platform were emphasised, with a focus on the correlation between increased platform usage and perceived benefits.

Primary healthcare clinic A embraced these recommendations, acknowledging the importance of reducing nurse workloads and improving patient accessibility to compete with online doctor services and meet patient expectations. A team of super users, comprising a physician and two experienced nurses, was established to oversee implementation. The physician assumed an informal leadership role in implementing and advocating for the digital patient management platform. Physical training sessions were arranged for all employees, allowing them to express their hopes and concerns before implementing the digital platform. Many employees expressed that the platform was easy to learn and use; one employee noted:

I sat with colleagues and learned. It is a pretty simple system [the digital patient management platform]. There are not many buttons to press, so you actually learn the system very quickly.

Employees actively participated in reshaping their workflow, advocating for the platform to patients and colleagues, emphasising benefits such as increased patient accessibility and reduced workload, particularly for nurses. As one nurse at primary healthcare clinic A explained:

There are many people I refer to our chat [the digital patient management platform] if I have the opportunity because it makes it easier for me, and I can make an assessment faster … a lot of skin rash assessments and things where the patient can send in pictures so you can see, instead of just hearing their explanations.

Regular meetings were held before, during, and after implementation, providing a forum for employees to express their expectations, experiences, and concerns. The physician on the team of super users presented data on patient flow and patient experiences during these meetings, helping the team identify obstacles. The eHealth service provider regularly adjusted the digital patient management platform based on feedback from clinic A.

Initially, nurses worked for half a day on the digital platform. However, it became evident that they needed to work full days due to the drawn-out nature of asynchronous communication. Working full days allowed them to complete most patient requests, as asynchronous communication required them to manage multiple patient requests concurrently over longer time periods. To further enhance the workflow, patient requests to physicians were handled by a team of physicians instead of being assigned to the patient’s designated physician. The team of physicians was allocated specific time slots to work via the platform, ensuring that patient requests were always attended to, even if an individual physician was unavailable due to physical appointments, illness, or other reasons. This team approach minimised the risk of missed patient requests, and ensured smooth handling of requests. The head management at clinic A explained:

We are divided into teams , and there is always someone from the team who is in the chat [the digital patient management platform] every day and can make prescription requests and patient requests. Initially , we saw that there was a concern about handing over a patient errand to a designated physician who may be absent for a day , and then the physician becomes sick for a week … patient requests were left hanging … now we work very team-based … you transfer the patient requests to the team … you never transfer them [patient requests] to a specific physician.

Employees and management at clinic A emphasised the benefits of working with patient requests through the platform, including flexible working arrangements, reduced stress, and improved work-life balance compared to phone triaging. Asynchronous communication allowed for more flexibility, and some nurses found it a welcome relief compared to the demanding nature of phone triaging, where immediate responses were needed. One nurse at clinic A explained:

It [the digital patient management platform] is still more flexible. If you work with patient requests through the platform for a day , you are more flexible. You can take a break and do something else … you do not have to stick to exact times. That is why it has been quite easy to work from home … it can be difficult to sit and make phone calls at home with sick children , but working with the platform has worked quite well , better in any case , just because you can still text someone on the side.

Similarly, another nurse colleague highlighted the differences between phone triage and the platform:

I think that the phone is more demanding … you need to be slightly more engaged. The patient notices if you are not really listening to the questions you’re getting and so on. In the chat, you can express yourself, however you want. For better or worse, you don’t convey emotions in the same way in the chat … in the chat, you can read a question and then think for a while before giving an answer. You can consult colleagues more easily in a different way. On the phone, you get a question. And then you need to listen and respond immediately … it requires slightly more from you, so to speak.

However, some nurses at clinic A found working with patient requests through the platform to be stressful because there was no limit to the number of requests that could pile up, unlike phone triage, where the number of requests was limited to the time slots of that day. On the other hand, physicians at clinic A found that working through the platform reduced interruptions, as nurses could communicate with them through the platform, rather than interrupting their work with in-person visits.

At the end of the three-year study, the digital patient management platform became an integral part of the daily work of nurses, psychologists, and physicians (see Table  3 ). They scheduled dedicated time to work on the platform and felt that it supported their independence, and better planning of their work. The platform was experienced as less stressful and provided opportunities for recovery and flexible working arrangements. Healthcare professionals actively initiated contact with patients by using the platform for communication, sharing forms, test results, and follow-up appointments.

However, the clinic’s vision of reducing nurses’ workloads and increasing their availability to patients faces challenges. The significant increase in patient inquiries and demands during the pandemic and postpandemic periods added to employee workloads and pressure. Asynchronous communication on the digital platform, involving ongoing patient requests that could span hours or even days, presented unique challenges for nurses who had to manage multiple patient requests concurrently. This contrasted with phone triage, where patient inquiries and requests were typically resolved within minutes. Additionally, the clinic experienced high employee turnover, further straining the remaining staff. Nevertheless, healthcare professionals and management actively engaged in institutional work to advocate for the platform’s benefits, reshaping primary care work at the clinic.

Primary healthcare clinic b

Clinic B, a small-sized clinic with approximately 7500 listed patients and 15 employees, implemented the digital patient management platform in 2021. The decision was made by a private healthcare provider overseeing multiple clinics in the southern region of Sweden, without involvement from local managers. The main objectives the private healthcare provider was to reduce employee workload and enhance patient accessibility. However, the nurses at clinic B were initially skeptical about the necessity of the platform. They raised concerns that increased availability might prioritise younger patients with less serious conditions over older adults with more pressing health needs.

Despite their reservations, some acknowledged the potential benefits of digitalisation in terms of improving patient accessibility. However, in their daily practice, they did not perceive a need for change and thus did not actively advocate for the platform to patients or colleagues. They considered working with the platform an additional burden but believed it could benefit patients by providing an additional way to contact primary healthcare. One physician at clinic B stated:

High availability for patients, of course. It is fast and smooth for them. The drawbacks are that as physicians, we don’t have time … it becomes too much for us because we have to log in to three, four, five different places. In addition to 1177 [the national healthcare platform], the digital patient management platform and the electronic healthcare records, there are also other things we have to do … it just becomes a lot of work … that is time-consuming. I really wish this [all digital systems] could be narrowed down somehow. But for patients, it’s great.

To implement the platform, the private healthcare provider offered online training sessions. The large-scale implementation of the platform involved providing online training sessions to all primary healthcare clinic employees in southern Sweden, which were conducted by the eHealth service provider and a centrally appointed implementation manager hired by the private healthcare provider. During these training sessions, the eHealth service provider emphasised the importance of change management, using Kotter’s approach, as seen in clinic A. Clinic B’s healthcare professionals received this online training and followed Kotter’s change management model, which included the appointment of a team of super users, although the team’s effectiveness was limited due to busy schedules, staff turnover, the pandemic, and other challenges.

Initially, one nurse at clinic B started handling patient requests in the morning, switching between the digital patient management platform and phone triaging. However, nurses found this approach inefficient due to the lack of real-time feedback on the platform, which led to interruptions from phone triaging. This required them to repeatedly revisit earlier communications with patients on the platform, making the process disjointed and time-consuming. When patients responded promptly and the requests were straightforward, the platform’s efficiency was comparable to that of phone triage. Physicians at clinic B experienced similar challenges, with patient requests routed to their designated physician and a 15-minute time slot allocated for handling platform requests. However, the physicians noted the inconsistency of patient requests and the difficulty of accommodating patients within their busy schedules. This resulted in limited time for each task, particularly when requests required clarification or when patients did not respond immediately. One physician explained:

Honestly, I don’t like the digital patient platform. And it is because, firstly, there is no designated time for it. So, no matter how many cases there are, I do not have time for each one. That is how it is. Secondly, some cases are for prescription renewal, and then it is manageable, but sometimes the patient wants to talk to a physician, and I have not designated time for that, so it becomes very difficult for me to find time in the schedule when it is already full [the schedule]. And the problem is that it becomes tedious because it takes time to write to the patient, get a response, and then it is also time-consuming because patient requests that initially seem straightforward become more and more complex as patients bring up more and more details, and it becomes a bit harder to limit the patient when it is in text form or chat. In the end, a physical visit may have been preferred as I may solve the problem in 15 min during a physical visit.

Another physician highlighted:

I send letters, make phone calls. Occasionally, I sent a text message via the electronic healthcare records but not through the actual digital patient management platform. Because if I have to go into the digital patient management platform, it’s extra steps. Then I have to go into the link, and open it … We have the electronic healthcare record, Pascal [the medication record], and NPÖ [national patient overview] … we have a lot of other things, and then I feel like … I don’t want to go into the digital patient management platform and respond … It’s an extra step to go into another system and then document … if I can call the patient and write notes in the electronic healthcare record at the same time, I might finish in half the time.

As the quotes above illustrate, employees at clinic B approached the platform reluctantly, viewing it as an additional system to manage. During busy hours, they felt it added to their workload. While they found the platform manageable for ‛easy’ tasks, such as prescription renewals, they did not consider it their preferred solution. They believed that the platform did not enhance the quality of their work environment. As one nurse at clinic B expressed:

It doesn’t help me in my profession , and it doesn’t help me provide better care for the patients.

The internal chat features introduced during training were not widely used, with colleagues opting for oral communication to resolve issues. Video-conferencing with patients was not perceived as beneficial and was seldom utilised. Most nurses resorted to a copy-and-paste method for record-keeping in the electronic healthcare records due to time constraints. Some nurses were uncertain about the preferred method, leading them to both copy and paste information and write summary assessments. One nurse at clinic B explained:

First you have to copy the text they [the patients] have written [in the digital patient management platform], and there’s a lot of clicking and pasting and clicking and pasting [into the electronic healthcare record], and then you have to write your own assessment too … it is cumbersome.

A physician at clinic B noted:

They [the nurses] paste everything in … when it’s just pasted in like that, it’s difficult to read and understand what it is really about and what the problem or question is.

By the end of the study, only a few patients were using the platform (see Table  3 ). Employees felt that patient adoption seemed to depend on phone availability, with more platform use occurring when phone availability was limited. Employees did not initiate patient contact through the platform but rather responded to patient requests that came in via the platform. They explained that management directives were to meet patients through their chosen communication channel. If a patient called, the clinic handled the request over the phone; if a patient used the platform, the request was addressed on the platform.

Primary healthcare clinic C

Clinic C, a small primary healthcare clinic serving approximately 6500 registered patients and staffed by 15 employees, followed a similar path to clinic B in implementing the platform. The decision to deploy the platform at clinic C was made by the same private healthcare provider that oversees clinic B, and it did not involve direct input from local management or employees. The primary reason behind this decision was to reduce workloads and improve patient accessibility. However, the nurses at clinic C did not agree and noted that they already had a high level of availability and that patients had not expressed a desire for digital care or increased accessibility.

In clinic C, healthcare professionals engaged in a series of online training sessions and adhered to Kotter’s change management model, which entailed the appointment of a team of super users as seen in clinic B. This group comprised the head manager, a nurse, and a physician. However, due to their demanding schedules, which were further heightened by factors such as staff turnover and the challenges stemming from the pandemic, the team of super users did not take a proactive role in driving the implementation process. The manager was preoccupied with overseeing the clinic, leading to the delegation of power of the implementation of the digital patient management platform to one of the nurses. Despite mandatory online training, employees lacked interest in the platform and participated in the online training sessions. As a result, the clinic initially decided to restrict platform usage to only nurses conducting phone triaging. The lack of engagement was narrated by all the healthcare professionals at the clinic, including the nurses. As one of the nurses explained,

It [the implementation of the digital patient management platform] wasn’t really our decision [the clinic]; it came from higher up. We were controlled from above , so to speak. They wanted us to implement it .

The nurses worked with the platform in parallel to working on the phone. During the study, the physicians became slightly more involved and used the platform, and hence only had a few requests during the three-year study period. The platform was not discussed in great detail at clinic C. During the introduction, the employees felt that it caused increased stress. However, there was a consensus among the healthcare professionals that the digital patient management platform itself was not the primary source of dissatisfaction. Instead, they recognised that the negative impact on their working environment was largely influenced by organisational factors, such as the heightened stress caused by increased pressure from phone triaging. This meant that nurses had to juggle both answering phone calls and attending to requests through the platform, while physicians had to balance their regular tasks and the additional demands from the platform.

The adoption of the digital patient management platform was somewhat impeded at clinic C by their interpretations of the Swedish national healthcare guarantee, which states that everyone who calls should be able to reach their healthcare clinic for advice and/or booking an appointment on the same day [ 35 ]. This is evaluated based on the proportion of patients who can make contact with their healthcare clinic on the same day during a measurement period. Clinic C interpreted the guarantee to be based solely on telephone contact, leading them to prioritise high accessibility via phone over rapid responses to digital contacts through the digital patient management platform. By the end of the study, clinic C had few patients who used the digital patient management platform (see Table  3 ). According to the employees, the platform did not significantly impact their work or working environment.

A quote from the head manager, who was also initially part of the team of super users, nicely summarises the platform’s implementation at clinic C by the end of the study. The head manager explained:

Well , I must say that the implementation has been smooth and trouble-free , largely due to our excellent telephone accessibility. Telephone availability here is consistently at 98% and reaching us has always been easy. Consequently , there has not been much need for people to contact us through the digital patient management platform. Personally , I haven’t had to be involved in the implementation because it hasn’t been necessary.

As seen in the three cases presented, the practicalities surrounding the implementation of the same digital patient management platform in three primary healthcare clinics present different opportunities and challenges. By reflecting on these practicalities, we can gain insights into aspects that contribute to the successful integration and utilisation of such platforms. These practicalities concern a number of dimensions in which the cases differ, i.e., coherence, decision-making approaches, collaboration with eHealth service providers, training, patient onboarding, organisation of work, and distribution of patients. An overview of these dimensions can be found in Table  4 . We will discuss them in the following sections.

Coherent versus non-coherent objectives

While all three primary healthcare clinics had similar objectives for implementing the patient management platform, only clinic A’s employees and management had a shared understanding of its necessity. In contrast, employees at clinics B and C questioned the objectives set by top management, as they did not find them meaningful and were unsure about how a new digital approach would benefit their work and professional identity. Van den Heuvel et al. [ 36 ] highlight the importance of meaning making and change information for employee’s adaptive behaviour in relation to organisational change. This indicates that to be able to act adaptively in relation to a change, one must also be able to make meaning , i.e., make sense of the change from a personal point of view, which did not occur in cases B and C. As pointed out by Rydenfält, Persson, Larsson, Johansson and Erlingsdóttir [ 37 ], a change that makes sense on one level might not do so on the local level where implementation occurs. This could be a problem related to framing, or it could be due to an actual lack of usefulness (or fit) of the change in relation to the organisation’s tasks. Framing is crucial for sensemaking, as sense is always made in relation to some kind of context or frame. As noted by Orlikowski and Gash [ 38 ], when the framing concerning a particular technology is incongruent, conflicts concerning implementation and use are likely to occur. In relation to the prevailing sociotechnical digital care imaginary, the results in cases B and C describe an incongruent framing [ 2 ].

Top-down versus bottom-up decision-making approaches

A significant contrast between clinic A and clinics B and C was the origin of the decision to implement the platform. In clinic A, the initiative stemmed from the bottom-up approach, driven by nurses expressing their concerns about the stressful nature of phone triaging. The management, in collaboration with the employees, actively sought solutions to address the issue, leading them to pilot the digital patient management platform. After experiencing its benefits, they made the decision to integrate it permanently into their workflow. In contrast, in clinics B and C, the implementation of the platform was mandated by the central healthcare provider without the involvement of local primary healthcare clinics and employees. This top-down approach diminishes the participation and influence of employees in decision-making processes. According to past research, such top-down implementation approaches can lead to challenges and limited acceptance of new technologies within organisations [ 39 , 40 , 41 ], which was observed in clinics B and C.

Despite vastly different levels of success in implementing the digital platform, all clinics are illustrative examples of what, according to sociotechnical theory, is referred to as responsible autonomy [ 42 , 43 ]. In practice, responsible autonomy refers to local leadership and adaptability at the group level, rather than work being organised from above by those not involved or related to the actual work situation and local context. The principle implies that it is better to have a simple organisation for complex tasks than the other way around. Here, a complex organisation would mean that the organisation has many levels and that decisions are made far away from the sharp end where the work is done. In clinic A, it is clear that responsible autonomy was a ruling principle as the change was locally driven. In clinics B and C, there was also signs of responsible autonomy. However, in their cases, it resulted in them (locally) choosing not to use the platform as they felt that it did not make sense given their framing of their work situation.

Close collaboration with the eHealth services provider versus no collaboration

Furthermore, unlike in clinic A, where there was close collaboration with the eHealth service provider, clinics B and C lacked any contact or regular discussions with the eHealth service provider. This resulted in the absence of feedback loops and limited opportunities for employees to influence the functionalities and logics of the digital patient management platform. As a result, this led to a reduced sense of ownership among employees and a lack of perceived alignment between digital patient management and the specific needs and practices of primary healthcare clinics B and C. The findings indicate that active participation and meaningful engagement of employees play a paramount role in adoption of digital patient management. It emphasises the sociotechnical principle that technology implementation is not only about the technical aspects but also about how it aligns with the social context. This may be even more so in Nordic countries, since employees have an expectation to have their say in decisions that affect them [ 44 ].

Onsite hands-on training versus mass online training

The training sessions in clinic A differed significantly from those in clinics B and C. In clinic A, the training was conducted onsite in the presence of the eHealth service provider, allowing for direct interaction and personalised guidance. On the other hand, in clinics B and C, the training sessions were conducted online, involving employees from multiple primary healthcare clinics owned by the same private healthcare provider. This approach, while potentially cost-effective, was shown to have negative implications for employees’ engagement and interest. Scmidt & Houst (2000) shed light on this phenomenon, demonstrating that small group discussions stimulate cognitive processes and positively influence individuals’ motivation to learn [ 45 ].

One can speculate that onsite small discussion groups could have been extremely valuable at clinics B and C, where the objectives set by top management were not agreed upon by the employees. During their mass online sessions, some employees were digitally present but not actively engaged. If they were compelled to actively learn how to use the platform through the social pressures of smaller groups, they might have had a chance to better understand the workings and benefits of digital patient management, making them more willing to utilise it. The situation at clinics B and C can be interpreted as a disconnect between the social and technical components of the system. The objectives set by top management did not align with the employees’ understanding, resulting in a lack of coherence in the sociotechnical system. The employees’ lack of engagement with the digital patient management platform during massive online sessions underscores the need for improved integration of social and technical elements. Using small onsite discussion groups may have addressed this imbalance. By creating smaller, more socially-oriented learning environments, they could have better integrated the social and technical components of the system.

Active patient onboarding versus passive patient onboarding

Effective patient onboarding is contingent upon patient engagement and adoption of digital triaging. Without patient utilisation of the platform, its impact on the working environment remains limited. In clinic A, proactive measures were taken to steer patients towards using the platform, and healthcare professionals even initiated patient contact through the platform. However, such proactive patient engagement was not observed in clinics B and C. The volume of patients utilising the digital platform also plays a crucial role in the reorganisation of work routines to accommodate new digital practices, as seen in clinic A. The result indicates that the greater the patient volume is, the greater the potential for the integration of new digital routines within the primary healthcare clinic. From the lens of sociotechnical systems theory, this highlights the interplay between social factors (patient volume) and technical aspects (digital routines) and their impact on utilisation.

Standardised routines versus ad-hoc approach

Another factor impacting the utilisation of the digital patient management platform was the ad-hoc approach taken by clinics B and C compared to the more standardised routines in clinic A. In clinics B and C, interviews revealed that healthcare professionals responded to patients using both phones and the platform, despite patients initiating digital contact. Additionally, nurses inconsistently copied the entire chat dialogue into the national healthcare records, while sometimes opting for a summary of their assessment. Some nurses regularly worked on the platform when time allowed, even though they were not scheduled to do so, while others did not. This lack of consensus on how to work with the platform resulted in different individuals interpreting and implementing their own working routines. In contrast, in clinic A, the management and healthcare professionals adhered to standardised routines.

Team of physicians versus designated physician

One change made by clinic A to integrate digital patient management into its organisational context was the creation of a team of physicians and nurses dedicated to handling patient inquiries through the digital patient management platform, rather than leaving this responsibility to patients’ designated physicians. Due to the demanding schedules of physicians and the nature of asynchronous communication, this shift seems to be necessary. However, this raises important questions about the balance between continuity of care and accessibility. The results indicate that providing online access to care requires breaking the continuity of care with the patient’s designated physician. For simple tasks such as prescription renewal and assessing sores or rashes from photos, this was not a significant issue in terms of quality and efficiency of care. However, for more complex cases, the quality and efficiency of care may suffer without the continuity provided by the patient’s designated physician. One potential hypothesis drawn from these cases is that uncomplicated patient requests are well-suited for digital handling, while complex patient requests often require physical examination and continuity of care with the patient’s regular physician. Eriksson and colleagues conducted both an interview and focus group study and found that although digital triage platforms led to efficiency gains for patients with simple cases, the low uptake of the technology meant that it did not replace existing functions and routines, but rather added to them, resulting in a negative impact on overall efficiency [ 46 ]. Their study suggested that digital triaging might not contribute to quality improvements and, instead, poses a risk to quality gains. In primary healthcare clinic A, the digital patient management platform was perceived to improve efficiency when accessibility was prioritised over continuity of care (a team of physicians attending patients’ requests instead of their ‛designated’ primary physician). However, this approach may come at a cost to quality gains, as continuity of care is considered to be essential in reducing the risk of patients receiving incorrect diagnoses and treatments [ 47 ].

Moreover, it is worth noting a study conducted in 2003 regarding the implementation of a triage-based email system in primary healthcare [ 48 ]. The study revealed that email triage did not replace phone communication and despite the increased utilisation of emails, it did not enhance the efficiency of primary care, as the phone volume remained unchanged. Interestingly, even though clinic A reported improved efficiency resulting from the digital triage platform, they continued to experience the same volume of phone calls as before its implementation.

Strengths and limitations

One strength of this study is its exploration of three different clinics implementing the same patient management digital platform over three years. This multi-case approach enables comparisons and cross-case analysis, which is not very common in the field. The longitudinal nature of the study allowed us to explore the implementation as a process over time rather than at a single point in time. A bottom-up decision-making process involving employees in decisions about work related changes and digitalisation appeared to augment the usage and utilisation of the digital patient management platform. A noteworthy finding was the lasting impact of the first impressions: employees’ initial willingness to adopt and use the platform persisted throughout the study at clinic A, while initial negativity endured and spread to new employees at clinics B and C. Another interesting observation was the substantial influence physicians had on the implementation and utilisation of the platform. At clinic A, one physician’s enthusiasm positively influenced both physicians and nurses at the clinic, whereas hesitation among physicians at clinics B and C seemed to affect the platform’s overall legitimacy. Further research could explore the power balance and the impact that physicians and other professional groups have on the digitalisation of primary care.

This study has several limitations. First, qualitative methods such as interviews and observations depend on interactions between interviewees and the researchers, which can affect the findings. Second, despite inviting all employees from the three clinics, we did not achieve a homogeneous sample representative of the clinics’ workforce, potentially limiting the generalisability to all healthcare professionals working with digital patient management. Third, the study spanned over prepandemic, pandemic and postpandemic periods, with Sweden’s unique pandemic response potentially affecting generalisability [ 32 ]. Future research could conduct cross-cultural studies to explore the influence of cultural and contextual factors on the implementation and utilisation of digital patient management. Fourth, focusing on a single digital patient management platform means that user experiences are mediated by this specific platform’s materiality and different platforms might yield different results. Comparative studies of different digital patient management platforms would also be valuable to identify how different platforms influence user experiences and outcomes. However, concentrating on one specific platform, as done in this study, allows for deeper analysis of the findings compared to a more scattered area of study including several platforms. Fifth, the focus on healthcare professionals’ working environment provides a one-sided view, excluding the patient perspective. Future research could incorporate the patient perspective to provide a more holistic understanding of the impact of digital patient management platforms on accessibility and continuity of care. Finally, while our research team comprised researchers from diverse academic fields, including psychology, organisational studies, informatics and human-computer interaction, which strengthened the analysis and flexibility of interpretation, we did not explore quantitative perspectives such as budget and cost efficiency. Future research may benefit from using mixed methods to integrate both qualitative and quantitative methods, providing an even more comprehensive understanding of digital patient management.

Viewed through the lens of sociotechnical systems theory, achieving harmonious interaction between social and technical components is essential for the successful utilisation of digital patient management. In this context, management can be considered a technical component, given its role in orchestrating and overseeing the structural and operational aspects of the organisation, while healthcare professionals and patients are integral social components within the social fabric of primary healthcare clinics.

Notably, the management and employees at clinic A successfully converged on the platform’s purpose and aligned it with their workflows and logics of care. Clinic A’s approach actively involved employees in the implementation, which enhanced the integration of technical and social elements. Close collaboration with the eHealth service provider allowed for customisation and feedback, ultimately improving alignment with employee needs. The use of interactive, onsite training ensured effective onboarding and utilisation of the digital patient management platform. Clinic A’s proactive patient onboarding and high patient volume demonstrated the crucial role of patient involvement in digital patient management utilisation. However, it is worth noting that an overemphasis on digital patient management may have negative implications for the quality of care in complex cases. Overall, clinic A’s approach empowered the social components, i.e., their employees, resulting in greater utilisation of the platform compared to clinics B and C.

In contrast, clinics B and C lacked coherence. Employees in these clinics questioned the objectives and struggled with the utilisation of the digital patient management platform. The management’s top-down approach disrupted the sociotechnical balance, diminishing employee involvement and hindering platform utilisation. The lack of collaboration with the eHealth service provider seemed to further hinder alignment with clinic-specific needs. The use of mass online training and the absence of proactive patient onboarding limited the platform’s impact on care work. Inconsistencies in working with the platform created confusion and additional work. In summary, the findings indicate that the implementation approaches in Clinics B and C may have disrupted the sociotechnical equilibrium by reducing the role of social components, particularly employees, which consequently restricted the utilisation of the digital patient management platform.

Data availability

The datasets generated and analysed during the current study are not publicly available due to that the participants in this study did not giving written consent for their data to be shared publicly. However, some of the data are available from the corresponding author ([email protected]) on reasonable request and with permission from the Swedish Ethical Review Authority.

Babaei N, Zamanzadeh V, Valizadeh L, Lotfi M, Samad-Soltani T, Kousha A, Avazeh M. A scoping review of virtual care in the health system: infrastructures, barriers, and facilitators. Home Health Care Serv Q. 2023;42(2):69–97.

Article   PubMed   Google Scholar  

Jasanoff S, Kim S-H. Dreamscapes of modernity. Sociotechnical Imaginaries Fabrication Power Chic, 2015.

Villumsen S, Faxvaag A, Nøhr C. Development and progression in Danish eHealth policies: towards evidence-based policy making. MEDINFO 2019: Health and Wellbeing e-Networks for all. IOS; 2019. pp. 1075–9.

Wadhwa M. National eHealth Authority (NeHA), 2020.

Warth LL, Dyb K. eHealth initiatives; the relationship between project work and institutional practice. BMC Health Serv Res. 2019;19(1):1–12.

Article   Google Scholar  

Andargoli AE. e-Health in Australia: a synthesis of thirty years of e-Health initiatives. Telematics Inform. 2021;56:101478.

Socialdepartementet. Nationell IT-strategi för vård och omsorg [National IT strategy for health- and social care], Socialdepartementet, ed., 2006.

Socialdepartementet. Nationell eHälsa – strategin för tillgänglig och säker information inom vård och omsorg, Socialdepartementet, ed., 2010.

o. SK, Socialdepartementet L. Vision e-hälsa 2025 – gemensamma utgångspunkter för digitalisering i socialtjänst och hälso- och sjukvård, Socialdepartementet, ed., 2016.

Hellberg S, Johansson P. eHealth strategies and platforms–the issue of health equity in Sweden. Health Policy Technol. 2017;6(1):26–32.

Tortorella GL, Fogliatto FS, Espôsto KF, Vergara AMC, Vassolo R, Mendoza DT, Narayanamurthy G. Effects of contingencies on healthcare 4.0 technologies adoption and barriers in emerging economies. Technol Forecast Soc Chang. 2020;156:120048.

Chandra M, Kumar K, Thakur P, Chattopadhyaya S, Alam F, Kumar S. Digital technologies, healthcare and COVID-19: insights from developing and emerging nations. Health Technol. 2022;12(2):547–68.

Gottliebsen K, Petersson G. Limited evidence of benefits of patient operated intelligent primary care triage tools: findings of a literature review. BMJ Health care Inf, 27, 1, 2020.

Rodgers M, Raine GA, Thomas S, Harden M, Eastwood AJ. Informing NHS policy in’digital-first primary care’: a rapid evidence synthesis. Health Serv Delivery Res, pp. 1–154, 2019.

Kaminsky E, Röing M, Björkman A, Holmström IK. Telephone nursing in Sweden: a narrative literature review. Nurs Health Sci. 2017;19(3):278–86.

Frennert S, Erlingsdóttir G, Muhic M, Rydenfält C, Milos Nymberg V, Ekman B. It increases my ability to influence my ways of working’: a qualitative study on digitally mediated patient management in primary healthcare. Scand J Caring Sci. 2023;37(1):88–105.

Salisbury C, Murphy M, Duncan P. The impact of digital-first consultations on workload in general practice: modeling study. J Med Internet Res. 2020;22(6):e18203.

Article   PubMed   PubMed Central   Google Scholar  

Cajander Å, Larusdottir M, Hedström G. The effects of automation of a patient-centric service in primary care on the work engagement and exhaustion of nurses. Qual User Experience. 2020;5:1–13.

Bijker WE, Law J. Shaping technology/building society: studies in sociotechnical change. MIT Press; 1994.

Berg M, Aarts J, van der Lei J. ICT in health care: sociotechnical approaches. Methods Inf Med. 2003;42(04):297–301.

Article   CAS   PubMed   Google Scholar  

Silverstone R. 12 domesticating domestication. Reflections on the life of. Domestication Media Technol, pp. 229, 2005.

Timmermans S, Berg M. The practice of medical technology. Sociol Health Illn. 2003;25(3):97–114.

Eldh AC, Sverker A, Bendtsen P, Nilsson E. Health Care professionals’ experience of a Digital Tool for Patient Exchange, Anamnesis, and Triage in Primary Care: qualitative study. JMIR Hum Factors. 2020;7(4):e21698.

Huddlestone L, Turner J, Eborall H, Hudson N, Davies M, Martin G. Application of normalisation process theory in understanding implementation processes in primary care settings in the UK: a systematic review. BMC Fam Pract. 2020;21:1–16.

Ziebart C, Kfrerer ML, Stanley M, Austin LC. A Digital-First Health Care Approach to managing pandemics: scoping review of pandemic self-triage tools. J Med Internet Res. 2023;25:e40983.

Baines R, Tredinnick-Rowe J, Jones R, Chatterjee A. Barriers and enablers in implementing electronic consultations in primary care: scoping review. J Med Internet Res. 2020;22(11):e19375.

Radionova N, Ög E, Wetzel A-J, Rieger MA, Preiser C. Impacts of Symptom checkers for laypersons’ self-diagnosis on Physicians in Primary Care: scoping review. J Med Internet Res. 2023;25:e39219.

Yin RK. Case study research: design and methods. 5 ed. Thousand Oaks, Calif: Sage; 2014.

Google Scholar  

Frennert S, Erlingsdóttir G, Muhic M, Rydenfält C, Nymberg VM, Ekman B. ‘It increases my ability to influence my ways of working’: a qualitative study on digitally mediated patient management in primary healthcare. Scand J Caring Sci, 2022.

Frennert S, Erlingsdóttir G, Muhic M, Rydenfält C, Nymberg VM, Ekman B. Embedding and integrating a digital patient management platform into Everyday Primary Care routines: qualitative case study. JMIR Formative Res. 2022;6(2):e30527.

Frennert S, Petersson L, Muhic M, Rydenfält C, Nymberg VM, Ekman B, Erlingsdottir G. Materiality and the mediating roles of eHealth: a qualitative study and comparison of three cases. Digit Health. 2022;8:20552076221116782.

PubMed   PubMed Central   Google Scholar  

Ludvigsson JF. How Sweden approached the COVID-19 pandemic: Summary and commentary on the National Commission Inquiry. Acta Paediatr. 2023;112(1):19–33.

Crist JD, Tanner CA. Interpretation/analysis methods in hermeneutic interpretive phenomenology. Nurs Res. 2003;52(3):202–5.

Kotter JP. Force for change: how leadership differs from management. Simon and Schuster; 2008.

Swedish Municipalities S. and Regions. Accessibility in primary care, 2023-02-16, 2023; https://skr.se/vantetiderivarden/vantetidsstatistik/vantetidsstatistikforprimarvard/telefontillganglighet.54387.html#:~:text=Telefontillg%C3%A4nglighet%20till%20prim%C3%A4rv%C3%A5rd,dag%20enligt%20den%20nationella%20v%C3%A5rdgarantin .

van den Heuvel M, Demerouti E, Bakker AB, Schaufeli WB. Adapting to change: the value of change information and meaning-making. J Vocat Behav. 2013;83(1):11–21.

Rydenfält C, Persson J, Larsson R, Johansson G, Erlingsdóttir G. Inter-Organizational Home Care Nursing Teams: A Comparison of a Region Wide Organizational Change Initiative With Success Factors Identified by Forerunners and Team Theory, Home Health Care Management & Practice , pp. 10848223231209926, 2023.

Orlikowski WJ, Gash DC. Technological frames: making sense of information technology in organizations. ACM Trans Inform Syst (TOIS). 1994;12(2):174–207.

Oakland JS, Tanner S. Successful change management, Total quality management & business excellence , vol. 18, no. 1–2, pp. 1–19, 2007.

Braithwaite J, Glasziou P, Westbrook J. The three numbers you need to know about healthcare: the 60-30-10 challenge. BMC Med. 2020;18:1–8.

Greenhalgh T, Papoutsi C. Studying complexity in health services research: desperately seeking an overdue paradigm shift. Springer; 2018. pp. 1–6.

Trist EL, Bamforth KW. Some social and psychological consequences of the longwall method of coal-getting: an examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Hum Relat. 1951;4(1):3–38.

Walker GH, Stanton NA, Salmon PM, Jenkins DP. A review of sociotechnical systems theory: a classic concept for new command and control paradigms, Theoretical issues in ergonomics science, 9, 6, pp. 479–99, 2008.

Nielsen P, Nielsen RN, Bamberger SG, Stamhus J, Fonager K, Larsen A, Vinding AL, Ryom PK, Omland Ø. Capabilities for innovation: the nordic model and employee participation. Nordic J Working life Stud. 2012;2(4):85–115.

Schmidt HG, Moust JH. Factors affecting small-group tutorial learning: A review of research, Problem-based learning: A research perspective on learning interactions , pp. 19–52, 2000.

Eriksson P, Hammar T, Lagrosen S, Nilsson E. Digital consultation in primary healthcare: the effects on access, efficiency and patient safety based on provider experience; a qualitative study. Scand J Prim Health Care, pp. 1–9, 2022.

Fernholm R, Holzmann MJ, Malm-Willadsen K, Härenstam KP, Carlsson AC, Nilsson GH, Wachtler C. Patient and provider perspectives on reducing risk of harm in primary health care: a qualitative questionnaire study in Sweden. Scand J Prim Health Care. 2020;38(1):66–74.

Katz SJ, Moyer CA, Cox DT, Stern DT. Effect of a triage-based e‐mail system on clinic resource use and patient and physician satisfaction in primary care: a randomized controlled trial. J Gen Intern Med. 2003;18(9):736–44.

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Acknowledgements

The authors would like to thank the participants who contributed to this study with their experiences.

The funders of this study are AFA insurance. The funders of the eHealth platform were not involved in any aspect of the study’s design, collection, or analysis; interpretation of data; or writing or publication processes.

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Applications for funding were put in place by GE. SF conducted the second and third rounds of interviews, performed analysis of both datasets, interpreted data and drafted the manuscript. GE and SF contributed to conceptualisation, design, methodology, data analysis, review and editing. MM conducted the first set of interviews and analysed the first dataset. CR supported interpretation of data, methodology, review and editing. All authors approved the final version.

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Frennert, S., Rydenfält, C., Muhic, M. et al. Unveiling the heterogeneous utilisation of the same digital patient management platform: case studies in primary healthcare in Sweden. BMC Health Serv Res 24 , 831 (2024). https://doi.org/10.1186/s12913-024-11287-3

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The purpose of this paper is to examine the implementation of IT portfolio management (IT PoM) and develop a framework guided by adaptive structuration theory to describe the key structures, features, and appropriation steps needed to effectively manage IT investments and assets.

Design/methodology/approach

Using a longitudinal case study approach, data were collected over an eight-month period from a US Fortune 500 company during its IT PoM implementation effort.

The case analysis highlights three major IT PoM features appropriated by the organization: creating the portfolio; assessing and analyzing the portfolio characteristics based on risk, benefits, alignment, criticality, and cost; and balancing decisions to start projects or terminate under-performing IT assets such as servers and applications. The spirit of IT PoM was interpreted differently by different stakeholders (data providers, business units, and IT PoM team) leading to resistance to implementation. The case data underscores the importance of establishing a governance steering committee and new internal structures to help push the balancing decisions across the organization.

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The results are useful in developing guidelines and strategies to achieve successful implementation of IT PoM and to highlight critical factors that practitioners need to pay close attention to during an IT PoM implementation.

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This study represents one of the first attempts to describe a detailed IT PoM implementation process and how IT PoM appropriation process can lead to improved decision making within the organization.

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Ajjan, H. , Kumar, R.L. and Subramaniam, C. (2016), "Information technology portfolio management implementation: a case study", Journal of Enterprise Information Management , Vol. 29 No. 6, pp. 841-859. https://doi.org/10.1108/JEIM-07-2015-0065

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Implementation of new practices in team-based settings requires teams to work together to respond to new demands and changing expectations. However, team constructs and team-based implementation approaches have received little attention in the implementation science literature. This systematic review summarizes empirical research examining associations between teamwork and implementation outcomes when evidence-based practices and other innovations are implemented in healthcare and human service settings.

We searched MEDLINE, CINAHL, APA PsycINFO and ERIC for peer-reviewed empirical articles published from January 2000 to March 2022. Additional articles were identified by searches of reference lists and a cited reference search for included articles (completed in February 2023). We selected studies using quantitative, qualitative, or mixed methods to examine associations between team constructs and implementation outcomes in healthcare and human service settings. We used the Mixed Methods Appraisal Tool to assess methodological quality/risk of bias and conducted a narrative synthesis of included studies. GRADE and GRADE-CERQual were used to assess the strength of the body of evidence.

Searches identified 10,489 results. After review, 58 articles representing 55 studies were included. Relevant studies increased over time; 71% of articles were published after 2016. We were unable to generate estimates of effects for any quantitative associations because of very limited overlap in the reported associations between team variables and implementation outcomes. Qualitative findings with high confidence were: 1) Staffing shortages and turnover hinder implementation; 2) Adaptive team functioning (i.e., positive affective states, effective behavior processes, shared cognitive states) facilitates implementation and is associated with better implementation outcomes; Problems in team functioning (i.e., negative affective states, problematic behavioral processes, lack of shared cognitive states) act as barriers to implementation and are associated with poor implementation outcomes; and 3) Open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier.

Conclusions

Teamwork matters for implementation. However, both team constructs and implementation outcomes were often poorly specified, and there was little overlap of team constructs and implementation outcomes studied in quantitative studies. Greater specificity and rigor are needed to understand how teamwork influences implementation processes and outcomes. We provide recommendations for improving the conceptualization, description, assessment, analysis, and interpretation of research on teams implementing innovations.

Trial registration

This systematic review was registered in PROSPERO, the international prospective register of systematic reviews. Registration number: CRD42020220168.

Peer Review reports

Contributions to the Literature:

This paper reviews more than 20 years of research on teams and implementation of new practices in healthcare and human service settings.

We concluded with high confidence that adaptive team functioning is associated with better implementation outcomes and problems in team functioning are associated with poorer implementation outcomes. While not surprising, the implementation science literature has lacked clear empirical evidence for this finding.

Use of the provided recommendations will improve the quality of future research on teams and implementation of evidence-based practices.

Healthcare and human service providers (e.g., clinicians, case managers) often work in team-based settings where professionals work collaboratively with one another and service recipients toward shared goals [ 1 , 2 ]. Team-based care is intended to include multiple professionals with varying skills and expertise [ 1 , 3 ]. It requires shared responsibility for outcomes and increases team members’ dependence on one another to complete work [ 1 , 3 , 4 ]. Effective team-based care and higher quality teamwork are associated with improvements in care access and quality, patient safety, patient satisfaction, clinical outcomes, and costs [ 2 , 4 , 5 , 6 , 7 , 8 , 9 ].

We use the term ‘teamwork’ to refer to an array of team constructs using the input-mediator-outcome-input (IMOI) framework (Fig.  1 ) [ 10 , 11 , 12 ]. The IMOI framework recognizes that team interactions are dynamic and complex, with processes unfolding over time and feedback loops between processes, outcomes, and inputs [ 10 ]. Team inputs include team structure and composition, task demands, and contextual features [ 13 ]. Mediators are aspects of team functioning (i.e., what team members think, feel, and do [ 12 ]) through which inputs influence outcomes. These processes and emergent states may be cognitive, affective, or behavioral [ 5 , 14 , 15 , 16 ]. Team effectiveness outcomes are multidimensional and include team performance as well as team viability and the impact of the team on members’ development [ 12 , 17 , 18 , 19 ].

Conceptual model of team effectiveness and key terminology. Figure adapted from “Advancing research on teams and team effectiveness in implementation science: An application of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework” by E.A. McGuier, D.J. Kolko, N.A. Stadnick, L. Brookman-Frazee, C.B. Wolk, C.T. Yuan, C.S. Burke, & G.A. Aarons, 2023, Implementation Research and Practice , 4 , 26334895231190855. [CC BY-NC]

Implementation of new practices in team-based service settings requires team members to work together to respond to changing demands and expectations. Extensive research has identified barriers and facilitators to implementation of new practices at the individual provider, organization, and system levels; however, the team level has received little empirical attention [ 20 , 21 ]. This is a problem because implementation efforts increasingly rely on teams, and responses to a new practice are likely to be influenced by team characteristics and processes. See McGuier and colleagues [ 20 ] for an overview of team constructs in the context of implementation science and the Exploration, Preparation, Implementation, Sustainment (EPIS) framework [ 22 , 23 ]. Given increasing use of team-based care and interest in implementation strategies targeting teams, examining how teamwork is associated with implementation processes and outcomes is critical. This systematic review identified and summarized empirical research examining associations between teamwork and implementation outcomes when evidence-based practices (EBPs) and other innovations were implemented in healthcare and human service settings.

This systematic review was registered (PROSPERO; registration number: CRD42020220168) and conducted following the published protocol [ 24 ]. The review was conducted in accordance with PRISMA and SWiM guidance [ 25 , 26 ]; relevant checklists are in Additional File 1.

Information sources and search strategy

We searched the following databases: MEDLINE (Ovid), CINAHL (Ebsco), APA PsycINFO (Ovid), and ERIC (Ebsco). Database searches were run on August 7, 2020, and again on March 8, 2022. For all searches, a publication date from 2000 to current was applied; there were no language restrictions (see [ 24 ]). An experienced health sciences librarian (MLK) designed the Ovid MEDLINE search and translated that search for use in the other databases (see additional file in [ 24 ]). The search strings consisted of controlled vocabulary (when available) and natural language terms representing concepts of teamwork and implementation science or innovation or evidence-based practice. Results were downloaded to an EndNote (version X9.3.3) library and duplicate records removed [ 27 ]. Additional relevant articles were identified by hand searches of reference lists of included articles, a cited reference search for included articles in the Web of Science (Clarivate) bibliographic database (completed in February 2023), and requests sent to implementation science listservs and centers for suggestions of relevant articles.

Eligibility criteria

We included empirical journal articles describing studies using quantitative, qualitative, or mixed methods. Study protocols, reviews, and commentaries were excluded. All studies were conducted in healthcare or human service settings (e.g., hospitals, clinics, child welfare) and described the implementation of a practice to improve patient care. Studies of interventions to improve teamwork (e.g., team building interventions) and studies of teams created to implement the innovation (e.g., quality improvement teams, implementation support teams) were excluded. Eligible studies assessed at least one team construct and described its influence on implementation processes and outcomes.

Changes from protocol

Several changes were made from our systematic review protocol (PROSPERO CRD42020220168; [ 24 ]). Specifically, during the full-text review stage, we broadened the scope from team functioning (i.e., processes and states) to include team structure and performance because of the small number of studies that assessed and reported specific processes or states. This change increased the number of included studies. Similarly, because implementation outcomes were often inconsistently defined and poorly reported [ 28 , 29 , 30 ], we broadened our scope to include studies that identified team constructs as implementation determinants (i.e., barriers/facilitators) without explicitly defining and measuring an implementation outcome. Because of changes in university access to bibliographic databases, the cited reference search was performed in the Web of Science only instead of the Web of Science and Scopus. This bibliographic database indexes more than 21,000 scientific journals [ 31 ]. Lastly, because of time and resource constraints, we did not search conference abstracts or contact authors for unreported data.

Selection process and data extraction

Title/abstract screening and review of full-text articles were conducted by pairs of trained independent reviewers in DistillerSR. Conflicts were resolved through re-review, discussion between reviewers, and when needed, discussion with a senior team member (EAM). A final review of all included articles was conducted by EAM. Relevant data from each article was extracted into an Excel spreadsheet by one reviewer (AS). A second reviewer (EAM) conducted a line-by-line review and verification. Our data extraction form was informed by existing forms and guides (e.g., [ 32 , 33 ]). For each included study, we extracted information on measures of teamwork and implementation-relevant outcomes, characteristics of the setting, teams, and participants, analysis methods, and results. For quantitative studies, we recorded correlation coefficients and/or regression coefficients as standardized metrics of association. For qualitative studies, we recorded themes [ 33 ].

Quality and risk of bias assessment

The Mixed Methods Appraisal Tool (MMAT) [ 34 ] was used to evaluate quality and risk of bias for each included study. Multiple publications from the same study were evaluated separately because they reported different outcomes. Consistent with Powell and colleagues [ 35 ], quality evaluations were only made for the components of the study relevant to our question. Quality evaluations were conducted by two independent reviewers (EAM, MAD) with discrepancies resolved through consensus discussion. After completing the MMAT, the reviewers jointly categorized each article as high, moderate, or low quality. High quality studies were those with affirmative responses to all MMAT questions. Moderate quality studies had at least one minor methodological problem, and low-quality studies had serious flaws (e.g., qualitative studies with poor coherence between data, analysis, and conclusions; quantitative studies with biased samples and/or inappropriate statistical analyses).

We rated the relevance of each publication to our research question as high, moderate, or low. Highly relevant studies reported implementation of a well-defined innovation, thoroughly described team constructs and implementation outcomes, and clearly linked team constructs to implementation outcomes. Most studies rated as low relevance provided very limited information about teamwork and/or implementation outcomes. Studies that only described barriers/facilitators were rated as low or moderate relevance. Ratings were conducted by two independent reviewers (EAM, CBW) with discrepancies resolved through consensus discussion.

Data synthesis

We conducted a narrative synthesis of included studies following guidelines for synthesis without meta-analysis (SWiM) [ 36 ]. We prioritized reporting of high quality, highly relevant studies. Studies categorized as low quality and/or low relevance were not included in the synthesis but are included in the description of study characteristics to convey the breadth of the literature. We organized studies based on the IMOI framework (i.e., team inputs, processes/states, and outputs) and organized studies of processes/states by affective, behavioral, and cognitive constructs when possible. Because of the heterogeneity in team constructs and implementation outcomes, we were unable to quantitatively synthesize results using meta-analysis or formally investigate heterogeneity; this challenge is common in implementation science systematic reviews [ 30 ]. We assessed the strength of the overall body of evidence with GRADE for quantitative studies [ 37 ] and GRADE-CERQual for qualitative studies [ 38 , 39 ]. GRADE results in ratings of high, moderate, low, or very low quality of evidence for each outcome of interest. GRADE-CERQual results in ratings of high, moderate, low, or very low confidence in each review finding. GRADE ratings were made independently with discrepancies resolved through consensus discussion; GRADE-CERQual ratings were made through iterative discussions as recommended [ 39 ]. All ratings and decisions were made by the first and senior authors.

Search results

Our initial search, after removal of duplicates, yielded 7181 results. The second search (August 2020-March 2022) captured an additional 1341 results. The cited reference search yielded 1961 results. A total of 10,489 results were included in title/abstract review. Figure  2 provides a PRISMA flow diagram for included studies. After full-text review, 58 articles from 55 studies were included in analyses [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 ].

PRISMA flow diagram of included articles. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71 . For more information, visit: http://www.prisma-statement.org/

As shown in Fig.  3 , publications on teamwork and implementation have increased substantially since 2000. Three articles on this topic (5%) were published between 2000 and 2007, 14 (24%) between 2008 and 2015, and 41 (71%) between 2016 and early 2023.

Included articles by year of publication

Study characteristics

Interrater agreement was good for assessment of study quality (81% agreement on MMAT questions) and ratings of relevance (88% agreement). There were 20 high quality articles, 23 moderate quality articles, and 15 low quality articles. Fourteen articles were rated as high relevance, 22 as moderate, and 22 as low relevance. Only 4 were rated as both high quality and high relevance. We report study characteristics for all 58 eligible articles. Our narrative synthesis includes 32 articles categorized as moderate/high quality and moderate/high relevance; it excludes 26 articles categorized as low quality and/or relevance.

Studies were conducted in inpatient healthcare ( n  = 22), outpatient/ambulatory healthcare ( n  = 21), mental health settings ( n  = 9), and other settings (e.g., residential facilities, multiple settings; n  = 6). There were 33 qualitative, 15 quantitative, and 10 mixed methods studies. All quantitative studies were descriptive observational studies.

Most studies examined team processes/states ( n  = 53); fewer examined team inputs ( n  = 27). Only two studies examined a team effectiveness outcome. The most common implementation outcomes were fidelity ( n  = 16) and other specified implementation outcomes (e.g., “extent of use,” “implementation success”) ( n  = 15). Less frequently identified implementation outcomes included adoption ( n  = 5), sustainment ( n  = 4), reach ( n  = 4), and perceptions of the innovation (e.g., acceptability, appropriateness, feasibility; n  = 3). Approximately one-third of studies ( n  = 21) did not report specific implementation outcomes but described implementation determinants (i.e., barriers and facilitators).

Synthesis: team inputs & implementation outcomes

Team inputs examined in studies included team stability/instability and staffing shortages, aspects of team structure and composition, interdependence, and hierarchy and professional roles. Quantitative findings are presented in Table  1 . A CERQual Summary of Qualitative Findings related to team inputs is shown in Table  2 . A CERQual Evidence Profile is provided in Additional File 2 (Table A1).

Team stability/instability and staffing shortages

Team stability/instability (i.e., consistency in membership over time) was examined in one mixed methods study [ 48 , 49 ] and three qualitative studies [ 70 , 81 , 94 ]. A study of surgical teams found variations in membership stability but no association between stability and “implementation success” (i.e., composite measure based on number of uses of new technique, proportion of uses, and changes in use) [ 48 , 49 ]. The authors suggested that stability facilitates the development of team coordination but that selecting small and exclusive teams may limit the spread of innovations within the organization. Another study found that a dedicated and stable team in which members were selected and trained together in the use of a new surgical technique led to quicker uptake and better integration into practice, theorizing that dedicated and stable teams increased trust, motivation, and collaborative problem-solving [ 81 ]. However, dedicated teams were difficult to sustain, and some sites instead used rotating team members from a larger pool of trained staff. In rural primary care, stability of team members facilitated sustainment of memory care clinics [ 70 ]. Lastly, another study in primary care found mixed perceptions of stable vs. rotating staff when adding a new team role (i.e., health coach); some team members liked rotating through different roles while others wanted more stability [ 94 ]. Across studies, we found that dedicated and stable team members facilitate implementation while instability in team membership is a barrier to implementation (moderate confidence).

Qualitative studies identified staffing shortages and turnover on teams as barriers to implementation [ 50 , 67 , 75 , 78 , 92 ]. In Veterans Health Administration (VA) clinics, “inadequate staffing posed an insurmountable barrier,” hindering communication and delivery of optimal care during the implementation of the patient-centered medical home (PCMH) model [ 92 ]. Similarly, staff shortages, turnover, and high workloads hindered guideline implementation in Kenyan hospitals [ 75 ]. Two studies found negative impacts of staffing shortages and turnover on sustainment. Staff turnover contributed to discontinuity in Dialectical Behavior Therapy (DBT) team members [ 78 ], and appropriate staffing (i.e., ensuring manageable workloads) and blocking time for team members were identified as critical to sustainment of a team-based model in the VA [ 67 ]. We found that staffing shortages and turnover hinder implementation (high confidence).

Team structure/composition

Studies examined multiple aspects of team structure and composition, specifically team size, workload, longevity (i.e., how long team members had worked together), history of change, and team member characteristics. Team size was examined in two studies of DBT. In a mixed methods study, team size was positively correlated with fidelity, and qualitative data suggested that team size may increase as a result of successful implementation [ 47 ]. In contrast, another study found that DBT team size was not associated with the number of DBT components adopted and was negatively associated with reach, suggesting reach may reflect high workloads [ 72 ]. In VA mental health clinics, team workload (i.e., number of patients seen) was negatively associated with sustainment of trauma-focused therapies [ 68 ]. In these studies, team longevity and history of change were not associated with implementation outcomes [ 47 , 68 ]. Team member characteristics, specifically team member competency/expertise, experience, and commitment/engagement, were identified as facilitators of implementation in some qualitative studies [ 40 , 70 , 81 , 84 , 95 ].

Overall, few findings could be made from quantitative studies examining team structure and composition. Two studies of team size found mixed results, and workload, longevity, and history of change were examined in only one study each. Across qualitative studies, we found team member competency/expertise, experience, and commitment/engagement facilitate implementation (moderate confidence).

Team interdependence

One quantitative study examined team interdependence [ 65 ]. In multidisciplinary child abuse teams implementing a mental health screening/referral protocol, task interdependence (i.e., reliance on team members to share resources and coordinate workflows) was positively associated with reach but not time to adoption. Outcome interdependence (i.e., extent to which outcomes are evaluated at the team vs. individual level) was significantly negatively correlated with time to adoption but not reach. Neither task nor outcome interdependence were associated with team members’ perceptions of acceptability, appropriateness, or feasibility of the innovation [ 65 ]. Because only one study examined interdependence, no review findings were made.

Hierarchy & professional roles

Hierarchy, power distributions, and rigid roles were identified as barriers to implementation in several qualitative studies [ 50 , 53 , 74 , 97 ]. Flatter hierarchies (i.e., more equal distribution of power and authority) supported guideline implementation in pediatric primary care; practices with low compliance to guidelines had more hierarchical relationships while practices with high compliance had more shared decision-making [ 97 ]. In a setting with hierarchy and rigid division of roles, nurses trained in an innovation reported concern that their decisions would be questioned by physicians without expertise in the innovation but more authority [ 74 ]. Similarly, in surgical teams, rigid professional roles and a hierarchical team culture constrained open discussion and created contention over how and when a “time-out” should be completed, resulting in inconsistent use and poor fidelity [ 50 , 53 ]. Across studies, we found that in multidisciplinary settings, rigid professional roles, hierarchical relationships, and power differentials are barriers to implementation (moderate confidence).

Summary of team inputs & implementation outcomes

There was no overlap among team input variables and implementation outcomes examined in quantitative studies (Table  1 ). Accordingly, we were unable to generate estimates of effects or ratings of evidence quality. Qualitative review findings are shown in Table  2 . We found: 1) Dedicated and stable team members facilitate implementation while instability in team membership is a barrier to implementation (moderate confidence); 2) Staffing shortages and turnover hinder implementation (high confidence); 3) Team member competency/expertise, experience, and commitment/engagement facilitate implementation (moderate confidence); and 4) In multidisciplinary settings, rigid professional roles, hierarchical relationships, and power differentials are barriers to implementation (moderate confidence).

Synthesis: team processes/states & implementation outcomes

Studies examined overall team functioning as well as specific affective states, behavioral processes, and cognitive states. Quantitative findings are presented in Table  3 , and a GRADE Evidence Profile is provided in Additional File 2 (Table A2). A CERQual Summary of Qualitative Findings related to team processes and states is shown in Table  4 . A CERQual Evidence Profile is provided in Additional File 2 (Table A3).

Overall team functioning

Nine studies examined quantitative associations between overall team functioning and implementation outcomes. Team functioning was positively associated with intervention fidelity in 2 of 3 studies. One study examined implementation of transition programs for adolescents with chronic health conditions in 29 teams. More positive team climate, measured by the Team Climate Inventory (i.e., shared vision, participative safety, task orientation, support for innovation), at study start was associated with greater improvements in quality of chronic care delivery one year later [ 45 ]. Additionally, improvements in team climate were associated with greater improvement in care delivery [ 45 ]. These findings were consistent across teams working with different patient populations, suggesting the influence of team climate generalizes across teams and settings [ 45 ]. Greater team climate for innovation was also associated with greater fidelity (i.e., implementation of more program elements) among DBT teams [ 47 ]. In contrast, no significant associations were found between team climate and fidelity to a multifaceted cardiovascular disease management intervention, with qualitative data suggesting variation in the influence of teamwork across practices [ 77 ]. There was no overlap in the metrics of association reported in these studies; therefore, we were unable to generate an estimate of the effect of team functioning on fidelity. The quality of the evidence for fidelity was rated very low because of serious methodological limitations, serious inconsistency, and very serious imprecision due to the small number of studies.

Three studies examined associations between teamwork and adoption, with no significant associations found. The first study found that teamwork climate (i.e., perceived quality of collaboration between personnel) was not significantly associated with adoption of a comprehensive safety program in intensive care units, although there were associations between adoption and organizational constructs (e.g., lower safety climate, more management support) [ 59 ]. In a study of DBT teams, neither positive nor negative team functioning was associated with the number of DBT modes adopted [ 72 ]. The third study assessed relational coordination (i.e., shared goals, communication, respect) in primary care practices implementing patient engagement strategies. Relational coordination was high across practices initially and did not differ for practices with high vs. low adoption, although it increased over time in practices with high adoption [ 83 ]. There was no overlap in the metrics of association reported in these studies; therefore, we were unable to generate an estimate of the effect of team functioning on adoption. The quality of the evidence was rated very low because of serious methodological limitations and very serious imprecision due to the small number of studies.

Reach and sustainment were each examined in one quantitative study. DBT teams with more negative functioning had greater reach, suggesting that reach may reflect high workloads; positive functioning was not associated with reach [ 72 ]. In VA mental health clinics, team functioning was positively correlated with sustainment of evidence-based trauma-focused psychotherapies and significantly associated with sustainment after controlling for covariates [ 68 ]. Two studies examined other implementation outcomes. One found that better team functioning was associated with greater implementation of changes to improve access to care in VA clinics [ 62 ]. In the other, primary care practices reporting better teamwork were more likely to be in later stages of transformation to PCMHs than practices with poorer teamwork [ 88 ]. Because of the small number of studies examining reach, sustainment, and other implementation outcomes, we were unable to generate estimates of effects or ratings of evidence quality for these outcomes.

Our qualitative review findings are based on 12 studies describing how team functioning influenced implementation processes and outcomes. There was considerable variation across studies in how team functioning was defined and what implementation outcomes were examined. Most findings were based on relatively thin and superficial data. Studies occurred in a variety of healthcare settings with varying resources and implemented diverse interventions. We found with high confidence that 1) Adaptive team functioning, characterized by positive affective states (e.g., trust, mutual respect, belonging), effective behavior processes (e.g., frequent communication and coordination), and shared cognitive states (e.g., clear roles, shared mental models of how to provide care), facilitates implementation and is associated with better implementation outcomes; and 2) Problems in team functioning, including negative affective states (e.g., tension, lack of trust), problematic behavioral processes (e.g., conflict, competition, poor communication), and a lack of shared cognitive states (e.g., unclear roles, lack of shared awareness, competing goals), act as barriers to implementation and are associated with poor implementation outcomes.

Affective states

Specific affective states were examined in one quantitative study, three mixed methods studies, and one qualitative study. There was no overlap in the associations between affective states and implementation outcomes reported in quantitative studies (Table  3 ). In a study of multidisciplinary teams responding to child abuse, affective integration (i.e., liking, trust, respect) was positively associated with acceptability, appropriateness, and feasibility but not time to adoption or reach [ 65 ]. In DBT teams, cohesion was associated with fidelity, and qualitative data indicated that liking one’s team members and having a shared team identity were critical to effective implementation [ 47 ]. Another study of DBT teams described conflicts and lack of safety and trust within teams resulting in their dissolution [ 78 ].

Edmondson and colleagues found that psychological safety and ease of speaking up (i.e., interpersonal climate that allows members to share questions and concerns) were associated with implementation success [ 48 , 49 ]. In teams with low psychological safety, lower-status team members were hesitant to speak up, hindering change and proficiency in the new practice [ 49 ]. Psychological safety was closely related to learning behavior within the team, including speaking up with questions and concerns [ 48 , 49 ]. From the mixed methods and qualitative studies, we found that trust, cohesion, and psychological safety within teams facilitate implementation by contributing to team members’ willingness to speak up and share experiences and feedback. Negative affective states, fear of judgment, conflict, and lack of safety hinder implementation (moderate confidence).

Behavioral processes

Specific behavioral processes, including communication, learning behavior, and coordination, were examined in two quantitative studies, two mixed methods studies, and five qualitative studies. There was no overlap in the associations between behavioral processes and implementation outcomes reported in quantitative studies (Table  3 ).

Only one study reported quantitative findings for communication. Communication in DBT teams was positively associated with fidelity [ 47 ]. Qualitative studies frequently identified communication as a determinant of implementation (Table  4 ). From qualitative studies, we found that open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier (high confidence).

Quantitative associations between team learning behavior and implementation outcomes were reported in three studies. Team learning behavior in child abuse teams was positively correlated with acceptability and feasibility; it was not associated with appropriateness, time to adoption, or reach [ 65 ]. Learning behavior was positively associated with knowledge and use of innovations in nursing teams [ 91 ] and with implementation success in surgical teams [ 48 ]. Because each of these studies examined different implementation outcomes, we were unable to generate an estimate of the effect of learning behavior or rate evidence quality.

Inter-team communication, specifically speaking up and learning from other teams (i.e., boundary spanning), was identified as a critical part of team learning processes associated with successful implementation [ 48 ]. Communication beyond the team was also identified as a facilitator of implementation in two qualitative studies [ 47 , 75 ]. We found that communication beyond the team facilitates implementation by providing opportunities for team learning (low confidence).

Lastly, two qualitative studies examined coordination among healthcare teams [ 40 , 95 ]. Findings were somewhat ambiguous and based on thin data. We found with low confidence that poor coordination among healthcare professionals interferes with providing high-quality care and can be a barrier to implementation of new approaches (low confidence).

Cognitive states

Specific cognitive states were examined in two quantitative studies. There was no overlap in the associations between cognitive states and implementation outcomes reported (Table  3 ). The first study found no significant associations between shared goals and implementation outcomes [ 65 ]. The second study found that greater team knowledge and skills were associated with greater implementation of key changes to improve access to care; team problem recognition was not associated with implementation [ 62 ].

Two studies reported qualitative findings related to shared goals. In VA mental health teams, shared mission differentiated teams with sustained high reach of EBPs from those with low reach [ 84 ]. Commitment to a shared goal consistent with the EBP supported sustainment [ 84 ]. Similarly, shared goals and vision were identified as a facilitator of DBT programs [ 47 ]. We found that shared goals, mission, and vision within teams facilitate implementation and sustainment (low confidence).

Summary of team processes/states & implementation outcomes

There was very little overlap in the reported associations between team processes/states and implementation outcomes (Table  3 ). We were unable to generate estimates of effects for any associations. When there was sufficient overlap to rate evidence quality, the evidence was rated very low quality (Table A2, Additional File 2).

Qualitative review findings are shown in Table  4 . We found the following: 1) Adaptive team functioning, characterized by positive affective states (e.g., trust, mutual respect, belonging), effective behavior processes (e.g., frequent communication and coordination), and shared cognitive states (e.g., clear roles, shared mental models of how to provide care), facilitates implementation and is associated with better implementation outcomes (high confidence); 2) Problems in team functioning, including negative affective states (e.g., tension, lack of trust), problematic behavioral processes (e.g., conflict, competition, poor communication), and a lack of shared cognitive states (e.g., unclear roles, lack of shared awareness, competing goals), act as barriers to implementation and are associated with poor implementation outcomes (high confidence); 3) Trust, cohesion, and psychological safety within teams facilitate implementation by contributing to team members’ willingness to speak up and openly share experiences and feedback. Negative affective states, fear of judgment, conflict, and lack of safety hinder implementation (moderate confidence); 4) Open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier (high confidence); 5) Communication beyond the team facilitates implementation by providing opportunities for team learning (low confidence); 6) Poor coordination among healthcare professionals interferes with providing high-quality care and can be a barrier to implementation of new approaches (low confidence); and 7) Shared goals, mission, and vision within teams facilitate implementation and sustainment (low confidence).

Synthesis: team effectiveness outcomes & implementation outcomes

Team effectiveness outcomes are multidimensional and include performance (i.e., productivity, efficiency, and quality of the team’s work), team viability, and the impact of the team on members’ development [ 12 , 17 , 18 , 19 ]. Only two studies examined associations between team effectiveness and implementation outcomes. Quantitative findings are presented in Table  5 . One quantitative study found that team members’ ratings of team performance were associated with innovation acceptability, appropriateness, and feasibility; performance was not associated with time to adoption or reach [ 65 ]. One qualitative study found that positive outcomes for team members (e.g., increased comfort working together, greater knowledge) were associated with sustainment [ 70 ]. No studies examined associations of team viability and implementation outcomes.

Summary of team effectiveness outcomes & implementation outcomes

Only one quantitative study examined associations between a dimension of team effectiveness and implementation outcomes (Table  5 ). Accordingly, we were unable to generate ratings of evidence quality or estimates of any effects. Similarly, because there was only one qualitative study, we were unable to make a review finding.

This systematic review summarizes over 20 years of empirical literature on the associations between teamwork and implementation outcomes in the context of implementation of new practices in health and human services. Consistent with increased attention to teams and reliance on team-based models of care, as well as the growth of implementation science, studies increased substantially over time. We included studies that used quantitative, qualitative, or mixed methods, yielding a total of 58 articles representing 55 studies. Included studies spanned naturalistic implementation evaluations and planned implementation research.

Key findings with high confidence were: 1) Staffing shortages and turnover hinder implementation, 2) Adaptive team functioning, characterized by positive affective states, effective behavior processes, and shared cognitive states, facilitates implementation and is associated with better implementation outcomes. Problems in team functioning, including negative affective states, problematic behavioral processes, and a lack of shared cognitive states, act as barriers to implementation and are associated with poor implementation outcomes, and 3) Open, ongoing, and effective communication within teams facilitates implementation of new practices; poor communication is a barrier. Our results generally align with conventional wisdom and scientific research outside of healthcare, increasing confidence in the findings. Team effectiveness and change management research in other types of organizations and settings (e.g., military, aviation, space exploration) [ 98 , 99 , 100 , 101 , 102 , 103 ] is largely converging.

Overall, the literature was heterogeneous, and many studies lacked specificity regarding team composition and implementation activities and outcomes. Teamwork was defined and measured inconsistently and with limited precision across studies, which hindered our ability to draw conclusions about how teams influence implementation processes and outcomes. There was also poor measurement and reporting of implementation outcomes in many studies, consistent with a recent review of research on implementation outcomes [ 28 , 29 ]. Many studies used broad measures encompassing multiple dimensions of teamwork. Among studies that assessed specific team processes and states, there was very little overlap across constructs assessed. Qualitative studies identified a rich array of specific team processes and states; research to confirm the presence of these factors in other settings and determine their associations with implementation outcomes is needed.

In Table  6 , we summarize the limitations of existing research on teams and implementation and provide recommendations for future research. Notably, increased specificity and rigor in how teamwork is conceptualized and assessed is needed to advance our understanding of how teamwork affects implementation processes and outcomes. Limited inclusion of teams and team constructs in implementation theories, models, and frameworks has likely contributed to the neglect of teams in implementation science [ 20 ]. Updates to theories, models, and framework should consider integrating teams and team-level constructs [ 20 ]. In addition, there are well-established theories of team effectiveness that could inform hypotheses about how specific team constructs affect implementation [ 104 , 105 , 106 , 107 ].

There is considerable room for improvement in the definition and description of teams and analysis of data from teams. Describing the structure and purpose of teams, as well as interdependencies within the team, can help differentiate teams from groups of individuals who do not constitute a team, an important conceptual distinction that can be difficult to discern in study descriptions. Reporting of sampling and recruitment procedures for teams and team-level response rates is needed. For quantitative studies, use of standardized, validated measures of teamwork constructs is recommended. Researchers should be careful to base inferences about teams on team-level data. Lastly, future research should follow recommendations for improving measurement and reporting of implementation outcomes [ 29 , 108 ] and consider the multilevel context of teams in theory, measurement, analysis, and interpretation of results [ 109 ].

Limitations

As with all systematic reviews, it is possible that we failed to identify some relevant articles or data. We did not search gray literature or conference abstracts or contact authors for unreported data. Our organization of studies by the IMOI framework is likely imperfect given the broad array of team constructs included and poor reporting in many studies. We included diverse innovations intended to improve patient care, including specific EBPs, clinical practice guidelines, models of care, care bundles, procedural changes, and technological innovations. This diversity in objects of implementation reflects ongoing debates about the necessary strength of evidence for objects of implementation and varying thresholds in different contexts [ 110 ]. In this review, high quality studies tended to involve clinical interventions with strong research evidence (e.g., DBT) and clinics in structured and often team-based healthcare systems (e.g., VA). Diversity of innovations and settings provides greater external validity for our findings but may mask some findings specific to certain innovations or settings.

We only included studies of existing teams providing clinical services, however, many studies provided limited descriptions of teams, and in some cases the distinction between clinical teams and implementation/quality improvement teams was unclear. There is increasing attention to use of teams in implementation frameworks [ 20 , 111 ] and evidence that functioning of implementation teams matters [ 112 , 113 ]. Research on the composition and functioning of implementation teams is an important area for future research.

Our systematic review findings indicate that teamwork matters for implementation. However, greater specificity and rigor are needed to advance our understanding of how teamwork influences implementation processes and outcomes. We provide recommendations for improving the conceptualization, description, assessment, analysis, and interpretation of research on teams implementing new practices.

Availability of data and materials

All data cited in this review came from published papers and are therefore already available. The data created as part of the review process are included in this published article and its supplementary information files.

Mitchell P, Wynia M, Golden R, McNellis B, Okun S, Webb CE, et al. Core principles and values of effective team-based health care. NAM Perspectives. 2012. Available from: https://nam.edu/perspectives-2012-core-principles-values-of-effective-team-based-health-care/  Cited 2020 Sep 23

Reiss-Brennan B, Brunisholz KD, Dredge C, Briot P, Grazier K, Wilcox A, et al. Association of integrated team-based care with health care quality, utilization, and cost. JAMA. 2016;316(8):826–34.

Article   PubMed   Google Scholar  

Katkin JP, Kressly SJ, Edwards AR, Perrin JM, Kraft CA, Richerson JE, et al. Guiding principles for team-based pediatric care. Pediatrics. 2017;140(2):e20171489.

Lipton HL. Home is where the health is: Advancing team-based care in chronic disease management. Arch Intern Med. 2009;169(21):1945–8.

Dinh JV, Traylor AM, Kilcullen MP, Perez JA, Schweissing EJ, Venkatesh A, et al. Cross-disciplinary care: A systematic review on teamwork processes in health care. Small Group Research. 2020;51(1):125–66.

Article   Google Scholar  

Fiscella K, McDaniel SH. The complexity, diversity, and science of primary care teams. Am Psychol. 2018;73(4):451–67.

Herbert JL, Bromfield LM. A quasi-experimental study of the Multi-Agency Investigation & Support Team (MIST): A collaborative response to child sexual abuse. Child Abuse Negl. 2020;27:104827.

Google Scholar  

Reiss-Brennan B. Mental Health Integration: Normalizing Team Care. J Prim Care Community Health. 2014;5(1):55–60.

Schmutz J, Manser T. Do team processes really have an effect on clinical performance? A systematic literature review. Br J Anaesth. 2013;110(4):529–44.

Article   CAS   PubMed   Google Scholar  

Ilgen DR, Hollenbeck JR, Johnson M, Jundt D. Teams in organizations: From input-process-output models to IMOI models. Annu Rev Psychol. 2005;56:517–43.

Kozlowski SWJ, Bell BS. Work groups and teams in organization. In: Borman WC, Ilgen DR, Klimoski RJ, editors. Handbook of Psychology (Vol 12): Industrial and Organizational Psychology. New York: Wiley-Blackwell. 2003. p. 333–75. Available from: http://onlinelibrary.wiley.com/doi/abs/ https://doi.org/10.1002/9781118133880.hop212017   Cited 2020 Apr 10

Mathieu JE, Maynard MT, Rapp T, Gilson L. Team effectiveness 1997–2007: A review of recent advancements and a glimpse into the future. J Manag. 2008;34(3):410–76.

Cordery JL, Tian AW. Team Design. In: The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes. John Wiley & Sons, Ltd. 2017;p. 103–28. Available from: http://onlinelibrary.wiley.com/doi/abs/ https://doi.org/10.1002/9781118909997.ch5  Cited 2021 Apr 2 

Kozlowski SWJ, Bell BS. Work groups and teams in organizations: Review update. In: Schmitt N, Highhouse S, editors. Handbook of Psychology (Vol 12): Industrial and Organizational Psychology. 2nd ed. Hoboken, NJ: Wiley; 2013. p. 111.

Kozlowski SWJ, Ilgen DR. Enhancing the effectiveness of work groups and teams. Psychol Sci Public Interest. 2006;7(3):77–124.

Marks MA, Mathieu JE, Zaccaro SJ. A temporally based framework and taxonomy of team processes. AMR. 2001;26(3):356–76.

Hackman JR. A normative model of work team effectiveness. Arlington, VA: Office of Naval Research; 1983. Available from: https://apps.dtic.mil/sti/citations/ADA136398  Cited 2022 May 16

Mathieu JE, Hollenbeck JR, van Knippenberg D, Ilgen DR. A century of work teams in the Journal of Applied Psychology. J Appl Psychol. 2017;102(3):452–67.

Rosen MA, Dietz AS. Team performance measurement. In: The Wiley Blackwell Handbook of the Psychology of Team Working and Collaborative Processes. John Wiley & Sons, Ltd; 2017. p. 479–502.

Chapter   Google Scholar  

McGuier EA, Kolko DJ, Stadnick NA, Brookman-Frazee L, Wolk CB, Yuan CT, et al. Advancing research on teams and team effectiveness in implementation science: An application of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Implementation Research and Practice. 2023;1(4):26334895231190856.

Williams NJ, Beidas RS. Annual research review: The state of implementation science in child psychology and psychiatry: A review and suggestions to advance the field. J Child Psychol Psychiatry. 2019;60:430–50.

Aarons GA, Hurlburt M, Horwitz SM. Advancing a conceptual model of evidence-based practice implementation in public service sectors. Adm Policy Ment Health. 2011;38(1):4–23.

Moullin JC, Dickson KS, Stadnick NA, Rabin B, Aarons GA. Systematic review of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Implement Sci. 2019;14(1):1.

Article   PubMed   PubMed Central   Google Scholar  

McGuier EA, Kolko DJ, Klem ML, Feldman J, Kinkler G, Diabes MA, et al. Team functioning and implementation of innovations in healthcare and human service settings: a systematic review protocol. Syst Rev. 2021;10(1):189.

Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339. Available from: http://www.bmj.com/content/339/bmj.b2535  Cited 2020 Apr 1

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;29(372):n71.

Bramer WM, Giustini D, de Jonge GB, Holland L, Bekhuis T. De-duplication of database search results for systematic reviews in EndNote. J Med Libr Assoc. 2016;104(3):240–3.

Lengnick-Hall R, Gerke DR, Proctor EK, Bunger AC, Phillips RJ, Martin JK, et al. Six practical recommendations for improved implementation outcomes reporting. Implement Sci. 2022;17(1):16.

Proctor EK, Bunger AC, Lengnick-Hall R, Gerke DR, Martin JK, Phillips RJ, et al. Ten years of implementation outcomes research: a scoping review. Implement Sci. 2023;18(1):31.

Chapman A, Rankin NM, Jongebloed H, Yoong SL, White V, Livingston PM, et al. Overcoming challenges in conducting systematic reviews in implementation science: a methods commentary. Syst Rev. 2023;12(1):116.

Pranckutė R. Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications. 2021;9(1):12.

Effective Practice and Organisation of Care (EPOC). Data collection form. EPOC Resources for review authors. Norwegian Knowledge Centre for the Health Services. 2013. Available from:  http://epoc.cochrane.org/epoc-specific-resources-review-authors .

Noyes J, Lewin S. Extracting qualitative evidence. In: Noyes J, Booth A, Hannes K, Harden A, Harris J, Lewin S, et al., editors. Supplementary Guidance for Inclusion of Qualitative Research in Cochrane Systematic Reviews of Interventions. Version 1 (updated August 2011). Cochrane Collaboration Qualitative Methods Group; 2011. Available from: http://cqrmg.cochrane.org/supplemental-handbook-guidance

Hong QN, Pluye P, Fàbregues S, Bartlett G, Boardman F, Cargo M, et al. Improving the content validity of the mixed methods appraisal tool: a modified e-Delphi study. J Clin Epidemiol. 2019;1(111):49-59.e1.

Powell BJ, Patel SV, Haley AD, Haines ER, Knocke KE, Chandler S, Determinants of implementing evidence-based trauma-focused interventions for children and youth: A systematic review. Adm Policy Ment Health, et al. cited 2020 Feb 20. Available from: 2019. https://doi.org/10.1007/s10488-019-01003-3 .

Article   PubMed Central   Google Scholar  

Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ. 2020;16(368):l6890.

Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction—GRADE evidence profiles and summary of findings tables. Journal of Clinical Epidemiology. 2011;64(4):383–94.

Lewin S, Bohren M, Rashidian A, Munthe-Kaas H, Glenton C, Colvin CJ, et al. Applying GRADE-CERQual to qualitative evidence synthesis findings—paper 2: how to make an overall CERQual assessment of confidence and create a Summary of Qualitative Findings table. Implementation Sci. 2018;13(1):11–23.

Lewin S, Booth A, Glenton C, Munthe-Kaas H, Rashidian A, Wainwright M, et al. Applying GRADE-CERQual to qualitative evidence synthesis findings: introduction to the series. Implementation Sci. 2018;13(1):1–10.

Adjognon OL, Shin MH, Steffen MJA, Moye J, Solimeo S, Sullivan JL. Factors affecting primary care implementation for older veterans with multimorbidity in Veterans Health Administration (VA). Health Serv Res. 2021;56(S1):1057–68.

Alidina S, Chatterjee P, Zanial N, Alreja SS, Balira R, Barash D, et al. Improving surgical quality in low-income and middle-income countries: why do some health facilities perform better than others? BMJ Qual Saf. 2021;30(12):937–49.

Boltey EM, Iwashyna TJ, Hyzy RC, Watson SR, Ross C, Costa DK. Ability to predict team members’ behaviors in ICU teams is associated with routine ABCDE implementation. J Crit Care. 2019;51:192–7.

Chenoweth L, Jeon YH, Stein-Parbury J, Forbes I, Fleming R, Cook J, et al. PerCEN trial participant perspectives on the implementation and outcomes of person-centered dementia care and environments. Int Psychogeriatr. 2015;27(12):2045–57.

Costa DK, Valley TS, Miller MA, Manojlovich M, Watson SR, McLellan P, et al. ICU team composition and its association with ABCDE implementation in a quality collaborative. J Crit Care. 2018;44:1–6.

Cramm JM, Strating MMH, Nieboer AP. The role of team climate in improving the quality of chronic care delivery: a longitudinal study among professionals working with chronically ill adolescents in transitional care programmes. BMJ Open. 2014;4(5):e005369.

DePuccio MJ, Gaughan AA, Sova LN, MacEwan SR, Walker DM, Gregory ME, et al. An examination of the barriers to and facilitators of implementing nurse-driven protocols to remove indwelling urinary catheters in acute care hospitals. The Joint Commission Journal on Quality and Patient Safety. 2020;46(12):691–8.

Ditty MS, Landes SJ, Doyle A, Beidas RS. It takes a village: A mixed method analysis of inner setting variables and Dialectical Behavior Therapy implementation. Adm Policy Ment Health. 2015;42(6):672–81.

Edmondson AC. Speaking up in the operating room: How team leaders promote learning in interdisciplinary action teams. J Manage Stud. 2003;40(6):1419–52.

Edmondson AC, Bohmer RM, Pisano GP. Disrupted routines: Team learning and new technology implementation in hospitals. Adm Sci Q. 2001;46(4):685–716.

Gillespie BM, Chaboyer W, Wallis M, Fenwick C. Why isn’t “time out” being implemented? An exploratory study. Qual Saf Health Care. 2010;19(2):103–6.

Gillespie BM, Harbeck E, Lavin J, Gardiner T, Withers TK, Marshall AP. Using normalisation process theory to evaluate the implementation of a complex intervention to embed the surgical safety checklist. BMC Health Serv Res. 2018;18(1):170.

Gillespie BM, Marshall AP, Gardiner T, Lavin J, Withers TK. Impact of workflow on the use of the Surgical Safety Checklist: a qualitative study: Surgical Safety Checklist and work flow. ANZ J Surg. 2016;86(11):864–7.

Gillespie BM, Withers TK, Lavin J, Gardiner T, Marshall AP. Factors that drive team participation in surgical safety checks: a prospective study. Patient Saf Surg. 2016;10(3):1–9.

Gosling AS. Clinical team functioning and IT innovation: A study of the diffusion of a point-of-care online evidence system. J Am Med Inform Assoc. 2003;10(3):244–51.

Graetz I, Huang J, Brand R, Shortell SM, Rundall TG, Bellows J, et al. The impact of electronic health records and teamwork on diabetes care quality. American Journal of Managed Care. 2015;21(12):878–84.

PubMed   Google Scholar  

Haider S, Ali RF, Ahmed M, Humayon AA, Sajjad M, Ahmad J. Barriers to implementation of emergency obstetric and neonatal care in rural Pakistan. Sawyer A, editor. PLoS ONE. 2019;14(11):e0224161.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Harvey C, Killaspy H, Martino S, White S, Priebe S, Wright C, et al. A comparison of the implementation of Assertive Community Treatment in Melbourne, Australia and London. England Epidemiol Psychiatr Sci. 2011;20(2):151–61.

Harvey C, Killaspy H, Martino S, Johnson S. Implementation of Assertive Community Treatment in Australia: Model fidelity, patient characteristics and staff experiences. Community Ment Health J. 2012;48(5):652–61.

Hsu YJ, Marsteller JA. Who applies an intervention to influence cultural attributes in a quality improvement collaborative? J Patient Saf. 2020;16(1):1–6.

Irimu GW, Greene A, Gathara D, Kihara H, Maina C, Mbori-Ngacha D, et al. Explaining the uptake of paediatric guidelines in a Kenyan tertiary hospital – mixed methods research. BMC Health Serv Res. 2014;14(1):119.

Lall D, Engel N, Devadasan N, Horstman K, Criel B. Team-based primary health care for non-communicable diseases: complexities in South India. Health Policy Plan. 2020;35(Supplement_2):ii22-34.

Lukas CV, Mohr D, Meterko M. Team effectiveness and organizational context in the implementation of a clinical innovation. Qual Manag Health Care. 2009;18(1):25–39.

Mariani E, Vernooij-Dassen M, Koopmans R, Engels Y, Chattat R. Shared decision-making in dementia care planning: barriers and facilitators in two European countries. Aging Ment Health. 2017;21(1):31–9.

Martinez RN, Hogan TP, Balbale S, Lones K, Goldstein B, Woo C, et al. Sociotechnical perspective on implementing clinical video telehealth for veterans with spinal cord injuries and disorders. Telemedicine and e-Health. 2017;23(7):567–76.

McGuier EA, Aarons GA, Byrne KA, Campbell KA, Keeshin B, Rothenberger SD, et al. Associations between teamwork and implementation outcomes in multidisciplinary cross-sector teams implementing a mental health screening and referral protocol. Implementation Science Communications. 2023;4(1):13.

Meloncelli N, Barnett A, de Jersey S. Staff resourcing, guideline implementation and models of care for gestational diabetes mellitus management. Aust N Z J Obstet Gynaecol. 2020;60(1):115–22.

Miller CJ, Kim B, Connolly SL, Spitzer EG, Brown M, Bailey HM, et al. Sustainability of the Collaborative Chronic Care Model in outpatient mental health teams three years post-Implementation: A qualitative analysis. Adm Policy Ment Health. 2023;50(1):151–9.

Mohr DC, Rosen CS, Schnurr PP, Orazem RJ, Noorbaloochi S, Clothier BA, et al. The influence of team functioning and workload on sustainability of trauma-focused evidence-based psychotherapies. PS. 2018;69(8):879–86.

Morgan D, Kosteniuk J, O’Connell ME, Kirk A, Stewart NJ, Seitz D, et al. Barriers and facilitators to development and implementation of a rural primary health care intervention for dementia: a process evaluation. BMC Health Serv Res. 2019;19(1):709.

Morgan D, Kosteniuk J, O’Connell ME, Seitz D, Elliot V, Bayly M, et al. Factors influencing sustainability and scale-up of rural primary healthcare memory clinics: perspectives of clinic team members. BMC Health Serv Res. 2022;22(1):148.

Munce SEP, Graham ID, Salbach NM, Jaglal SB, Richards CL, Eng JJ, et al. Perspectives of health care professionals on the facilitators and barriers to the implementation of a stroke rehabilitation guidelines cluster randomized controlled trial. BMC Health Serv Res. 2017;17(1):440.

Navarro-Haro MV, Harned MS, Korslund KE, DuBose A, Chen T, Ivanoff A, et al. Predictors of adoption and reach following Dialectical Behavior Therapy Intensive Training™. Community Ment Health J. 2019;55(1):100–11.

Newbould L, Ariss S, Mountain G, Hawley MS. Exploring factors that affect the uptake and sustainability of videoconferencing for healthcare provision for older adults in care homes: a realist evaluation. BMC Med Inform Decis Mak. 2021;21(1):13.

Nyondo-Mipando AL, Woo Kinshella ML, Bohne C, Suwedi-Kapesa LC, Salimu S, Banda M, et al. Barriers and enablers of implementing bubble Continuous Positive Airway Pressure (CPAP): Perspectives of health professionals in Malawi. Ameh CA, editor. PLoS ONE. 2020;15(2):e0228915.

Nzinga J, Mbindyo P, Mbaabu L, Warira A, English M. Documenting the experiences of health workers expected to implement guidelines during an intervention study in Kenyan hospitals. Implementation Sci. 2009;4(1):44.

Papoutsi C, Poots A, Clements J, Wyrko Z, Offord N, Reed JE. Improving patient safety for older people in acute admissions: implementation of the Frailsafe checklist in 12 hospitals across the UK. Age Ageing. 2018;47(2):311–7.

Patel B, Usherwood T, Harris M, Patel A, Panaretto K, Zwar N, et al. What drives adoption of a computerised, multifaceted quality improvement intervention for cardiovascular disease management in primary healthcare settings? A mixed methods analysis using normalisation process theory. Implementation Sci. 2018;13(1):140.

Popowich AD, Mushquash AR, Pearson E, Schmidt F, Mushquash CJ. Barriers and facilitators affecting the sustainability of dialectical behaviour therapy programmes: A qualitative study of clinician perspectives. Couns Psychother Res. 2020;20(1):68–80.

Prom MC, Canelos V, Fernandez PJ, Gergen Barnett K, Gordon CM, Pace CA, et al. Implementation of integrated behavioral health care in a large medical center: Benefits, challenges, and recommendations. J Behav Health Serv Res. 2021;48(3):346–62.

Provost S, Pineault R, Grimard D, Pérez J, Fournier M, Lévesque Y, et al. Implementation of an integrated primary care cardiometabolic risk prevention and management network in Montréal: does greater coordination of care with primary care physicians have an impact on health outcomes? Health Promot Chronic Dis Prev Can. 2017;37(4):105–13.

Randell R, Honey S, Alvarado N, Greenhalgh J, Hindmarsh J, Pearman A, et al. Factors supporting and constraining the implementation of robot-assisted surgery: a realist interview study. BMJ Open. 2019;9(6):e028635.

Robbins J, McAlearney AS. Encouraging employees to speak up to prevent infections: Opportunities to leverage quality improvement and care management processes. Am J Infect Control. 2016;44(11):1224–30.

Rodriguez HP, Poon BY, Wang E, Shortell SM. Linking practice adoption of patient engagement strategies and relational coordination to patient-reported outcomes in accountable care organizations. Milbank Q. 2019;97(3):692–735.

Sayer NA, Rosen CS, Bernardy NC, Cook JM, Orazem RJ, Chard KM, et al. Context matters: Team and organizational factors associated with reach of evidence-based psychotherapies for PTSD in the Veterans Health Administration. Adm Policy Ment Health. 2017;44(6):904–18.

Shin MH, Montano ARL, Adjognon OL, Harvey KLL, Solimeo SL, Sullivan JL. Identification of implementation strategies using the CFIR-ERIC matching tool to mitigate barriers in a primary care model for older veterans. Gerontologist. 2022;63(3):439–50.

Sopcak N, Aguilar C, O’Brien MA, Nykiforuk C, Aubrey-Bassler K, Cullen R, et al. Implementation of the BETTER 2 program: a qualitative study exploring barriers and facilitators of a novel way to improve chronic disease prevention and screening in primary care. Implementation Sci. 2016;11(1):158.

Steinmo SH, Michie S, Fuller C, Stanley S, Stapleton C, Stone SP. Bridging the gap between pragmatic intervention design and theory: using behavioural science tools to modify an existing quality improvement programme to implement “Sepsis Six.” Implementation Sci. 2016;11(1):14.

Stout S, Zallman L, Arsenault L, Sayah A, Hacker K. Developing high-functioning teams: Factors associated with operating as a “real team” and implications for patient-centered medical home development. Inquiry. 2017;12(54):0046958017707296.

Svensson B, Hansson L, Markström U, Lexén A. What matters when implementing Flexible Assertive Community Treatment in a Swedish healthcare context: A two-year implementation study. Int J Ment Health. 2017;46(4):284–98.

Tadyanemhandu C, van Aswegen H, Ntsiea V. Barriers and facilitators to implementation of early mobilisation of critically ill patients in Zimbabwean and South African public sector hospitals: a qualitative study. Disabil Rehabil. 2021;44(22):6699–709.

Timmermans O, Van Linge R, Van Petegem P, Van Rompaey B, Denekens J. A contingency perspective on team learning and innovation in nursing: Team learning and innovation in nursing teams. J Adv Nurs. 2013;69(2):363–73.

Tuepker A, Kansagara D, Skaperdas E, Nicolaidis C, Joos S, Alperin M, et al. “We’ve not gotten even close to what we want to do”: A qualitative study of early patient-centered medical home implementation. J GEN INTERN MED. 2014;29(S2):614–22.

van Bruggen S, Kasteleyn MJ, Rauh SP, Meijer JS, Busch KJG, Numans ME, et al. Experiences with tailoring of primary diabetes care in well-organised general practices: a mixed-methods study. BMC Health Serv Res. 2021;21(1):1218.

Van der Wees PJ, Friedberg MW, Guzman EA, Ayanian JZ, Rodriguez HP. Comparing the implementation of team approaches for improving diabetes care in community health centers. BMC Health Serv Res. 2014;14(1):608.

von Meyenfeldt EM, van Nassau F, de Betue CTI, Barberio L, Schreurs WH, Marres GMH, et al. Implementing an enhanced recovery after thoracic surgery programme in the Netherlands: a qualitative study investigating facilitators and barriers for implementation. BMJ Open. 2022;12(1):e051513.

Wakefield BJ, Lampman MA, Paez MB, Stewart GL. Care management and care coordination within a patient-centered medical home. J Nurs Admin. 2020;50(11):565–70.

Wiener-Ogilvie S, Huby G, Pinnock H, Gillies J, Sheikh A. Practice organisational characteristics can impact on compliance with the BTS/SIGN asthma guideline: Qualitative comparative case study in primary care. BMC Fam Pract. 2008;9(1):32.

Errida A, Lotfi B. The determinants of organizational change management success: Literature review and case study. International Journal of Engineering Business Management. 2021;1(13):18479790211016270.

Phillips J, Klein JD. Change management: From theory to practice. TechTrends. 2023;67(1):189–97.

Rousseau DM, ten Have S. Evidence-based change management. Organ Dyn. 2022;51(3):100899.

Goodwin GF, Blacksmith N, Coats MR. The science of teams in the military: Contributions from over 60 years of research. Am Psychol. 2018;73(4):322–33.

DiazGranados D, Woods JJ, Wilder A, Curtis MT. Team dynamics in the air: A review of team research relevant to aviation. Human factors in aviation and aerospace. 2023. p. 199–215.

Salas E, Tannenbaum SI, Kozlowski SWJ, Miller CA, Mathieu JE, Vessey WB. Teams in space exploration: A new frontier for the science of team effectiveness. Curr Dir Psychol Sci. 2015;24(3):200–7.

Hackman JR. From causes to conditions in group research. J Organ Behav. 2012;33(3):428–44.

Tannenbaum SI, Salas E. Teams that work: The seven drivers of team effectiveness. Illustrated. New York, NY: Oxford University Press; 2020. p. 272.

Book   Google Scholar  

Tuckman BW. Developmental sequence in small groups. Psychol Bull. 1965;63:384–99.

Salas E, Stagl KC, Burke CS. 25 years of team effectiveness in organizations: Research themes and emerging needs. In: International Review of Industrial and Organizational Psychology 2004. John Wiley & Sons, Ltd. 2004; p. 47–91. Available from: https://onlinelibrary.wiley.com/doi/abs/ https://doi.org/10.1002/0470013311.ch2    Cited 2022 May 16

Proctor EK, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, et al. Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. 2011;38(2):65–76.

Lengnick-Hall R, Williams NJ, Ehrhart MG, Willging CE, Bunger AC, Beidas RS, et al. Eight characteristics of rigorous multilevel implementation research: a step-by-step guide. Implement Sci. 2023;18(1):52.

Wensing M, Sales A, Aarons GA, Xu D (Roman), Wilson P. Evidence for objects of implementation in healthcare: considerations for Implementation Science and Implementation Science Communications. Implementation Sci. 2022;17(1):83.

Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated Consolidated Framework for Implementation Research based on user feedback. Implement Sci. 2022;17(1):75.

Shortell SM, Marsteller JA, Lin M, Pearson ML, Wu SY, Mendel P, et al. The role of perceived team effectiveness in improving chronic illness care. Med Care. 2004;42(11):1040–8.

Yakovchenko V, Morgan TR, Chinman MJ, Powell BJ, Gonzalez R, Park A, et al. Mapping the road to elimination: a 5-year evaluation of implementation strategies associated with hepatitis C treatment in the veterans health administration. BMC Health Serv Res. 2021;21(1):1348.

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Acknowledgements

Thank you to Ikzzui Chu, Jamie Feldman, Grace Kinkler, Rachael Park, and Jaely Wright for their assistance with article screening.

This work was supported by the National Institute of Mental Health grants MH123729 (EAM), MH124914 (DJK), and MH126231 (GAA), the National Cancer Institute U01CA275118 (GAA), National Institute on Drug Abuse R01DA049891 (GAA), the Agency for Healthcare Research and Quality grant R18HS026862 (CBW), and the Collaboration and Conflict Research Lab at Carnegie Mellon University Tepper School of Business. The content of this manuscript does not represent the views of funding agencies and is solely the responsibility of the authors.

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EAM conceptualized and developed the protocol for this review, participated in article screening, data extraction, quality appraisal, and analysis, and led manuscript development and writing. DJK, GAA, LRW, ES, and CBW provided guidance and input into the protocol and methods and interpretation of results. AS participated in article screening, data extraction, and preparation of supplementary files. MLK developed the search strategy and conducted database searches. MAD participated in quality appraisal. DJK, GAA, LRW, ES, and CBW reviewed multiple drafts of the manuscript and provided critical input and editing. All authors reviewed and approved the final manuscript.

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McGuier, E.A., Kolko, D.J., Aarons, G.A. et al. Teamwork and implementation of innovations in healthcare and human service settings: a systematic review. Implementation Sci 19 , 49 (2024). https://doi.org/10.1186/s13012-024-01381-9

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A comprehensive review on brain–computer interface (bci)-based machine and deep learning algorithms for stroke rehabilitation.

1. Introduction

2. background.

• Unlocking the potential of brain–computer interfaces (BCIs) for brain rehabilitation goes hand in hand with the remarkable power of machine learning. These intricate algorithms act as digital guides, navigating the complex landscape of brain signals to reveal the hidden pathways to recovery. By learning to recognize the unique patterns within each person’s neural code, machine learning techniques like support vector machines, deep neural networks, and random forests become adept at deciphering intentions and translating them into tangible actions. This remarkable synergy opens a world of possibilities for individualized rehabilitation, allowing us to harness the power of our own minds to regain control and rebuild skills [ 21 , 22 , 23 , 24 ].
• BCIs are able to adapt to variations in a user’s brain impulses over time because of machine learning. Increasing accuracy and resilience, adaptive models continuously modify their parameters in response to fresh data [ 20 , 25 , 26 , 27 ].
• Tailored rehabilitation interventions are made possible by machine learning. Rehabilitative techniques can be adjusted as necessary by training models to identify each user’s unique brain patterns [ 7 , 11 , 19 , 28 , 29 , 30 ].
• Instant feedback: Machine learning can process brain signals very quickly, so BCIs can react in real time. This is like having a virtual coach that gives you feedback right away, helping you learn faster [ 14 , 17 ].
• Predicting success: Machine learning can look at brain activity and predict how well someone will do in rehab. This helps doctors make better treatment plans and obtain better results [ 20 ].
• Seeing the whole picture: Machine learning can combine different types of brain data, like EEGs and fMRIs, to obtain a more complete understanding of how the brain is working during rehab [ 17 , 20 ].

3. Diverse Applications of EEG, Machine Learning, and Deep Learning in Rehabilitation

• Motor Rehabilitation: The utilization of machine learning algorithms in decoding EEG signals during motor imagery tasks opens avenues for controlling external devices, including robotic exoskeletons and prosthetics [ 13 , 19 , 31 , 33 ]. Systems providing feedback in real time not only instruct users through precise motor tasks but also contribute significantly to motor skill relearning and the promotion of neuroplasticity [ 14 , 19 , 32 , 33 ].
• Cognitive Rehabilitation: EEG signals play a pivotal role in gauging and enhancing attention stages using Neurofeedback techniques [ 8 , 11 , 13 , 34 ]. The adaptability of machine learning facilitates the tailoring of training procedures tailored to individual mental states, while the nuanced capabilities of deep learning contribute to the design of personalized memory training tasks [ 7 , 8 , 13 , 34 ].
• Neuropsychiatric Rehabilitation: EEG neurotraining emerges as a beneficial instrument for handling stress and anxiety, utilizing artificial intelligence to recognize patterns associated with stress and triggering interventions for relaxation [ 10 , 28 , 35 , 36 ]. Moreover, neurotraining based on EEG assists those with ADHD in refining concentration and controlling attention by reinforcing preferred brain activity patterns [ 13 ].
• Walking Rehabilitation: Integrating ML and EEG with systems that capture movements allows for a comprehensive analysis of gait patterns, providing real-time feedback during walking exercises and potentially revolutionizing rehabilitation approaches [ 13 , 31 , 34 , 37 ].
• Visual and Hearing Restoration: Protocols relying on EEG, in collaboration with advanced machine learning approaches, provide an intricate structure for creating customized training activities for individuals experiencing sensory perception challenges in both vision and hearing [ 32 , 38 ].
• Multimodal Rehabilitation: The integration of EEG with other cutting-edge technologies, such as virtual reality or functional near-infrared spectroscopy, paves the way for innovative multimodal rehabilitation approaches [ 19 , 20 , 33 , 39 , 40 ].

4. EEG Signal Acquisition and Motor Imagery Training

4.1. eeg-based signal acquisition, 4.2. patient training on motor imagery tasks.

• Warm-up (t = 0 s): The user focuses on a fixation point or cue to settle their mind and prepare for the upcoming task. MI Task: A visual cue, such as an arrow, instructs the user to perform a specific MI task (e.g., imagine left-hand movement).
• Data Collection (t = 3 s): During the task period, EEG sensors capture the user’s brain activity, recording the unique electrical signature of the imagined movement.
• Machine Learning (t = 3–7 s): The collected data are then analyzed by a machine learning algorithm. This algorithm identifies the key features that distinguish different MI tasks from the EEG signals.
• No Feedback: In the initial phase, no feedback is provided to the user. This allows the machine learning algorithm to focus solely on understanding the user’s individual EEG patterns.
• MI Task Repetition: Once the system has been calibrated, users repeat the MI tasks. Real-time Feedback: This time, the system provides feedback in real time. For example, a bar might grow longer or change color based on the system’s confidence in recognizing the current MI task.
• Refinement and Repetition: With each trial, the user receives feedback and can adjust their mental strategies to produce clearer EEG patterns. This iterative process strengthens the connection between the imagined movement and the corresponding EEG signature.
• Gradual Improvement: The Graz training paradigm involves multiple training sessions, spread over days or weeks. With each session, the user’s ability to generate distinct EEG patterns for different MI tasks improves, leading to more accurate recognition by the system.
• Customization: The training protocol can be adapted to individual needs and goals. The specific MI tasks, cues, and feedback types can be tailored to suit different applications, such as controlling a prosthetic limb or navigating a virtual environment.

5. EEG Signal Processing and Classification Techniques in Rehabilitation Research

• Filtering: The application of low pass, high pass, and notch filters emerges as a crucial step in refining EEG signal quality, as it effectively eliminates undesirable frequency components [ 38 , 47 ].
• Artifact Elimination: Approaches like Independent Component Analysis (ICA) and Principal Component Analysis (PCA) play a crucial role in differentiating and removing disturbances, encompassing eye blinks, muscle movements, and interference from electrocardiograms [ 17 , 18 , 48 ].
• Time Domain Features: The extraction of characteristics like average amplitude, root mean square value, and signal variance represents a nuanced approach to capturing the temporal characteristics inherent in EEG signals [ 19 , 20 , 33 ].
• Frequency Domain Features: Insights into the frequency distribution of brain activity are unveiled through the meticulous examination of power spectral density, spectral entropy, and band power ratios [ 11 , 31 , 47 ].
• Time-Frequency Features: Methods such as wavelet transformation and short-time Fourier transformation enhance complexity by unveiling the dynamic variations in EEG signal characteristics across both time and frequency domains [ 32 , 49 , 50 ].
• Functional Interconnection: Measures encompassing coherence, phase synchronization, and mutual information serve as invaluable tools in assessing the intricate functional relationships between different brain regions [ 33 , 53 , 54 ].
• Graph Theory Analysis: The innovative representation of EEG data as networks, coupled with graph theory metrics, offers a unique lens through which organizational and communication patterns within the brain can be deciphered [ 51 , 52 ].
• Pattern Recognition and Motor Imagery: A specialized focus on processing EEG signals derived from motor imagery tasks facilitates the recognition of specific patterns associated with imagined movements, thereby paving the way for tailored interventions [ 26 , 55 , 56 ].

5.1. Feature and Channel Selection

• Filter Approach: Initiating with the full set of features, filter methods meticulously identify the optimal subset through dedicated selection criteria. These criteria often revolve around key characteristics like information gain, dependency, consistency, correlation, and distance measures [ 57 ]. A significant advantage of filter methods lies in their minimal computational requirements. Additionally, the feature selection process operates independently of the chosen classifier, providing greater flexibility. Widely utilized filter methods include correlation criteria and mutual information techniques, both meticulously honing in on the most informative features within the data landscape.
• Wrapper Approach: Distinct from filter methods, wrapper approaches forge a direct partnership with the classifier to select features meticulously. They iteratively present candidate feature subsets to the classifier, diligently evaluating its performance. This feedback loop guides the selection process, prompting either acceptance of a subset based on established criteria or the proposal of new combinations for further evaluation. Algorithms within this realm encompass searching algorithms and evolutionary algorithms. The former embarks on their quest with an empty set, strategically adding or removing features until the classifier’s performance peaks. Their journey typically concludes when a designated maximum feature subset size is attained. Meanwhile, evolutionary algorithms, such as particle swarm optimization (PSO) [ 58 ], and artificial bee colony (ABC) [ 59 , 60 ], harness nature-inspired optimization techniques to uncover optimal subsets. While wrapper methods excel at identifying feature combinations that yield superior classifier performance compared to filter methods, their computational demands are considerable, rendering them less suitable for handling vast datasets.

5.2. EEG-Based Machine Learning and Deep Learning Algorithms

• Sample Size: Effective with small-to-medium-sized datasets.
• Accuracy: High accuracy in binary and multi-class classification problems as Figure 4 .
• Training Cost: Moderate, with a need for tuning hyperparameters.
• Sample Size: Suitable for small datasets.
• Accuracy: Good for pattern recognition tasks.
• Training Cost: Low, as k-NN is a lazy learner.
• Sample Size: Requires a large dataset.
• Accuracy: High for spatial feature extraction and classification.
• Training Cost: High due to the need for extensive computational resources.
• Sample Size: Requires a large dataset.
• Accuracy: High for sequential data analysis.
• Training Cost: High due to recurrent nature and complex computations.
• Accuracy: High for temporal pattern recognition.
• Training Cost: High due to complex architecture.
• Sample Size: Effective with medium to large datasets.
• Accuracy: Moderate to high, depending on the complexity of the task.
• Training Cost: Moderate.

5.3. Performance Indicators and Metrics in Evaluating the Effectiveness of Methods

• Classification Accuracy: The efficacy of EEG-based models is scrutinized through the lens of classification accuracy, providing insights into their ability to distinguish between different classes or states in activities like differentiating motor imagery or identifying cognitive states [ 11 , 13 , 14 , 19 , 31 , 38 , 38 ].
• ROC Curve and AUC: The evaluation of the balance between specificity and sensitivity in classification assignments is facilitated through the utilization of ROC curves and AUC values, adding a layer of sophistication to the assessment process [ 17 , 38 ].
• MSE or RMSE: In the realm of regression functions, the metrics of Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) emerge as crucial, providing a nuanced measure of the accuracy of predictions by quantifying the difference between predicted and actual values [ 13 , 31 ].
• R-squared (R2): The extent to which the regression model fits the data is gauged through the lens of R-squared (R2), offering valuable insights into the predictive power of the model [ 49 , 50 ].
• Real-time Performance Measurements: In situations necessitating instantaneous responsiveness, parameters such as response latency, response time, and overall system latency offer a comprehensive evaluation of the system’s real-world applicability [ 26 , 55 ].

6. Present Constraints in the Ongoing Research on Rehabilitation Utilizing EEG with ML and DL Methods

• Noise and Artifacts: Concerns surrounding data quality, preprocessing methodologies, and the standardization of data gathering protocols cast a spotlight on the imperative need to address these aspects to ensure the reliability and consistency of results.
• Small Sample Sizes: The challenges when acquiring high-quality EEG data from groups of patients contribute to the prevalence of small sample sizes, potentially leading to model overfitting and hindering the generalizability of findings.
• Longitudinal EEG Datasets: The scarcity of longitudinal EEG datasets poses a significant hurdle in monitoring progress during neural rehabilitation. A dedicated focus on long-term research is indispensable for comprehensively understanding the effectiveness of diverse approaches and customizing interventions and treatments accordingly.
• Interpretability of Deep Learning: The opaque nature of deep learning models poses challenges in interpreting results, necessitating research that seamlessly combines deep learning methodologies with insights from neuroscience. This integration is crucial for gaining a deeper understanding of the fundamental neurophysiological mechanisms associated with brain rehabilitation.
• Ethical Concerns in Real-Time Applications: While offline analysis dominates several EEG-based brain rehabilitation techniques, the shift towards real-time applications introduces ethical considerations related to patient consent, data ownership, and privacy. Meticulous attention is required to ensure that these issues are addressed with the utmost care, respecting patients’ rights.
• Bridging the Gap Between Research and Clinical Implementation: Despite the strides made in research in neurological rehabilitation utilizing EEG signals, there remains a discernible discrepancy between academic research and the practical implementation of clinical solutions. Efforts to bridge this gap are essential for the seamless translation of research findings into real-world clinical practices.

7. Comparison of EEG Headsets for Rehabilitation Purposes with Various Datasets

8. most significant eeg- and motor imagery-based studies in the last 7 years, 9. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• McFarland, D.J.; Wolpaw, J.R. Brain-computer interfaces for the operation of robotic and prosthetic devices. In Advances in Computers ; Elsevier: Amsterdam, The Netherlands, 2010; Volume 79, pp. 169–187. [ Google Scholar ] [ CrossRef ]
• Dobkin, B.H. Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J. Physiol. 2007 , 579 , 637–642. [ Google Scholar ] [ CrossRef ]
• Dodd, K.C.; Nair, V.A.; Prabhakaran, V. Role of the contralesional vs. ipsilesional hemisphere in stroke recovery. Front. Hum. Neurosci. 2017 , 11 , 469. [ Google Scholar ] [ CrossRef ]
• Fouad, M.M.; Amin, K.M.; El-Bendary, N.; Hassanien, A.E. Brain computer interface: A review. In Brain-Computer Interfaces: Current Trends and Applications ; Springer: Berlin/Heidelberg, Germany, 2015; Volume 74, pp. 3–30. [ Google Scholar ] [ CrossRef ]
• Birbaumer, N.; Ghanayim, N.; Hinterberger, T.; Iversen, I.; Kotchoubey, B.; Kübler, A.; Perelmouter, J.; Taub, E.; Flor, H. A spelling device for the paralyzed. Nature 1999 , 398 , 297–298. [ Google Scholar ] [ CrossRef ]
• Mondal, D.; Alagirisamy, M. Brain computer interface (BCI): Mechanism and challenges–A survey. Int. J. Pharm. Res. 2020 , 12 , 2200–2208. [ Google Scholar ] [ CrossRef ]
• Wilson, J.A.; Felton, E.A.; Garell, P.C.; Schalk, G.; Williams, J.C. ECoG factors underlying multimodal control of a brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 2006 , 14 , 246–250. [ Google Scholar ] [ CrossRef ]
• Rezeika, A.; Benda, M.; Stawicki, P.; Gembler, F.; Saboor, A.; Volosyak, I. Brain–computer interface spellers: A review. Brain Sci. 2018 , 8 , 57. [ Google Scholar ] [ CrossRef ]
• Hwang, H.J.; Kwon, K.; Im, C.H. Neurofeedback-based motor imagery training for brain–computer interface (BCI). J. Neurosci. Methods 2009 , 179 , 150–156. [ Google Scholar ] [ CrossRef ]
• Remsik, A.; Young, B.; Vermilyea, R.; Kiekhoefer, L.; Abrams, J.; Evander Elmore, S.; Schultz, P.; Nair, V.; Edwards, D.; Williams, J.; et al. A review of the progression and future implications of brain–computer interface therapies for restoration of distal upper extremity motor function after stroke. Expert Rev. Med. Devices 2016 , 13 , 445–454. [ Google Scholar ] [ CrossRef ]
• Ramos-Murguialday, A.; Broetz, D.; Rea, M.; Läer, L.; Yilmaz, O.; Brasil, F.L.; Liberati, G.; Curado, M.R.; Garcia-Cossio, E.; Vyziotis, A.; et al. Brain-machine interface in chronic stroke rehabilitation: A controlled study. Ann. Neurol. 2013 , 74 , 100–108. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kitchenham, B.; Brereton, O.P.; Budgen, D.; Turner, M.; Bailey, J.; Linkman, S. Systematic literature reviews in software engineering—A systematic literature review. Inf. Softw. Technol. 2009 , 51 , 7–15. [ Google Scholar ] [ CrossRef ]
• Demers, L.; Fuhrer, M.J.; Jutai, J.; Lenker, J.; Depa, M.; De Ruyter, F. A conceptual framework of outcomes for caregivers of assistive technology users. Am. J. Phys. Med. Rehabil. 2009 , 88 , 645–655. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jackson, A.; Zimmermann, J.B. Neural interfaces for the brain and spinal cord—Restoring motor function. Nat. Rev. Neurol. 2012 , 8 , 690–699. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• López-Larraz, E.; Sarasola-Sanz, A.; Irastorza-Landa, N.; Birbaumer, N.; Ramos-Murguialday, A. Brain-machine interfaces for rehabilitation in stroke: A review. NeuroRehabilitation 2018 , 43 , 77–97. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Coscia, M.; Wessel, M.J.; Chaudary, U.; Millán, J.d.R.; Micera, S.; Guggisberg, A.; Vuadens, P.; Donoghue, J.; Birbaumer, N.; Hummel, F.C. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke. Brain 2019 , 142 , 2182–2197. [ Google Scholar ] [ CrossRef ]
• Oguntosin, V.W.; Mori, Y.; Kim, H.; Nasuto, S.J.; Kawamura, S.; Hayashi, Y. Design and validation of exoskeleton actuated by soft modules toward neurorehabilitation—Vision-based control for precise reaching motion of upper limb. Front. Neurosci. 2017 , 11 , 352. [ Google Scholar ] [ CrossRef ]
• Cheng, N.; Phua, K.S.; Lai, H.S.; Tam, P.K.; Tang, K.Y.; Cheng, K.K.; Yeow, R.C.; Ang, K.K.; Guan, C.; Lim, J.H. Brain–computer interface-based soft robotic glove rehabilitation for stroke. IEEE Trans. Biomed. Eng. 2020 , 67 , 3339–3351. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schwarz, A.; Höller, M.K.; Pereira, J.; Ofner, P.; Müller-Putz, G.R. Decoding hand movements from human EEG to control a robotic arm in a simulation environment. J. Neural Eng. 2020 , 17 , 036010. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Diekelmann, S.; Born, J. The memory function of sleep. Nat. Rev. Neurosci. 2010 , 11 , 114–126. [ Google Scholar ] [ CrossRef ]
• Meng, J.; Zhang, S.; Bekyo, A.; Olsoe, J.; Baxter, B.; He, B. Non-invasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci. Rep. 2016 , 6 , 38565. [ Google Scholar ] [ CrossRef ]
• Akbari, H.; Sadiq, M.T.; Payan, M.; Esmaili, S.S.; Baghri, H.; Bagheri, H. Depression detection based on geometrical features extracted from SODP shape of EEG signals and binary PSO. Trait. Signal 2021 , 38 , 13–26. [ Google Scholar ] [ CrossRef ]
• Sadiq, M.T.; Yu, X.; Yuan, Z.; Zeming, F.; Rehman, A.U.; Ullah, I.; Li, G.; Xiao, G. Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain–computer interfaces. IEEE Access 2019 , 7 , 171431–171451. [ Google Scholar ] [ CrossRef ]
• Sharma, R.; Kim, M.; Gupta, A. Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model. Biomed. Signal Process. Control 2022 , 71 , 103101. [ Google Scholar ] [ CrossRef ]
• Antony, J.W.; Piloto, L.; Wang, M.; Pacheco, P.; Norman, K.A.; Paller, K.A. Sleep spindle refractoriness segregates periods of memory reactivation. Current Biol. 2018 , 28 , 1736–1743. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Akbari, H.; Sadiq, M.T.; Jafari, N.; Too, J.; Mikaeilvand, N.; Cicone, A.; Serra-Capizzano, S. Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain. Bratisl. Med. J. 2022 , 124 , 12–24. [ Google Scholar ] [ CrossRef ]
• Sadiq, M.T.; Yu, X.; Yuan, Z.; Aziz, M.Z. Motor imagery BCI classification based on novel two-dimensional modelling in empirical wavelet transform. Electron. Lett. 2020 , 56 , 1367–1369. [ Google Scholar ] [ CrossRef ]
• LaFleur, K.; Cassady, K.; Doud, A.; Shades, K.; Rogin, E.; He, B. Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface. J. Neural Eng. 2013 , 10 , 046003. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wodlinger, B.; Downey, J.E.; Tyler-Kabara, E.C.; Schwartz, A.B.; Boninger, M.L.; Collinger, J.L. Ten-dimensional anthropomorphic arm control in a human brain–machine interface: Difficulties, solutions, and limitations. J. Neural Eng. 2015 , 12 , 016011. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Huang, B.; Xu, H.; Yuan, M.; Aziz, M.Z.; Yu, X. Exploiting asymmetric EEG signals with EFD in deep learning domain for robust BCI. Symmetry 2022 , 14 , 2677. [ Google Scholar ] [ CrossRef ]
• Cypher, D.; Chevrollier, N.; Montavont, N.; Golmie, N. Prevailing over wires in healthcare environments: Benefits and challenges. IEEE Commun. Mag. 2006 , 44 , 56–63. [ Google Scholar ] [ CrossRef ]
• Sarasola-Sanz, A.; López-Larraz, E.; Irastorza-Landa, N.; Klein, J.; Valencia, D.; Belloso, A.; Morin, F.O.; Spüler, M.; Birbaumer, N.; Ramos-Murguialday, A. An EEG-based brain-machine interface to control a 7-degrees of freedom exoskeleton for stroke rehabilitation. Converging Clin. Eng. Res. Neurorehabilit. II (Biosyst. Biorobot.) 2017 , 15 , 1127–1131. [ Google Scholar ] [ CrossRef ]
• Escolano, C.; Navarro-Gil, M.; Garcia-Campayo, J.; Congedo, M.; Ridder, D.D.D.; Minguez, J. A controlled study on the cognitive effect of alpha neurofeedback training in patients with major depressive disorder. Front. Behav. Neurosci. 2014 , 8 , 296. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mortenson, W.; Demers, L.; Fuhrer, M.; Jutai, J.; Lenker, J.; DeRuyter, F. Development and preliminary evaluation of the caregiver assistive technology outcome measure. J. Rehabil. Med. 2015 , 47 , 412–418. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chiang, H.S.; Hsiao, K.L.; Liu, L.C. EEG-based detection model for evaluating and improving learning attention. J. Med. Biol. Eng. 2018 , 38 , 847–856. [ Google Scholar ] [ CrossRef ]
• Iturrate, I.; Chavarriaga, R.; Montesano, L.; Minguez, J.; Millán, J.d.R. Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control. Sci. Rep. 2015 , 5 , 13893. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hamid, H.; Naseer, N.; Nazeer, H.; Khan, M.J.; Khan, R.A.; Khan, U.S. Analyzing classification performance of fNIRS-BCI for gait rehabilitation using deep neural networks. Sensors 2022 , 22 , 1932. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Buch, E.; Weber, C.; Cohen, L.G.; Braun, C.; Dimyan, M.A.; Ard, T.; Mellinger, J.; Caria, A.; Soekadar, S.; Fourkas, A.; et al. Think to move: A neuromagnetic brain–computer interface (BCI) system for chronic stroke. Stroke 2008 , 39 , 910–917. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nakagome, S.; Luu, T.P.; He, Y.; Ravindran, A.S.; Contreras-Vidal, J.L. An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding. Sci. Rep. 2020 , 10 , 4372. [ Google Scholar ] [ CrossRef ]
• Tortora, S.; Ghidoni, S.; Chisari, C.; Micera, S.; Artoni, F. Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network. J. Neural Eng. 2020 , 17 , 046011. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Martini, M.L.; Oermann, E.K.; Opie, N.L.; Panov, F.; Oxley, T.; Yaeger, K. Sensor modalities for brain-computer interface technology: A comprehensive literature review. Neurosurgery 2020 , 86 , E108–E117. [ Google Scholar ] [ CrossRef ]
• Alotaiby, T.; El-Samie, F.E.A.; Alshebeili, S.A.; Ahmad, I. A review of channel selection algorithms for EEG signal processing. EURASIP J. Adv. Signal Process. 2015 , 2015 , 66. [ Google Scholar ] [ CrossRef ]
• Wan, X.; Zhang, K.; Ramkumar, S.; Deny, J.; Emayavaramban, G.; Ramkumar, M.S.; Hussein, A.F. A review on electroencephalogram based brain computer interface for elderly disabled. IEEE Access 2019 , 7 , 36380–36387. [ Google Scholar ] [ CrossRef ]
• Pfurtscheller, G.; Neuper, C. Motor imagery and direct brain-computer communication. Proc. IEEE 2001 , 89 , 1123–1134. [ Google Scholar ] [ CrossRef ]
• Jeunet, C.; Jahanpour, E.; Lotte, F. Why standard brain-computer interface (BCI) training protocols should be changed: An experimental study. J. Neural Eng. 2016 , 13 , 036024. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Farnsworth, B. Eeg (Electroencephalography): The Complete Pocket Guide ; IMotions, Global HQ: Copenhagen, Denmark, 2019. [ Google Scholar ]
• Shute, V.J.; Zapata-Rivera, D. Adaptive technologies. ETS Res. Rep. Ser. 2007 , 2007 , 34. [ Google Scholar ] [ CrossRef ]
• Norman, S.L.; McFarland, D.J.; Miner, A.; Cramer, S.C.; Wolbrecht, E.T.; Wolpaw, J.R.; Reinkensmeyer, D.J. Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke. J. Neural Eng. 2018 , 15 , 056026. [ Google Scholar ] [ CrossRef ]
• Sadiq, M.T.; Akbari, H.; Siuly, S.; Li, Y.; Wen, P. Alcoholic EEG signals recognition based on phase space dynamic and geometrical features. Chaos Solitons Fractals 2022 , 158 , 112036. [ Google Scholar ] [ CrossRef ]
• Sadiq, M.T.; Yu, X.; Yuan, Z.; Aziz, M.Z.; Rehman, N.u.; Ding, W.; Xiao, G. Motor imagery BCI classification based on multivariate variational mode decomposition. IEEE Trans. Emerg. Top. Comput. Intell. 2022 , 6 , 1177–1189. [ Google Scholar ] [ CrossRef ]
• Yu, X.; Aziz, M.Z.; Sadiq, M.T.; Fan, Z.; Xiao, G. A new framework for automatic detection of motor and mental imagery EEG signals for robust BCI systems. IEEE Trans. Instrum. Meas. 2021 , 70 , 1–12. [ Google Scholar ] [ CrossRef ]
• Altaheri, H.; Muhammad, G.; Alsulaiman, M.; Amin, S.; Altuwaijri, G.; Abdul, W.; Bencherif, M.; Faisal, M. Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review. Neural Comput. Appl. 2023 , 35 , 14681–14722. [ Google Scholar ] [ CrossRef ]
• Escolano, C.; Navarro-Gil, M.; Garcia-Campayo, J.; Congedo, M.; Minguez, J. The effects of individual upper alpha neurofeedback in ADHD: An open-label pilot study. Appl. Psychophysiol. Biofeedback 2014 , 39 , 193–202. [ Google Scholar ] [ CrossRef ]
• Ketz, N.; Jones, A.P.; Bryant, N.B.; Clark, V.P.; Pilly, P.K. Closed-loop slow-wave tACS improves sleep-dependent long-term memory generalization by modulating endogenous oscillations. J. Neurosci. 2018 , 38 , 7314–7326. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Acı, Ç.İ.; Kaya, M.; Mishchenko, Y. Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods. Expert Syst. Appl. 2019 , 134 , 153–166. [ Google Scholar ] [ CrossRef ]
• Kamble, A.; Ghare, P.H.; Kumar, V. Deep-learning-based BCI for automatic imagined speech recognition using SPWVD. IEEE Trans. Instrum. Meas. 2023 , 72 , 1–10. [ Google Scholar ] [ CrossRef ]
• Venkatesh, B.; Anuradha, J. A review of feature selection and its methods. Cybern. Inf. Technol. 2019 , 19 , 3–26. [ Google Scholar ] [ CrossRef ]
• Atyabi, A.; Shic, F.; Naples, A. Mixture of autoregressive modeling orders and its implication on single trial EEG classification. Expert Syst. Appl. 2016 , 65 , 164–180. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Karaboga, D. An Idea Based on Honey Bee Swarm for Numerical Optimization ; Technical Report, Technical Report-tr06; Erciyes University, Engineering Faculty, Computer Engineering Department: Kayseri, Turkey, 2005. [ Google Scholar ]
• Rakshit, P.; Bhattacharyya, S.; Konar, A.; Khasnobish, A.; Tibarewala, D.N.; Janarthanan, R. Artificial Bee Colony Based Feature Selection for Motor Imagery EEG Data. In Proceedings of the Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012); Bansal, J.C., Singh, P., Deep, K., Pant, M., Nagar, A., Eds.; Springer: Gwalior, India, 2013; pp. 127–138. [ Google Scholar ]
• Proulx, C.E.; Beaulac, M.; David, M.; Deguire, C.; Haché, C.; Klug, F.; Kupnik, M.; Higgins, J.; Gagnon, D.H. Review of the effects of soft robotic gloves for activity-based rehabilitation in individuals with reduced hand function and manual dexterity following a neurological event. J. Rehabil. Assistive Technol. Eng. 2020 , 7 , 205566832091813. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tabar, Y.R.; Halici, U. A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. 2016 , 14 , 016003. [ Google Scholar ] [ CrossRef ]
• Tang, Z.; Li, C.; Sun, S. Single-trial EEG classification of motor imagery using deep convolutional neural networks. Optik 2017 , 130 , 11–18. [ Google Scholar ] [ CrossRef ]
• Dose, H.; Møller, J.S.; Iversen, H.K.; Puthusserypady, S. An end-to-end deep learning approach to MI-EEG signal classification for BCIs. Expert Syst. Appl. 2018 , 114 , 532–542. [ Google Scholar ] [ CrossRef ]
• Olivas-Padilla, B.E.; Chacon-Murguia, M.I. Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl. Soft Comput. 2019 , 75 , 461–472. [ Google Scholar ] [ CrossRef ]
• Tayeb, Z.; Fedjaev, J.; Ghaboosi, N.; Richter, C.; Everding, L.; Qu, X.; Wu, Y.; Cheng, G.; Conradt, J. Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals. Sensors 2019 , 19 , 210. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tang, X.; Zhao, J.; Fu, W.; Pan, J.; Zhou, H. A novel classification algorithm for MI-EEG based on deep learning. In Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 24–26 May 2019; pp. 606–611. [ Google Scholar ]
• Qiao, W.; Bi, X. Deep spatial-temporal neural network for classification of EEG-based motor imagery. In Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science, Wuhan, China, 12–13 July 2019; pp. 265–272. [ Google Scholar ]
• Song, Y.; Wang, D.; Yue, K.; Zheng, N.; Shen, Z.J.M. EEG-based motor imagery classification with deep multi-task learning. In Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 14–19 July 2019; pp. 1–8. [ Google Scholar ]
• Amin, S.U.; Alsulaiman, M.; Muhammad, G.; Mekhtiche, M.A.; Shamim Hossain, M. Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Gener. Comput. Syst. 2019 , 101 , 542–554. [ Google Scholar ] [ CrossRef ]
• Alazrai, R.; Abuhijleh, M.; Alwanni, H.; Daoud, M.I. A Deep Learning Framework for Decoding Motor Imagery Tasks of the Same Hand Using EEG Signals. IEEE Access 2019 , 7 , 109612–109627. [ Google Scholar ] [ CrossRef ]
• Li, F.; He, F.; Wang, F.; Zhang, D.; Xia, Y.; Li, X. A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning. Appl. Sci. 2020 , 10 , 1605. [ Google Scholar ] [ CrossRef ]
• Miao, M.; Hu, W.; Yin, H.; Zhang, K. Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network. Comput. Math. Methods Med. 2020 , 2020 , 1981728. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, J.; Ma, Z.; Wang, J.; Fu, Y. A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion. IEEE Access 2020 , 8 , 202100–202110. [ Google Scholar ] [ CrossRef ]
• Ieracitano, C.; Morabito, F.C.; Hussain, A.; Mammone, N. A hybrid-domain deep learning-based BCI for discriminating hand motion planning from EEG sources. Int. J. Neural Syst. 2021 , 31 , 2150038. [ Google Scholar ] [ CrossRef ]
• Zhang, C.; Kim, Y.K.; Eskandarian, A. EEG-inception: An accurate and robust end-to-end neural network for EEG-based motor imagery classification. J. Neural Eng. 2021 , 18 , 046014. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Deng, X.; Zhang, B.; Yu, N.; Liu, K.; Sun, K. Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces. IEEE Access 2021 , 9 , 25118–25130. [ Google Scholar ] [ CrossRef ]
• Cho, J.H.; Jeong, J.H.; Lee, S.W. NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework. IEEE Trans. Cybern. 2022 , 52 , 13279–13292. [ Google Scholar ] [ CrossRef ]
• Tiwari, S.; Goel, S.; Bhardwaj, A. MIDNN-a classification approach for the EEG based motor imagery tasks using deep neural network. Appl. Intell. 2022 , 52 , 4824–4843. [ Google Scholar ] [ CrossRef ]
• Huang, E.; Zheng, X.; Fang, Y.; Zhang, Z. Classification of Motor Imagery EEG Based on Time-Domain and Frequency-Domain Dual-Stream Convolutional Neural Network. IRBM 2022 , 43 , 107–113. [ Google Scholar ] [ CrossRef ]
• Huang, W.; Chang, W.; Yan, G.; Yang, Z.; Luo, H.; Pei, H. EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick. Expert Syst. Appl. 2022 , 187 , 115968. [ Google Scholar ] [ CrossRef ]
• Zhu, H.; Forenzo, D.; He, B. On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery. IEEE Trans. Neural Syst. Rehabil. Eng. 2022 , 30 , 2283–2291. [ Google Scholar ] [ CrossRef ]
• Mattioli, F.; Porcaro, C.; Baldassarre, G. A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface. J. Neural Eng. 2022 , 18 , 066053. [ Google Scholar ] [ CrossRef ]
• Du, Y.; Liu, J. IENet: A robust convolutional neural network for EEG based brain-computer interfaces. J. Neural Eng. 2022 , 19 , 036031. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Zhang, Q.; Chen, W. Adaptive CNN for EEG classification in stroke rehabilitation. J. Neurosci. Methods 2023 , 275 , 112–120. [ Google Scholar ]
• Wang, Z.; Liu, S.; Zhou, Y. Attention-based CNN for EEG motor imagery classification. IEEE Trans. Neural Syst. Rehabil. Eng. 2023 , 31 , 1365–1373. [ Google Scholar ]
• Zhang, L.; Wang, H.; Li, J. Graph-CNN for spatial feature learning in EEG classification. Med. Biol. Eng. Comput. 2023 , 61 , 451–459. [ Google Scholar ]
• Xu, Y.; Zhang, W.; Chen, S. Hybrid CNN-RNN model for EEG motor imagery classification. Front. Neurosci. 2024 , 18 , 127–135. [ Google Scholar ]
• Chen, L.; Liu, Q.; Yang, H. Transformer-based model for EEG classification in stroke rehabilitation. IEEE Trans. Biomed. Eng. 2024 , 71 , 1461–1470. [ Google Scholar ]

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Elashmawi, W.H.; Ayman, A.; Antoun, M.; Mohamed, H.; Mohamed, S.E.; Amr, H.; Talaat, Y.; Ali, A. A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation. Appl. Sci. 2024 , 14 , 6347. https://doi.org/10.3390/app14146347

Elashmawi WH, Ayman A, Antoun M, Mohamed H, Mohamed SE, Amr H, Talaat Y, Ali A. A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation. Applied Sciences . 2024; 14(14):6347. https://doi.org/10.3390/app14146347

Elashmawi, Walaa H., Abdelrahman Ayman, Mina Antoun, Habiba Mohamed, Shehab Eldeen Mohamed, Habiba Amr, Youssef Talaat, and Ahmed Ali. 2024. "A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation" Applied Sciences 14, no. 14: 6347. https://doi.org/10.3390/app14146347

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