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Beyond TAM and UTAUT: Future directions for HIT implementation research
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Please cite this article as: Shachak, A., Kuziemsky, C., Petersen, C., Beyond TAM and UTAUT: Future directions for HIT implementation research, Journal of Biomedical Informatics (2019), doi: https://doi.org/ 10.1016/j.jbi.2019.103315
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Beyond TAM and UTAUT: future directions for HIT implementation research Aviv Shachak1,2, MSc, PhD, Craig Kuziemsky3, PhD, Carolyn Petersen4,, MS, MBI, FAMIA 1. Institute of Health Policy, Management and Evaluation (Dalla Lana School of Public Health), University of Toronto, Toronto, ON Canada 2. Faculty of Information, University of Toronto, Toronto ON Canada 3. Associate Vice-President, Research, MacEwan University, Edmonton, AB Canada 4. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States Corresponding Author: Dr. Aviv Shachak Institute of Health Policy, Management and Evaluation, University of Toronto 155 College St., Toronto, ON Canada M5T 3M6 Phone: 1-416-978-0998 Fax: 1-416-978-7350 Email: [email protected] Keywords: Technology Acceptance Model; Unified Theory of Acceptance and Use of Technology; Complexity Science; Technology Use; Health Information Technology Implementation Word count: 2684
ABSTRACT The Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) have been used widely in studies of health information technology (HIT) implementation. However, TAM and UTAUT have also been criticized for being overly simplistic (TAM) and for taking a narrow perspective, which focuses only on individual adopters’ beliefs, perceptions and usage intention. Furthermore, with thousands of studies using these theories, their contribution to knowledge has reached a plateau. In this commentary, we discuss some of the criticism of TAM and UTAUT, and argue that biomedical informatics research would benefit from shifting attention from these theories to multi-dimensional approaches that can better capture the complexity of issues surrounding implementation and use of HIT. We propose a number of future undertakings which, in our opinion, are more likely to move the field forward.
INTRODUCTION Adoption and implementation of health information technology (HIT) have been the focus of much research in biomedical informatics. Two of the most commonly used theories for this research are the Technology Acceptance Model (TAM) [1, 2] and the Unified Theory of Acceptance and Use of Technology (UTAUT).[3] Both TAM and UTAUT suggest that actual use of technology is affected by one’s behavioral intention to use it. In TAM, intended use is determined by attitude toward using the technology, which in turn is determined by two perceptions of the system: perceived usefulness and perceived ease of use. Various external factors affect both perceptions. UTAUT builds on TAM, as well as seven other theoretical frameworks. It proposes four constructs that affect usage intention—performance expectancy, effort expectancy, social influence and facilitating conditions. Age, gender, experience, and voluntariness of use mediate the impact of these expectancies and facilitating conditions on intention.[3] Both TAM and UTAUT have been used extensively in Management Information Systems (MIS) and biomedical informatics,[4] and many modifications and adaptations of these theories have been proposed over the years. This includes, for example, adding constructs from other theories and modifications to account for specific applications such as telemedicine or patients’ adoption of eHealth and mHealth applications.[5, 6] However, these theories (especially TAM) have also been widely criticized. This perspective article will present some of the criticism of TAM and UTAUT and then argue that it is time for biomedical informatics research to move away from TAM and UTAUT and focus their efforts on a broader array of implementation issues. We conclude with potential future directions for health information technology (HIT) implementation research. CRITICISM OF TAM AND UTAUT In MIS, the discipline from which it originated, criticism of TAM arose more than a decade ago.[7] Perhaps the most common criticism of TAM is its over-simplicity. In many studies, the model is reduced to three constructs only: perceived usefulness, perceived ease of use, and usage intention, which makes outcomes such as intended or perceived use become the end-point rather than actual use of the technology being studied. While this simplicity makes TAM a useful tool 1
to gauge the acceptability of new technologies in a ‘quick and dirty’ manner, [8] or assess the needs of different user groups, it lowers its explanatory power and provides little insight on actual HIT usage. UTAUT and its modifications attempt to overcome this limitation by incorporating additional constructs such as social influence, facilitating conditions, and habit, as well as mediating individual characteristics (age, gender, and experience).[3, 9] However, TAM, UTAUT, and related models often take a somewhat narrow perspective on diffusion and use of information and communication technology (ICT) in that they adopt a Social Psychology view, which focuses on the individual adopter (i.e. user) and assumes a direct causal influence of intention on actual behavior—the roots of this view can be traced back to Fishbein and Ajzen’s 1975 Theory of Reasoned Action.[10] While UTAUT includes some facilitating conditions, and extensions of TAM and UTAUT include such factors as perceived behavioral control [11] that can directly affect behavior, the effect of most predictors in these models on use is mediated through usage intention. In this view, the complexity of the socio-technical system, which includes technological components (e.g., system features and functionality, interoperability, usability) as well as organizational and social components (e.g., governance, project management, workflow integration, culture) is narrowed down to individual users’ perceptions or expectancies. In that respect, TAM has a similar shortcoming as some usability studies that focused on user interactions with HIT. Although both of these evaluation approaches provide meaningful insight about how users interact with technology, an acknowledged shortcoming of these approaches is a failure to account for broader system-level issues that go beyond the HIT and individual users’ acceptance of it, including team work, multitasking, time constraints, workflow and interruptions.[4, 12] Moreover, studies based on TAM and subsequent related models often conceptualize and operationalize system use in a rather simplistic manner such as the frequency of using the system, or the time spent using it.[13] This simplistic conceptualization does not distinguish between what McLean et al. have called requisite use (i.e., the “basic, mandatory, essential and obligatory use [that] denotes one‘s use of the system to complete the minimum requirements of a business process/task”), and value adding use, which “captures the additional (none-core, nonautomated and/or non-compulsory) use by the user conducted to enhance the output or impact.”[14] Indeed, it has been argued that maturity of use is an important factor for realizing
2
the potential benefits of HIT.[15] This conceptualization of use also ignores other important user behaviors such as workarounds, reinvention, and learning.[16] Nevertheless, our main criticism of TAM and UTAUT is that their contribution to our current knowledge has reached a plateau. Although the past contribution of these models to advancing information systems’ diffusion and implementation research is indisputable, there are now thousands of studies, which have utilized them (a recent search of Web of Science retrieved more than 12,000 articles citing Davis’ 1989 original TAM paper,[1] and more than 7,700 citations for Venkatesh et al. 2003 UTAUT article.[3]) The studies are fairly consistent in that the models explain a large portion of the variance in usage intention (typically in the range of 40-70%) and that the strongest predictor of usage intention is perceived usefulness or its UTAUT equivalent, performance expectancy. Similar findings have been reported for studies using TAM in biomedical informatics, [17] although for some consumer (or patient)-oriented applications, factors such as usability and perceived liveliness were as important as perceived usefulness.[5] We argue that simply applying these models to yet another type of HIT or to another specific application, or even slightly modifying the models by adding constructs from other theories, does not contribute much, conceptually, to our understanding of HIT implementation and use. In fact, in MIS, it has been argued that this focus “can be viewed as the ‘putting of blinders’ on IS researchers, diverting their main focus from investigating and understanding both the design- and implementation-based antecedents, as well as the behavior- and performance-based consequences of IT adoption and acceptance.”[16] TAM, UTAUT and related models focus on one step in the implementation process, i.e., acceptance, which is when the organization has already made an adoption decision and users within it need to buy-in.[18] However, implementation is a continuum and not a static one-time event. Studies have shown, for instance, how early in the process, the functionality of a system may not be used to its full potential. Some examples describe how providers were only using a limited subset of the HIT functionality initially, with more advanced functions gradually enabled over time,[19] or clinicians not realizing the importance of data quality in clinical documentation until reaching a certain level of usage maturity.[20]
3
The focus on acceptance may have been well justified when adoption of electronic health records (EHRs), computerized physician order entry (CPOE), and other HIT was low, especially in the United States and Canada, compared to other developed countries. With near ubiquitous adoption of these systems now, we believe it is time to shift our attention to other problems. In the following section, we propose some potential approaches and recommendations to advance our understanding of HIT implementation and use beyond TAM and UTAUT. RECOMMENDATIONS AND ALTERNATIVE APPROACHES Table 1 below summarizes some of the challenges with HIT implementation and proposes potential directions for moving beyond basic technology adoption or acceptance models. Table 1: HIT implementation challenges and potential approaches to address them HIT Implementation
Strategies to Address Challenges
References
Adoption and acceptance
Implement strategies to define and achieve
[21], [20, 22-24]
of HIT do not necessarily
value-adding use (e.g., education, training,
result in value adding use
end-user support, participatory design); focus
Challenges
on adaptation of HIT, tasks, and organizational aspects Technology cannot be
Adopt theoretical frameworks to study
Potential
separated from the
implementation and usage in context at
frameworks
underlying system in
multiple levels
include:
which it is being used
Activity Theory [25, 26], Adaptive Structuration Theory[27], and Nonadoption, Abandonment, Scale-up, Spread, and
4
Sustainability (NASSS)[28] Accounting for HIT
Frameworks and concepts from complex
implementation
adaptive systems and systems thinking to
complexity
characterize HIT complexity
Understanding and
Multi-stage usability models and establishing
reconciling multiple user
common ground across end users.
[29-31]
[32-34]
needs when designing for HIT complexity Temporal dimensions of
Concepts for understanding and managing
HIT implementation
HIT implementation over time such as
[24, 35-37]
principles of Learning Health Systems (LHS) Recommendation 1: Focus on value adding use. As suggested above, one potential future direction is to focus on issues of value-adding use and how the benefits of HIT can be realized. We have seen great progress in research on user experience design and human-computer interaction for HIT,[38]. However, although the need for education, training, and end-user support is widely recognized, there is still a need to explore these issues in depth (including strategies, approaches, and best practices). All of these are part of the adaptation process by which alignment of technology, people, organizations and contexts and ultimately, value-adding use are achieved. HIT adaptation should be studied longitudinally and multidimensionally, within cultural and organizational contexts, and using appropriate process measures.[24] Recommendation 2: Adopt and develop theoretical frameworks and methodologies that account for multiple, interrelated, sociotechnical aspects. We cannot separate technology from the underlying system where it is used. Healthcare processes such as decision making or communication require the collection, analysis, and 5
dissemination of information, and take place in a system of people, processes and technology [25]. Workflow issues, which are often defined by exceptions, must be considered as well and we cannot assume HIT will necessarily be used in a typical or ideal setting. To that end, we need evaluation approaches that consider HIT, processes, contexts, and users as a dynamic interactive system. More specifically, we need to acknowledge that many HIT implementation issues are not predictable or even identifiable at the time of implementation, but rather will be dynamic and emerge over time.[36] With that respect, we need to acknowledge our own biases. Many of us strongly believe that HIT can improve quality, safety and resource utilization, but research constantly reminds us that this is not always the case nor is it guaranteed. For example, seminal work on unintended consequences[37, 38] highlighted that post-implementation issues are often hidden initially, and will not be identified by formalized models that fail to account for flexible roles and processes. Beyond TAM and UTAUT, which focus on individuals’ perceptions and intention, there are other theoretical approaches in Social Sciences, Information Science, MIS and Biomedical Informatics, which allow for the study of HIT use in context and address multiple dimensions of the sociotechnical system. Examples include Activity Theory,[25, 26] Adaptive Structuration Theory,[27] theories of task, technology and organizational fit [42, 43], sociotechnical frameworks (e.g., [44]) and the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework.[28] Recommendation 3: Accounting for health system complexity. The complexity of health systems is derived, among other things, from the patients and their health conditions, providers and the interactions among them, and the work processes within and across settings. Consequently, HIT design becomes more complicated as we move away from technologies designed for single diseases, providers or locations, and into technologies to support patients with comorbidities that are managed by multidisciplinary teams. There can be significant variation among health systems where HIT is implemented and it is essential that we understand this and account for in implementation strategies. One example of models to help understand health system complexity is the Cyefin model that has been applied to characterize the degree of a complexity in a particular clinical situation.[45] We have used complexity science approaches to define HIT issues according to an upstream-downstream continuum where 6
tasks are connected to prior and subsequent tasks.[46] For example, data retrieval may become more challenging moving from upstream to downstream tasks because of the natural accumulation of data. Complexity-based evaluation also helps us to understand contextual variation across different HIT usage patterns. Data entry as a single task in which a nurse and another provider are interacting one-to-one is much less complex than data entry in an operating room in which an anesthetist enters dosage data while also dealing with patient medication management and monitoring vital signs and gas and flow rates.[47] Others have developed models that represent complexity according to the number of components and degree of interrelations between system components.[29] While such models are not definitive in characterizing complexity, they do provide a means of better understanding why a particular system is more complex than others. Recommendation 4: Understanding and reconciling multiple user needs. A shortcoming in adoption models like TAM and UTAUT is that they assume a relation of one user to one set of HIT requirements. In contrast, healthcare delivery is provided by a diverse set of users and tasks and HIT design must accommodate this diversity. HIT may be implemented to support new models of care delivery (e.g., collaborative care delivery) and HIT design for these models can be challenging because it will change individual workflows and require the development of new rules of engagement for working as a collaborative team.[42] HIT implementation can bring changes to individual workflow or tasks such as data entry because of the move from free text to standard data, and failure to account for individual workflow changes can lead to unintended consequences at the collaborative level.[40] HIT implementation for models such as collaborative care delivery must look beyond individual user workflows to develop collaborative common ground about system design requirements so that all users understand and agree on work practices changes because of HIT. Common ground ensures that all users have common understanding of how HIT will be used as well as how individual tasks such as information exchange will be affected by HIT.[49] One practical way of establishing common ground across user diverse groups is through multi-level HIT testing continuums such as the one proposed by Kushniruk et al.[50] The continuum moves from laboratory based usability testing (e.g., think aloud) to clinical simulations in a laboratory setting and finally to clinical simulations in real settings. The stepwise testing continuum enables us to 7
better understand the complexity and context of clinical tasks so that discrepancies between individual and collaborative tasks can be identified proactively and common ground can be established prior to HIT implementation. Recommendation 5: Consider Temporal Dimensions of HIT implementation. HIT implementation is a dynamic process that cannot be evaluated as a one-time event because regardless of how well we conceptualize and design for complexity we will not be able to account for all implementation challenges. Healthcare delivery models such as collaborative care delivery and essential processes like handovers are still in development.[48] Further, HITs such as EHRs, telehealth, and personal health records are still in their infancy as technologies with respect to how they are integrated and used in health care settings.[51] Therefore HIT implementation needs to be evaluated longitudinally as the relationship between users and technologies develops so that emerging issues between HIT, users and care delivery processed can be identified and addressed. It is not rational to assume that HIT can be implemented into complex sociotechnical environments without unintended consequences occurring. To that end, HIT implementation strategies must be cyclical to study and monitor how the interaction of HIT, people, processes and organizational contexts evolve over time. Principles of learning health systems (LHS) can provide the methodological basis for continuous approaches to study HIT implementation. LHSs are based on the premise that data can be continuously collected and analyzed to generate evidence to improve understanding and decision making about key healthcare transformation challenges such as HIT implementation.[35] Implementation frameworks based on LHS principles can enable us to maintain and build upon what is working well from HIT implementation but also to identify negative unintended consequences so they can be properly managed. Implementation strategies identified in recommendation 1 (e.g. education, training, and other end-user support) will have to be monitored and revised as education and training needs will evolve as users become more comfortable with a HIT system. LHS based approaches provide the means of monitoring HIT implementation over time so training and education can be properly configured. LHSs also emphasize the need to look beyond an isolated artifact (e.g., digital tool or work process) and instead to look at the overall cyber-social system of users, cultures, networks, technologies and processes.[36] 8
CONCLUSION We believe that biomedical informatics research, and especially the study of people and organizational issues, could benefit from shifting the focus from TAM++ and UTAUT++ as the primary scope of research, and focusing instead on other issues such as how to achieve valueadding use, avoid unintended consequences, and address challenging workflow issues. We call for researchers to adopt and develop other approaches that acknowledge the complexity of issues surrounding use of HIT. We believe these approaches are more likely to move the field forward than the narrow focus on adoption or acceptance as affected by individuals’ beliefs and attitudes, which is the scope of TAM and UTAUT-based models. A scoping or systematic review of HIT implementation research beyond TAM and UTAUT could assist with this effort.
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Graphical abstract
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HIGHLIGHTS This article discusses criticism of two widely used models of HIT implementation; Much of his criticism has been expressed before in Management Information Systems; We call researchers to adopt or develop other approaches to study HIT implementation Alternative directions should focus on value-adding use and understanding complexity; These approaches should also account for multiple user perspectives and temporality.
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S1532-0464(19)30234-5 https://doi.org/10.1016/j.jbi.2019.103315 YJBIN 103315
To appear in:
Journal of Biomedical Informatics
Received Date: Revised Date: Accepted Date:
8 July 2019 14 October 2019 15 October 2019
Please cite this article as: Shachak, A., Kuziemsky, C., Petersen, C., Beyond TAM and UTAUT: Future directions for HIT implementation research, Journal of Biomedical Informatics (2019), doi: https://doi.org/ 10.1016/j.jbi.2019.103315
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© 2019 Published by Elsevier Inc.
Beyond TAM and UTAUT: future directions for HIT implementation research Aviv Shachak1,2, MSc, PhD, Craig Kuziemsky3, PhD, Carolyn Petersen4,, MS, MBI, FAMIA 1. Institute of Health Policy, Management and Evaluation (Dalla Lana School of Public Health), University of Toronto, Toronto, ON Canada 2. Faculty of Information, University of Toronto, Toronto ON Canada 3. Associate Vice-President, Research, MacEwan University, Edmonton, AB Canada 4. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States Corresponding Author: Dr. Aviv Shachak Institute of Health Policy, Management and Evaluation, University of Toronto 155 College St., Toronto, ON Canada M5T 3M6 Phone: 1-416-978-0998 Fax: 1-416-978-7350 Email: [email protected] Keywords: Technology Acceptance Model; Unified Theory of Acceptance and Use of Technology; Complexity Science; Technology Use; Health Information Technology Implementation Word count: 2684
ABSTRACT The Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) have been used widely in studies of health information technology (HIT) implementation. However, TAM and UTAUT have also been criticized for being overly simplistic (TAM) and for taking a narrow perspective, which focuses only on individual adopters’ beliefs, perceptions and usage intention. Furthermore, with thousands of studies using these theories, their contribution to knowledge has reached a plateau. In this commentary, we discuss some of the criticism of TAM and UTAUT, and argue that biomedical informatics research would benefit from shifting attention from these theories to multi-dimensional approaches that can better capture the complexity of issues surrounding implementation and use of HIT. We propose a number of future undertakings which, in our opinion, are more likely to move the field forward.
INTRODUCTION Adoption and implementation of health information technology (HIT) have been the focus of much research in biomedical informatics. Two of the most commonly used theories for this research are the Technology Acceptance Model (TAM) [1, 2] and the Unified Theory of Acceptance and Use of Technology (UTAUT).[3] Both TAM and UTAUT suggest that actual use of technology is affected by one’s behavioral intention to use it. In TAM, intended use is determined by attitude toward using the technology, which in turn is determined by two perceptions of the system: perceived usefulness and perceived ease of use. Various external factors affect both perceptions. UTAUT builds on TAM, as well as seven other theoretical frameworks. It proposes four constructs that affect usage intention—performance expectancy, effort expectancy, social influence and facilitating conditions. Age, gender, experience, and voluntariness of use mediate the impact of these expectancies and facilitating conditions on intention.[3] Both TAM and UTAUT have been used extensively in Management Information Systems (MIS) and biomedical informatics,[4] and many modifications and adaptations of these theories have been proposed over the years. This includes, for example, adding constructs from other theories and modifications to account for specific applications such as telemedicine or patients’ adoption of eHealth and mHealth applications.[5, 6] However, these theories (especially TAM) have also been widely criticized. This perspective article will present some of the criticism of TAM and UTAUT and then argue that it is time for biomedical informatics research to move away from TAM and UTAUT and focus their efforts on a broader array of implementation issues. We conclude with potential future directions for health information technology (HIT) implementation research. CRITICISM OF TAM AND UTAUT In MIS, the discipline from which it originated, criticism of TAM arose more than a decade ago.[7] Perhaps the most common criticism of TAM is its over-simplicity. In many studies, the model is reduced to three constructs only: perceived usefulness, perceived ease of use, and usage intention, which makes outcomes such as intended or perceived use become the end-point rather than actual use of the technology being studied. While this simplicity makes TAM a useful tool 1
to gauge the acceptability of new technologies in a ‘quick and dirty’ manner, [8] or assess the needs of different user groups, it lowers its explanatory power and provides little insight on actual HIT usage. UTAUT and its modifications attempt to overcome this limitation by incorporating additional constructs such as social influence, facilitating conditions, and habit, as well as mediating individual characteristics (age, gender, and experience).[3, 9] However, TAM, UTAUT, and related models often take a somewhat narrow perspective on diffusion and use of information and communication technology (ICT) in that they adopt a Social Psychology view, which focuses on the individual adopter (i.e. user) and assumes a direct causal influence of intention on actual behavior—the roots of this view can be traced back to Fishbein and Ajzen’s 1975 Theory of Reasoned Action.[10] While UTAUT includes some facilitating conditions, and extensions of TAM and UTAUT include such factors as perceived behavioral control [11] that can directly affect behavior, the effect of most predictors in these models on use is mediated through usage intention. In this view, the complexity of the socio-technical system, which includes technological components (e.g., system features and functionality, interoperability, usability) as well as organizational and social components (e.g., governance, project management, workflow integration, culture) is narrowed down to individual users’ perceptions or expectancies. In that respect, TAM has a similar shortcoming as some usability studies that focused on user interactions with HIT. Although both of these evaluation approaches provide meaningful insight about how users interact with technology, an acknowledged shortcoming of these approaches is a failure to account for broader system-level issues that go beyond the HIT and individual users’ acceptance of it, including team work, multitasking, time constraints, workflow and interruptions.[4, 12] Moreover, studies based on TAM and subsequent related models often conceptualize and operationalize system use in a rather simplistic manner such as the frequency of using the system, or the time spent using it.[13] This simplistic conceptualization does not distinguish between what McLean et al. have called requisite use (i.e., the “basic, mandatory, essential and obligatory use [that] denotes one‘s use of the system to complete the minimum requirements of a business process/task”), and value adding use, which “captures the additional (none-core, nonautomated and/or non-compulsory) use by the user conducted to enhance the output or impact.”[14] Indeed, it has been argued that maturity of use is an important factor for realizing
2
the potential benefits of HIT.[15] This conceptualization of use also ignores other important user behaviors such as workarounds, reinvention, and learning.[16] Nevertheless, our main criticism of TAM and UTAUT is that their contribution to our current knowledge has reached a plateau. Although the past contribution of these models to advancing information systems’ diffusion and implementation research is indisputable, there are now thousands of studies, which have utilized them (a recent search of Web of Science retrieved more than 12,000 articles citing Davis’ 1989 original TAM paper,[1] and more than 7,700 citations for Venkatesh et al. 2003 UTAUT article.[3]) The studies are fairly consistent in that the models explain a large portion of the variance in usage intention (typically in the range of 40-70%) and that the strongest predictor of usage intention is perceived usefulness or its UTAUT equivalent, performance expectancy. Similar findings have been reported for studies using TAM in biomedical informatics, [17] although for some consumer (or patient)-oriented applications, factors such as usability and perceived liveliness were as important as perceived usefulness.[5] We argue that simply applying these models to yet another type of HIT or to another specific application, or even slightly modifying the models by adding constructs from other theories, does not contribute much, conceptually, to our understanding of HIT implementation and use. In fact, in MIS, it has been argued that this focus “can be viewed as the ‘putting of blinders’ on IS researchers, diverting their main focus from investigating and understanding both the design- and implementation-based antecedents, as well as the behavior- and performance-based consequences of IT adoption and acceptance.”[16] TAM, UTAUT and related models focus on one step in the implementation process, i.e., acceptance, which is when the organization has already made an adoption decision and users within it need to buy-in.[18] However, implementation is a continuum and not a static one-time event. Studies have shown, for instance, how early in the process, the functionality of a system may not be used to its full potential. Some examples describe how providers were only using a limited subset of the HIT functionality initially, with more advanced functions gradually enabled over time,[19] or clinicians not realizing the importance of data quality in clinical documentation until reaching a certain level of usage maturity.[20]
3
The focus on acceptance may have been well justified when adoption of electronic health records (EHRs), computerized physician order entry (CPOE), and other HIT was low, especially in the United States and Canada, compared to other developed countries. With near ubiquitous adoption of these systems now, we believe it is time to shift our attention to other problems. In the following section, we propose some potential approaches and recommendations to advance our understanding of HIT implementation and use beyond TAM and UTAUT. RECOMMENDATIONS AND ALTERNATIVE APPROACHES Table 1 below summarizes some of the challenges with HIT implementation and proposes potential directions for moving beyond basic technology adoption or acceptance models. Table 1: HIT implementation challenges and potential approaches to address them HIT Implementation
Strategies to Address Challenges
References
Adoption and acceptance
Implement strategies to define and achieve
[21], [20, 22-24]
of HIT do not necessarily
value-adding use (e.g., education, training,
result in value adding use
end-user support, participatory design); focus
Challenges
on adaptation of HIT, tasks, and organizational aspects Technology cannot be
Adopt theoretical frameworks to study
Potential
separated from the
implementation and usage in context at
frameworks
underlying system in
multiple levels
include:
which it is being used
Activity Theory [25, 26], Adaptive Structuration Theory[27], and Nonadoption, Abandonment, Scale-up, Spread, and
4
Sustainability (NASSS)[28] Accounting for HIT
Frameworks and concepts from complex
implementation
adaptive systems and systems thinking to
complexity
characterize HIT complexity
Understanding and
Multi-stage usability models and establishing
reconciling multiple user
common ground across end users.
[29-31]
[32-34]
needs when designing for HIT complexity Temporal dimensions of
Concepts for understanding and managing
HIT implementation
HIT implementation over time such as
[24, 35-37]
principles of Learning Health Systems (LHS) Recommendation 1: Focus on value adding use. As suggested above, one potential future direction is to focus on issues of value-adding use and how the benefits of HIT can be realized. We have seen great progress in research on user experience design and human-computer interaction for HIT,[38]. However, although the need for education, training, and end-user support is widely recognized, there is still a need to explore these issues in depth (including strategies, approaches, and best practices). All of these are part of the adaptation process by which alignment of technology, people, organizations and contexts and ultimately, value-adding use are achieved. HIT adaptation should be studied longitudinally and multidimensionally, within cultural and organizational contexts, and using appropriate process measures.[24] Recommendation 2: Adopt and develop theoretical frameworks and methodologies that account for multiple, interrelated, sociotechnical aspects. We cannot separate technology from the underlying system where it is used. Healthcare processes such as decision making or communication require the collection, analysis, and 5
dissemination of information, and take place in a system of people, processes and technology [25]. Workflow issues, which are often defined by exceptions, must be considered as well and we cannot assume HIT will necessarily be used in a typical or ideal setting. To that end, we need evaluation approaches that consider HIT, processes, contexts, and users as a dynamic interactive system. More specifically, we need to acknowledge that many HIT implementation issues are not predictable or even identifiable at the time of implementation, but rather will be dynamic and emerge over time.[36] With that respect, we need to acknowledge our own biases. Many of us strongly believe that HIT can improve quality, safety and resource utilization, but research constantly reminds us that this is not always the case nor is it guaranteed. For example, seminal work on unintended consequences[37, 38] highlighted that post-implementation issues are often hidden initially, and will not be identified by formalized models that fail to account for flexible roles and processes. Beyond TAM and UTAUT, which focus on individuals’ perceptions and intention, there are other theoretical approaches in Social Sciences, Information Science, MIS and Biomedical Informatics, which allow for the study of HIT use in context and address multiple dimensions of the sociotechnical system. Examples include Activity Theory,[25, 26] Adaptive Structuration Theory,[27] theories of task, technology and organizational fit [42, 43], sociotechnical frameworks (e.g., [44]) and the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework.[28] Recommendation 3: Accounting for health system complexity. The complexity of health systems is derived, among other things, from the patients and their health conditions, providers and the interactions among them, and the work processes within and across settings. Consequently, HIT design becomes more complicated as we move away from technologies designed for single diseases, providers or locations, and into technologies to support patients with comorbidities that are managed by multidisciplinary teams. There can be significant variation among health systems where HIT is implemented and it is essential that we understand this and account for in implementation strategies. One example of models to help understand health system complexity is the Cyefin model that has been applied to characterize the degree of a complexity in a particular clinical situation.[45] We have used complexity science approaches to define HIT issues according to an upstream-downstream continuum where 6
tasks are connected to prior and subsequent tasks.[46] For example, data retrieval may become more challenging moving from upstream to downstream tasks because of the natural accumulation of data. Complexity-based evaluation also helps us to understand contextual variation across different HIT usage patterns. Data entry as a single task in which a nurse and another provider are interacting one-to-one is much less complex than data entry in an operating room in which an anesthetist enters dosage data while also dealing with patient medication management and monitoring vital signs and gas and flow rates.[47] Others have developed models that represent complexity according to the number of components and degree of interrelations between system components.[29] While such models are not definitive in characterizing complexity, they do provide a means of better understanding why a particular system is more complex than others. Recommendation 4: Understanding and reconciling multiple user needs. A shortcoming in adoption models like TAM and UTAUT is that they assume a relation of one user to one set of HIT requirements. In contrast, healthcare delivery is provided by a diverse set of users and tasks and HIT design must accommodate this diversity. HIT may be implemented to support new models of care delivery (e.g., collaborative care delivery) and HIT design for these models can be challenging because it will change individual workflows and require the development of new rules of engagement for working as a collaborative team.[42] HIT implementation can bring changes to individual workflow or tasks such as data entry because of the move from free text to standard data, and failure to account for individual workflow changes can lead to unintended consequences at the collaborative level.[40] HIT implementation for models such as collaborative care delivery must look beyond individual user workflows to develop collaborative common ground about system design requirements so that all users understand and agree on work practices changes because of HIT. Common ground ensures that all users have common understanding of how HIT will be used as well as how individual tasks such as information exchange will be affected by HIT.[49] One practical way of establishing common ground across user diverse groups is through multi-level HIT testing continuums such as the one proposed by Kushniruk et al.[50] The continuum moves from laboratory based usability testing (e.g., think aloud) to clinical simulations in a laboratory setting and finally to clinical simulations in real settings. The stepwise testing continuum enables us to 7
better understand the complexity and context of clinical tasks so that discrepancies between individual and collaborative tasks can be identified proactively and common ground can be established prior to HIT implementation. Recommendation 5: Consider Temporal Dimensions of HIT implementation. HIT implementation is a dynamic process that cannot be evaluated as a one-time event because regardless of how well we conceptualize and design for complexity we will not be able to account for all implementation challenges. Healthcare delivery models such as collaborative care delivery and essential processes like handovers are still in development.[48] Further, HITs such as EHRs, telehealth, and personal health records are still in their infancy as technologies with respect to how they are integrated and used in health care settings.[51] Therefore HIT implementation needs to be evaluated longitudinally as the relationship between users and technologies develops so that emerging issues between HIT, users and care delivery processed can be identified and addressed. It is not rational to assume that HIT can be implemented into complex sociotechnical environments without unintended consequences occurring. To that end, HIT implementation strategies must be cyclical to study and monitor how the interaction of HIT, people, processes and organizational contexts evolve over time. Principles of learning health systems (LHS) can provide the methodological basis for continuous approaches to study HIT implementation. LHSs are based on the premise that data can be continuously collected and analyzed to generate evidence to improve understanding and decision making about key healthcare transformation challenges such as HIT implementation.[35] Implementation frameworks based on LHS principles can enable us to maintain and build upon what is working well from HIT implementation but also to identify negative unintended consequences so they can be properly managed. Implementation strategies identified in recommendation 1 (e.g. education, training, and other end-user support) will have to be monitored and revised as education and training needs will evolve as users become more comfortable with a HIT system. LHS based approaches provide the means of monitoring HIT implementation over time so training and education can be properly configured. LHSs also emphasize the need to look beyond an isolated artifact (e.g., digital tool or work process) and instead to look at the overall cyber-social system of users, cultures, networks, technologies and processes.[36] 8
CONCLUSION We believe that biomedical informatics research, and especially the study of people and organizational issues, could benefit from shifting the focus from TAM++ and UTAUT++ as the primary scope of research, and focusing instead on other issues such as how to achieve valueadding use, avoid unintended consequences, and address challenging workflow issues. We call for researchers to adopt and develop other approaches that acknowledge the complexity of issues surrounding use of HIT. We believe these approaches are more likely to move the field forward than the narrow focus on adoption or acceptance as affected by individuals’ beliefs and attitudes, which is the scope of TAM and UTAUT-based models. A scoping or systematic review of HIT implementation research beyond TAM and UTAUT could assist with this effort.
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health system: the foundation for continuous improvement in health and health care: workshop series summary. The National Academies Collection: Reports funded by National Institutes of Health. Washington (DC)2011. ISBN-13: 978-0-309-15416-1; ISBN-10: 0-309-15416-2
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Graphical abstract
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HIGHLIGHTS This article discusses criticism of two widely used models of HIT implementation; Much of his criticism has been expressed before in Management Information Systems; We call researchers to adopt or develop other approaches to study HIT implementation Alternative directions should focus on value-adding use and understanding complexity; These approaches should also account for multiple user perspectives and temporality.
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