Content area
Purpose
This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.
Design/methodology/approach
A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.
Findings
The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.
Originality/value
This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.
Introduction
Electronic Health Records System (EHRS) is a digital repository of medical data that can be exchanged securely and is accessible by multiple authorized users (Kanade and Kumar, 2021). EHRS assists service providers in delivering a higher quality of care to their patients (Koppel and Lehmann, 2015; Tianyang, 2019). While implementing EHRS, healthcare facilities seek to reach objectives such as improvement of physicians’ efficiency and decision-making, increased accuracy and reliability of medical data and improved patient safety (Spatar et al., 2019). Therefore, EHRS can include many potential capabilities, such as Clinical Decision Support (CDS) tools, Computerized Physician Order Entry (CPOE) and Health Information Exchange (HIE) (Menachemi and Collum, 2011). CDS tools assist the provider in making decisions concerning patient care. Some of the prominent functionalities of a CDS include providing the latest information about the drug, drug-interaction checking, adverse drug event detection such as drug-allergy alerting during medication order entry (Mccoy et al., 2015), preventive care reminders, automated clinical guidelines, evidence-based order sets, care recommendations and diagnostic support (Mandell, 2021). These functionalities provide means for healthcare to be delivered efficiently (Tetreault, 2016). Decision support is often mentioned as achievable by introducing an EHRS. The capabilities of the EHRS determine its effectiveness in supporting decision-making (Mercer et al., 2019). However, there are claims that an EHRS does not necessarily mean that decision support is effective (Rinke et al., 2016; Yanamadala et al., 2016; Zhou et al., 2009).
Despite EHRS’s benefits, healthcare facilities in low and middle-income countries (LMICs) face significant difficulties implementing the systems (Kumar and Mostafa, 2020). Although not all EHRS go unutilized, most are not effectively implemented to provide the abundantly envisaged need for improving healthcare practices (Fennelly et al., 2020), particularly decision support. Elsewhere in developed countries, things are different. In 2017, it was estimated that about 98.3% of hospitals in the United States of America had EHRS implemented, of which 55.6% had advanced CDS capabilities. Kose et al. (2020) report that 63.1% of hospitals in Turkey have adopted basic EHRS, and 36% have implemented EHRS with decision-support capabilities, which compares favorably to the results of Korean Hospitals. In Korea, a study by Park and Han (2017) found that 89.2% of hospitals were equipped with comprehensive EHRS systems, with a significant proportion featuring sophisticated decision-support functionalities.
It is asserted that contextual issues are the primary cause of implementation failures for health information systems (HIS) in general (Ahmadian et al., 2014; Boddy et al., 2009; Kimiafar, 2016). Some evidence suggests that EHRS implementation success may be determined by organizational and human factors rather than only technical factors (Fennelly et al., 2020). Katurura and Cilliers (2018) report that the barriers to implementing EHRS include a lack of framework for implementing and managing EHRS. Therefore, it is imperative to thoroughly examine both successful and unsuccessful implementations to grasp the potential advantage of EHRS in decision support. The in-depth analyses of how different EHRS have been used and adopted in healthcare facilities can then assist in developing a framework for future implementations.
Existing literature lacks a comprehensive exploration of contextual factors influencing the implementation of decision support models in the EHRS domain. The gap is caused by the lack of a systematic review that identifies strengths, weaknesses and opportunities for innovation while critically analyzing and synthesizing the many contextual implementation models. In order to close this gap, this study provides a comprehensive and current analysis that offers insightful information to researchers, practitioners and policymakers looking to improve decision support in the quickly changing EHRS ecosystem.
Thus, this study systematically assesses previously developed EHRS implementation models and identifies successful models for decision support. Although this study confirms some of the previous research findings, it stands out for identifying complexities and compiling data related to the implementation of EHRS. The study clarifies underrepresented strategies and models, focusing on how they might be applied in various healthcare settings, including those with few resources. This study contextualizes and applies these insights in a way that meets the needs of healthcare facilities that are under pressure to provide patient care that is both innovative and efficient.
Methods
The systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Google Scholar, Scopus and PubMed. The systematic review focused on identifying the contextual models for implementing EHRS to support health facility decision-making. The study targeted peer-reviewed journals published in the English Language from January 2012 to April 2022 that addressed the implementation of EHRS with consideration of decision-support capabilities in healthcare. The time range was chosen because technological innovations evolve quickly. Therefore, exploring more than ten years of technological setup would not be appropriate.
The review was expressed around a search string consisting of search terms and relevant keywords in titles, abstracts or publications. Using Boolean operators AND and OR, the following search terms were used: “Electronic Health Records,” “Contextual Implementation,” “Decision Support,” and “Models.”
Duplicates studies identified from the selected databases were loaded into Mendeley Software and removed. The screening process was done based on the titles and abstracts, and then full texts were read to determine the articles to be included in the systematic review. Articles that passed the eligibility criteria were selected for inclusion in the review. In addition, the reference lists of the articles that met the inclusion criteria were manually reviewed to see if any study met the established inclusion criteria and was not identified in the initial search.
The data extracted from each article included study identification information such as authors’ names and year of publication, study characteristics like research setting, study design and information related to the EHRS investigated, including the type of EHRS and the model used for its implementation. The researchers evaluated the articles, and disagreements were resolved through discussion.
During the data extraction phase, disagreements among the researchers were settled through a consensus-driven methodology. Team meetings were used to discuss the discrepancies until a consensus was reached. During an ongoing dispute, a third party with experience implementing EHRS was consulted to offer a neutral opinion. This method guaranteed the data’s accuracy and consistency, enhancing the conclusions’ validity.
Results
Description of studies
Results of the search
The electronic database searches yielded 1,702 potentially relevant documents. Of these 1,702 documents, 695 were duplicate hits, eliminated from further consideration. The titles and abstracts of 1,007 documents were reviewed to determine potential relevance, excluding 938 due to irrelevance to the review. Sixty-nine full-text documents were obtained, reviewed and formally excluded 49 (21 had inappropriate study design, 18 had inconsistent findings and 10 were from unreliable sources). Twenty studies (20 reports) met all eligibility criteria and were included in the review. Figure 1 illustrates the flow of studies through the systematic review process.
Included studies
Examining the full texts of 69 potentially relevant reports resulted in 20 studies. While studies found various effective models for implementing EHRS to support decision-making, there were significant differences across these studies. These differences are described in Table 1, which lists the characteristics of the studies included in the research. So, even though there are successful EHRS implementation models for decision, there is no one-size-fits-all approach, and context is important.
Study design
Of the 20 included studies,1 theory of change (Adedeji et al., 2022), 2 were a randomized controlled trial (Moja et al., 2016a, b), 2 were Mixed-methods (Gold et al., 2019; Zhai et al., 2022),3 Case study (Bossen et al., 2013; Cresswell et al., 2012; Grenha Teixeira et al., 2019) and 10 were cross-sectional studies (Ahmed et al., 2020; Bashiri et al., 2023; Erlirianto et al., 2015; Iqbal et al., 2013; Jain et al., 2022; Kroth et al., 2019; Lee et al., 2022; Maillet et al., 2015; Özkara et al., 2021; Sheehan et al., 2013).
Location of studies
As shown in Table 1, studies on implementing EHRS for decision support have been carried out in many countries. The study sites include Argentina, China, Denmark, England, Ethiopia, India, Indonesia, Iran, Italy, Nigeria, Portuguese, Taiwan, Turkey and the United States of America. However, developed nations have hosted most of the studies on this subject. For instance, two investigations were carried out in Italy, two in the United States of America and six in the developed countries of Argentina, China, Denmark, England, Portugal and Turkey. It is also important to remember that industrialized nations like the United States, England and Italy exist. However, studies from developing countries, such as Indonesia, India and Taiwan, were comparatively underrepresented. This trend aligns with the general trend in health informatics research, favoring developed countries. This could be attributed to the fact that more infrastructure and resources are available in developed countries to facilitate the use of EHRS and the carrying out of related research. The literature suggests that developed countries are where EHRS implementation for decision support is more common (Adler-Milstein et al., 2015; Kose et al., 2020; Takian et al., 2014)
Outcome measures
Primary outcome measures
Elements of EHRS implementation models that had been documented in the literature, such as stakeholder involvement, customization, stages of implementation and the impact on healthcare business processes and patient outcomes.
The review of the literature made clear how crucial it is for both clinical and non-clinical staff, patients and external stakeholders, including vendors and regulators, to be involved in the implementation of EHRS across all of these models (Cresswell et al., 2012; Irizarry and Barton, 2013; Sheehan et al., 2013). Along with efficient employee training and support, the availability of technical assistance and ongoing maintenance were mentioned as crucial elements for a successful EHRS deployment (Cresswell et al., 2012).
Moreover, according to the literature, there were differences between EHRS implementation models in terms of their implementation timescales, their implementation scope and their amount of customization. A phased adoption strategy in which the EHRS system is gradually introduced to various departments or units over time was chosen by some healthcare organizations (Gold et al., 2019). Other healthcare organizations favored a big-bang strategy, introducing the complete system simultaneously.
Additionally, the literature review highlighted the necessity of employing efficient change management techniques when implementing EHRS. This entails engaging with stakeholders, communicating with them and educating and assisting personnel in adjusting to the new system. Key barriers to a successful EHRS adoption were resistance to change and a lack of managerial support.
Secondary outcome measures
The study identified the EHRS implementation models healthcare organizations use and the critical factors contributing to their success. It also examined the similarities and differences between successful EHRS implementation models identified in the literature as secondary outcome measures. This analysis aimed to identify common characteristics that contributed to these models’ success and any differences that might have impacted their performance.
Several outcome measures were used to test the effectiveness of EHRS deployment models for decision-making. These metrics considered improvements in patient care, such as fewer medication errors and improved clinical outcomes, as well as other factors, such as the time required for drug administration and the length of hospital stay. Patient and physician satisfaction, clinical decision-making and overall healthcare quality were important factors.
A literature review revealed that effective EHRS implementation models had several characteristics, such as significant stakeholder involvement, comprehensive staff training and support and adopting standardized procedures and processes. In addition, successful models generally strongly valued interprofessional communication, collaboration and patient-centered treatment.
However, there were some differences across implementation models for EHRS. For instance, although some models were designed exclusively for specific types of healthcare facilities, such as hospitals or primary care clinics, others were more broadly applicable across various situations. The degree of customization offered by the EHRS was a crucial factor. Some models highlighted using commercially available technologies, while others emphasized the need for customization to fit the organization’s requirements.
EHRS contextual implementation models
Included studies examined several context-specific EHRS implementation approaches that can aid in offering efficient decision support in the healthcare industry. Studies have shown that successful implementation of EHRS for efficient decision support requires a holistic approach that considers social and technological factors (Dhillon-chattha et al., 2018; Mwachofi et al., 2016). Studies have shown that contextual implementation models like the Sociotechnical Model, the Information Systems Success Model (ISSM), the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (UTAUT) can be used to design and implement EHRS in a way that supports best practices and enhances patient outcomes. The Theory of Planned Behavior (TPB), Health Information Technology Acceptance Model (HITAM), Theory of change (ToC), Implementation science and Stepped Wedge Implementation were among the additional models found in the review.
Sociotechnical model for EHRS implementation
The sociotechnical model acknowledges that any technological system exists within a social environment and that technical and social factors affect the system’s success. The sociotechnical approach contends that the early involvement of healthcare providers in EHRS design and implementation is essential. EHRS can be developed to support healthcare professionals’ workflows and work practices rather than requiring them to change to fit the system by including them in the design process. This strategy increases the likelihood that healthcare providers will adopt the EHRS by ensuring it fits the current organizational culture and work habits.
Irizarry and Barton (2013) address the difficulties in deploying EHRS in healthcare facilities and suggest the sociotechnical systems theory as a framework to address these challenges. The paper presents five best practice recommendations for effective EHRS implementation and places special emphasis on the function of clinical nurse specialists (CNSs) throughout the procedure. According to this study’s recommendations, a thorough needs analysis should be done initially. Before adopting an EHRS, it is necessary to ascertain the specific needs of the healthcare facility, the potential advantages of doing so and any challenges that might emerge. The EHRS implementation procedure must involve all stakeholders, including clinicians, administrators, IT personnel and patients. This can help to build support for the system and ensure that it meets the needs of all stakeholders. Due to the complexity of EHRS deployment, offering thorough training and continuous assistance is crucial to ensure staff members are at ease using the new system. The study also demonstrates how workflow process modifications are frequently needed when implementing an EHRS. To ensure efficiency and effectiveness, it is crucial to thoroughly analyze how the technology will be incorporated into current processes and make the appropriate adjustments. A last recommendation stipulated in the studies is to routinely examine and upgrade the system because EHRS technology is continuously changing. Doing so will help to keep the system current and functional.
In three hospitals in England, Cresswell et al. (2012) researched the implementation of EHRS. The study discovered that the hospitals had comparable technical and political challenges in implementing the EHRS despite variations in local implementation strategies. Contextual factors influenced how the hospitals and stakeholders responded. The study recommends that, before addressing wider interoperability, national implementation efforts should allow for efficient local technology uptake. Individual consumers and organizations should be given enough time to adapt to the intricate changes frequently accompanying service re-design initiatives.
Using an EHRS in a primary care practice was studied by (Alanazi et al., 2022). The study did not explicitly mention the sociotechnical model but investigated the human factors influencing the adoption of EHRS in primary healthcare. The authors discovered that the ability of the EHRS to integrate with the current working procedures of the healthcare providers was connected to the system’s ability to be implemented successfully. The authors contend that a key to ensuring the success of EHRS is to consider the social context in which they are implemented.
Kroth et al. (2019) highlight the importance of involving healthcare professionals in developing and implementing EHRS. The study argues that involving clinicians in the design of EHRS and ensuring that systems are customized to meet the needs of particular practices are two ways to promote the acceptance and utilization of these systems. The study demonstrated a correlation between greater physician stress and burnout and specific EHR design and use parameters.
Stepped wedge implementation model
In a Stepped Wedge implementation, the EHRS with decision-support features is implemented gradually (Moja et al., 2016a, b). For instance, in the first stage, the system might be implemented in one hospital department, then in the second, it might be expanded to another. By collecting information on patient outcomes before and after each phase’s introduction, experts will be able to assess the impact of the EHRS on patient care. In the paper by Moja et al. (2016a, b), the intervention (CDSS) was gradually introduced to participants over a range of periods to facilitate security controls and successful implementation. All participants eventually received the intervention in this implementation. However, the order in which they did not follow a random process. The individuals were split into distinct groups, each receiving the intervention differently. When it was impractical or unethical to exclude any individuals from the intervention, this design was helpful because it allowed for the evaluation of the intervention’s efficacy over time.
Using EHRSs with decision support tools in a stepped wedge implementation can be a good strategy to assess how this technology affects medical outcomes (Moja et al., 2016a, b). Healthcare professionals can get accustomed to utilizing and integrating the tools into their workflow by progressively implementing the system in stages. Researchers can gather data to determine the impact on patient care (Gold et al., 2021).
Implementation science
Although not presented as a model, Implementation science focuses on understanding the factors influencing the adoption, implementation and sustainability of innovations in healthcare, including EHRSs. The field emphasizes the importance of considering context, stakeholders and implementation strategies in designing and implementing interventions.
Studies indicate that implementation science approaches have been used to identify key contextual factors that influence the successful implementation of EHRS. For example, a study by Fragidis and Chatzoglou (2018) identified the importance of stakeholder engagement, user-centered design and clear communication in successfully implementing EHRS. The paper discusses the findings of a survey conducted among 13 experts from various countries to determine the challenges and factors contributing to the success and best practices associated with adopting EHRS. According to the authors, the effectiveness of the implementation depends on how well the new EHRS’s functionality matches users’ needs. The study also addresses a few of the experts’ success factors, such as strong leadership, excellent communication and end-user participation in the design and implementation process. A thorough needs analysis, a complete implementation plan and continuing user training and support are just a few of the experts’ best practices for EHR implementation.
HOT fit framework
The Human, Organization and Technology (HOT) fit framework is a useful model for understanding how adopting and using technology, such as EHRS with decision support tools, can be influenced by factors related to human behavior, organizational culture and the technology itself. The framework emphasizes the importance of aligning the technology with organizational goals and workflows to ensure users accept and adopt it. Several studies have used the HOT fit framework to guide the implementation of EHRSs. For example, a study by Erlirianto et al. (2015) employs the Human, Organization and Technology-Fit (HOT-Fit) framework to analyze the implementation of an Electronic Medical Record System(EMRS) in a hospital. According to the study, a hospital’s adoption of new technology depends mostly on organizational and human factors. In particular, the environmental dimension in the organization aspect of the project has a positive and significant impact on net benefits, and the information quality and service quality dimensions in the technology aspect of the project both have a positive and significant impact on user satisfaction and the human aspect. The organization aspect’s structure and environment dimensions have a positive and important influence on one another. The study’s findings imply that the HOT-Fit framework can be used to assess the implementation of HIS in hospitals.
Another study by Zhai et al. (2022) used the HOT-Fit framework to look into how nurses felt about the new nursing information system. The results support the Hot-fit framework’s assertion that it is crucial to take user, organization and technology fit into account in order to improve EHRS adoption and user experience. This framework emphasizes the necessity of effective collaboration between clinicians, administrators and technical staff during system promotion to improve system usability and user experience. Another feature of the HOT-Fit framework highlighted in the study is the requirement for top-level management support and clear communication of organizational missions to workers to promote system adoption.
By ensuring that the technology is compatible with the requirements and goals of healthcare providers and the institution, the HOT-Fit framework may guide the implementation of EHR systems with decision support tools. This may entail conducting a needs analysis to determine the precise features and functionalities healthcare practitioners value the most. It may also ensure that the technology is user-friendly and simple to incorporate into current workflows and clinical processes. Healthcare organizations can maximize the potential advantages of this technology for enhancing patient care and outcomes by adopting a holistic approach that considers the human, organizational and technological factors that can affect the adoption and use of EHRSs with decision support tools.
The theory of change
The Theory of Change (ToC) is a framework for organizing, implementing and reviewing interventions or programs. It is a thorough approach that details the precise actions or interventions that produce the intended consequences of an intervention. ToC offers a framework for identifying the actions or interventions that must be taken to realize the desired results of decision support in EHRs.
Adedeji et al. (2022) employed the ToC approach to guide the pilot study at Festac Primary Health Centre in Lagos, Nigeria, and to determine the requirements required to achieve the project’s long-term goal. The study suggested a general ToC map with assumptions about sustainability and other relevant factors that implementers in LMICs may utilize to implement an effective EHRS. According to the study, using the ToC as a framework for planning, evaluating, learning and reflecting is a satisfying way to frame stakeholder conversations. According to the study’s hypothesis, future primary care health IT implementations could modify the ToC strategy to fit their contexts depending on inherent characteristics.
Service model
The Service model strongly emphasizes how crucial it is to develop systems that satisfy users’ demands. The system must be user-friendly and suit their needs. Hence, the model highlights the importance of including end users in the design process. The Service model has been utilized in numerous studies to guide the implementation of EHRSs. For example, a study by Borbolla et al. (2010) provides information on a feasibility study by Hospital Italiano de Buenos Aires to assess the implementation of a CDSS integrated with its EHRS. The study was divided into three phases: end-user acceptance testing, system design and implementation and system performance and accuracy validation. According to the study, it is possible to implement CDSS utilizing a service-based model. The authors integrated the CDSS with the hospital’s EHRS via the SEBASTIAN CDS web service. The study showed that this model could be successful in raising healthcare quality.
Another study by Grenha Teixeira et al. (2019) demonstrates how the development and implementation of EHRS can be successful when using a service design approach. Service design can assist in understanding user demands and context by taking a human-centered, participative, creative, visual and holistic approach. It can also include users in the design and co-creation process. This strategy can promote user acceptance during implementation and help create and adopt EHRS successfully. According to the study, the service design approach, which includes the visual models and tools used throughout the various design stages, was crucial in helping to conceive new EHRS concepts and build the system to improve the experience of health providers. The study presents a case study of how the Portuguese National EHRS was created and put into use utilizing a service modeling approach. Over 170 participants participated in the study’s interviews and interactive design workshops. The service design approach made it easier to imagine new EHRS ideas and develop the system to improve the experience of healthcare users. Following deployment, the EHRS was seen as helpful and simple to use, and it was widely used. Overall, the article demonstrates how a service design approach can support the effective creation and adoption of EHRS.
In general, the service model for putting EHRS in place should be customized to the particular requirements and objectives of the healthcare organization. Healthcare organizations can utilize the potential advantages of this technology for enhancing patient care and outcomes by adopting a complete strategy that includes needs assessment, system selection, implementation, monitoring and evaluation.
Information systems success model
The Information Success model used in many reviewed papers is the DeLone and McLean model, a popular and validated model for evaluating the success of information systems. The model proposes six interrelated constructs of information systems success: system quality, information quality, service quality, (intention to) use, user satisfaction and net benefits. System quality refers to the desirable features and overall support that a system provides, while information quality is defined as the quality of the information provided by the system. The model is widely used in research to evaluate the success of information systems in various domains (Bashiri et al., 2023).
The DeLone and McLean framework for evaluating the success of information systems provided as the basis for the Bossen et al. (2013) evaluation study of the EHRS. The framework suggests seven dimensions: usage, intention to use, information quality, system quality, service quality, user satisfaction and net benefits. According to the study, staff had excellent experiences with the EHR’s operational reliability, response speed, login and support, which are signs of a high-quality system and service. However, there have been calls for enhancements to some features, including the patient administration module and the creation of the overview of data that is relevant to professionals, which may impact the accuracy of the information and user satisfaction. The evaluation provides insights regarding the use and intent to use the EHRS, which are crucial in determining benefits.
Another paper by Özkara et al. (2021) evaluated the success of an HIS used by healthcare workers in Usak Training and Research Hospital. The researchers used a model to assess the system’s success based on system quality, information quality, user satisfaction, system usage and net benefit. The study found that improving the system and information quality increased user satisfaction and system usage, which led to positive perceptions about the voluntary use of the system and its benefits.
The information systems success model highlights the EHRS’s usability and the value clinicians and other stakeholders place on it (Bossen et al., 2013; Petter et al., 2008). The issues covered in this are the system’s effects on clinical procedures, patient care and organizational performance. Organizations can use this model to evaluate the usability and usefulness of an EHRS and the perception of value by clinicians and other stakeholders to guarantee the success of an EHR implementation. This can assist medical facilities in identifying issue areas and implementing the required changes to ensure that the EHRS satisfies the requirements of all users.
UTAUT model
The user acceptance of information systems is the main emphasis of the unified Theory of acceptance and use of technology (UTAUT) model. According to this model, users should have a favorable opinion of the system and find it easy to use and useful. The system should also outperform existing systems and be compatible with users’ current working procedures. Performance expectancy, effort expectancy, social influence and facilitating conditions are the four main characteristics the UTAUT model identifies impacting user acceptance and use of EHRS. The model also suggests that personal traits like gender, age and experience impact user acceptability and use behavior.
Performance expectancy is the belief the user holds that the EHRS will enhance their ability to perform their job. The user’s view of the system’s usability is called effort expectancy. Social influence is the user’s view of how peers, such as coworkers and managers, may encourage or discourage them from using the technology. The resources and assistance the user has access to make the system easier to use, such as technical help and training, are referred to as facilitating conditions.
The UTAUT model also emphasizes the significance of users’ attitudes toward the system, arguing that users should have a favorable attitude toward the system to encourage adoption and usage. Along with the requirement that the system outperforms the existing system, compatibility with users’ current work routines is also stressed.
The UTAUT model argues that effective adoption of EHRSs necessitates considering user perceptions, attitudes, current work patterns and technological issues. Health organizations can raise the possibility that EHRS will be adopted and used successfully by concentrating on these areas.
Numerous studies have been conducted to validate the UTAUT model and provide evidence for its effectiveness in predicting user acceptance and use of EHRSs. For example, a study by Iqbal et al. (2013) aimed to measure the relationship between usage intention and adoption behavior of electronic health records (EHRs) among primary healthcare physicians in Taiwan. The results showed that the intention to use EHRS, perceived usefulness and ease of use were key factors influencing EHRS adoption. Physicians with higher intention, perceived usefulness and ease of use were likelier to adopt EHRS. The study suggests that the government should promote the potential benefits of EHR and enhance physicians’ willingness to adopt EHRs at their clinic practices. Additionally, the study found that solo and group practice physicians were likely to adopt EHRs in Taiwan.
Another study by Ahmed et al. (2020) used the UTAUT model to assess the determinants of healthcare providers’ intention to use EMRs. The study’s results showed that perceived usefulness, ease of use, computer self-efficacy and facilitating conditions were significant predictors of healthcare providers’ intention to use EMRs, consistent with the UTAUT model.
These studies prove that the UTAUT model is useful for predicting user acceptance and use of EHRSs. By considering the factors identified in the model, healthcare organizations can better understand and address the factors that influence user acceptance and use behavior, ultimately leading to more successful implementation and use of EHRSs.
Discussion
A thorough and context-specific approach considering social and technological issues is essential for implementing EHRS in healthcare facilities. Healthcare organizations should use best practices and methods from different deployment methodologies to maximize decision-support capabilities and improve patient care outcomes. It is crucial to undertake a comprehensive needs analysis for any healthcare facility and comprehend its particular requirements, obstacles and objectives. Involving stakeholders—including patients, administrators, IT workers and healthcare professionals—is essential to a successful deployment because it promotes shared accountability and ownership. Providing extensive training and ongoing support for healthcare professionals is vital, ensuring their comfort and proficiency in using the EHRS. Important tasks include adjusting workflow processes to match the system and performing frequent updates to stay updated with technology improvements. Healthcare organizations can improve the adoption of EHRS by collecting feedback, continuously monitoring and evaluating user acceptance and usage trends and better aligning the system with user needs and preferences. This all-encompassing strategy seeks to increase workflow efficiency, strengthen decision-support capabilities, efficiently coordinate patient care and ultimately improve health outcomes for the Tanzanian population covered by the PHC institution.
Healthcare facilities investing in EHRS technology can make well-informed decisions by using the identified models or developing other models from these models as a guide. By considering these models and approaches, designers and implementers of EHRS can develop strategies tailored to their organization’s specific needs and context, increasing the likelihood of successful implementation and adoption of the technology. For example, Ebnehoseini et al. (2022) proposed an extended ISSM framework using the ISSM, TAM3, Task Technology Fit and HOT-FIT to determine the EHRS success rate and explore the effective factors contributing to EHRS success. This comprehensive approach, incorporating various models, enhances the ability of healthcare facilities to navigate the complexities of EHRS technology investments and optimize their systems for success in the evolving landscape of healthcare IT.
The review highlighted the importance of a holistic approach to designing and implementing EHRSs. Technical factors such as system functionality, usability, interoperability and security are important considerations, but so are social factors such as the attitudes and beliefs of clinicians, organizational culture and workflows. By using these contextual implementation models, healthcare providers can ensure that EHRS are designed and implemented in a way that aligns with best practices and improves patient outcomes.
Having a thorough understanding of user acceptance trends, sociotechnical factors and the value of stakeholder involvement can help develop EHRS implementation techniques that are less expensive. By delving into user acceptance trends, healthcare organizations can identify patterns and preferences among end-users, allowing for the customization of EHRS interfaces and functionalities to align with user needs. This targeted approach reduces the likelihood of resistance among healthcare professionals, potentially minimizing costly implementation challenges associated with low user adoption, incorporating insights from the sociotechnical model, as highlighted by Govers and van Amelsvoort (2023) ensures that technical and social aspects are considered in the design and implementation processes. This all-encompassing strategy improves the system’s usability and considers sociocultural limitations in healthcare environments, possibly preventing expensive disruptions and rework because of misalignment.
In addition, the review highlights the importance of collaboration among stakeholders in the design and implementation of EHRS. Clinicians, IT professionals and other stakeholders must work together to ensure that the system meets the needs of all parties involved. This requires ongoing communication and feedback to refine the system and ensure that it continues to meet the needs of healthcare providers and patients. Stakeholder involvement, as emphasized in the models by various studies, including Acharya and Werts (2019), is a key driver in successful EHRS implementation. Engaging healthcare professionals, administrators, IT staff and patients ensures diverse perspectives are considered, leading to more informed decisions throughout the implementation journey. This collaborative approach fosters a sense of ownership and buy-in and mitigates the risk of costly misalignments between EHRS functionalities and stakeholders’ needs.
Furthermore, understanding the sociotechnical landscape described by the models aids in identifying cost-effective solutions that resonate with both technological requirements and human factors. For instance, the service model, as discussed by Borbolla et al. (2010) and Grenha Teixeira et al. (2019), underscores the importance of developing EHRS systems that cater to users’ demands. By actively involving end-users in the design process, healthcare organizations can reduce the likelihood of costly post-implementation modifications and enhance the system’s overall effectiveness.
Developers and technology suppliers can successfully use these insightful findings to improve the functionality and design of EHRS products. Through the careful integration of user-centric methodologies—highlighted by models like UTAUT and the Service Model—commercial EHRS can be finely tuned to match the unique requirements of end users precisely. This calculated strategy guarantees the best possible user experience, significantly boosts market acceptance and broadens the utilization of EHRS systems.
Educational institutions can ensure that aspiring healthcare professionals are well-equipped for the efficient rollout of EHRS by incorporating lessons learned from these models into their curricula. This pedagogical integration goes beyond technical components, encompassing a comprehensive understanding of sociotechnical dynamics and user acceptance variables highlighted in the studied models. By emphasizing these crucial aspects, educational programs can play a pivotal role in preparing the next generation of healthcare professionals, fostering technical proficiency and improved awareness of the multifaceted aspects involved in successfully implementing and adopting EHRS.
Policymakers can use these findings to create regulations that support ongoing training initiatives, support interoperability standards and encourage stakeholder participation. This will successfully allay worries about data sharing between various EHRS platforms. Furthermore, by considering the contextual factors that impact successful local implementation, policymakers can learn from the Stepped Wedge Implementation model, which emphasizes the importance of a phased rollout of EHRS. This informed approach empowers policymakers to craft regulations that support the seamless integration of EHRS and align with the nuanced dynamics of specific healthcare environments, ultimately creating a robust and well-functioning electronic health record ecosystem.
Conclusion
A comprehensive strategy considering technological and social elements is necessary for effective decision support in EHRS. Healthcare professionals may make sure that EHRS are developed and implemented in a way that promotes best practices and improves patient outcomes by employing contextual implementation models like the sociotechnical model, the information systems success model and UTAUT. Effective implementation is affected by various issues, such as the requirement to promote evidence-based practice, design for end users, ensure compatibility and address patient privacy and data security issues.
There is a substantial research deficit regarding low- and middle-income countries (LMICs), like Tanzania, even though there is a vast literature on EHRS and their successful implementation for efficient decision support. While there has been considerable study on adopting EHRs in LMICs, much of it has concentrated on the technical aspects of adoption, such as the difficulties in adopting EHRs in settings with low resources.
However, research on the contextual factors particular to LMICs may affect the development and implementation of EHRS for effective decision support is lacking. For instance, in Tanzania, cultural or socioeconomic factors may influence the adoption and use of EHRSs, and there may be particular challenges with the system’s management and maintenance.
Additionally, studies are needed to determine how to adapt UTAUT, the sociotechnical model, the information systems success model and other contextual implementation models to LMICs like Tanzania. Since these models were devised in high-income countries, how well they will work in LMICs is unknown. Research must discover the relevant factors to consider when developing and implementing EHRS in these contexts and modify current implementation models accordingly.
Figure 1
Systematic review process
[Figure omitted. See PDF]
Studies characteristics
| No | Study | Study design | Location of the study | EHRS implementation model |
|---|---|---|---|---|
| 1 | Cresswell et al. (2012) | Case study | England | Sociotechnical |
| 2 | Irizarry and Barton (2013) | Literature Review | Sociotechnical | |
| 3 | Iqbal et al. (2013) | Cross-sectional | Taiwan | TAM and TPB model |
| 4 | Bashiri et al. (2023) | Cross-sectional | Iran | Information Success Model (DeLone and McLean model) |
| 5 | Moja et al. (2016a, b) | Randomized controlled trial | Italy | Stepped wedge implementation |
| 6 | Lee et al. (2022) | Cross-sectional | United States of America | clinical adoption (CA) framework |
| 7 | Ahmed et al. (2020) | Cross-sectional | Ethiopia | UTAUT |
| 8 | Bossen et al. (2013) | Case study | Denmark | Information Success Model (DeLone and McLean model) |
| 9 | Özkara et al. (2021) | Cross-sectional | Turkey | Information Systems Success |
| 10 | Sheehan et al. (2013) | Cross-sectional | Sociotechnical | |
| 11 | Erlirianto et al. (2015) | Cross-sectional | Indonesia | Human, Organization and Technology-fit (HOT-fit) framework |
| 12 | Jain et al. (2022) | Cross-sectional | India | UTAUT |
| 13 | Maillet et al. (2015) | Cross-sectional | UTAUT | |
| 14 | Gold et al. (2019) | Mixed-methods | Indonesia | Stepped wedge implementation |
| 15 | Kroth et al. (2019) | Cross-sectional | United States America | Sociotechnical |
| 16 | Moja et al. (2016a, b) | Randomized controlled trial | Italy | Stepped wedge implementation |
| 17 | Zhai et al. (2022) | Mixed-methods | China | HOT-fit framework |
| 18 | Adedeji et al. (2022) | Theory of change approach | Nigeria | Theory of change (ToC) |
| 19 | Borbolla et al. (2010) | Feasibility study | Argentina | Service Model |
| 20 | Grenha Teixeira et al. (2019) | Case study | Portuguese | Service Model |
Source(s): Authors work
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