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Conceptual models have been developed to address challenges inherent in studying health information technology (HIT). This manuscript introduces an 8-dimensional model specifically designed to address the socio-technical challenges involved in design, development, implementation, use, and evaluation of HIT within complex adaptive healthcare systems. The 8 dimensions are not independent, sequential, or hierarchical, but rather are interdependent and interrelated concepts similar to compositions of other complex adaptive systems. Hardware and software computing infrastructure refers to equipment and software used to power, support, and operate clinical applications and devices. Clinical content refers to textual or numeric data and images that constitute the “language” of clinical applications. The human computer interface includes all aspects of the computer that users can see, touch, or hear as they interact with it. People refers to everyone who interacts in some way with the system, from developer to end-user, including potential patient-users. Workflow and communication are the processes or steps involved in assuring that patient care tasks are carried out effectively. Two additional dimensions of the model are internal organizational features (e.g., policies, procedures, and culture) and external rules and regulations, both of which may facilitate or constrain many aspects of the preceding dimensions. The final dimension is measurement and monitoring, which refers to the process of measuring and evaluating both intended and unintended consequences of HIT implementation and use. We illustrate how our model has been successfully applied in real-world complex adaptive settings to understand and improve HIT applications at various stages of development and implementation.
An ongoing challenge to the design, development, implementation, and evaluation of health information technology (HIT) interventions is to operationalize their use within the complex adaptive health care system that consists of high-pressured, fast-paced, and distributed settings of care delivery. Many conceptual models of user interaction, acceptance, and evaluation exist [1, 2], but most are relatively limited in scope. Given the dearth of models that are specifically designed to address safe and effective HIT development and use, we have developed a comprehensive, socio-technical model that provides a multi-dimensional framework within which any HIT innovation, intervention, application, or device implemented within a complex adaptive healthcare system can be studied. This model builds upon and bridges previous frameworks and is further informed by our own work to study the safe and effective implementation and use of HIT interventions. In this paper we describe the conceptual foundations of our model and provide several examples of its utility for studying HIT interventions within real-world clinical contexts.
Previous analyses of HIT interventions have been limited by a lack of conceptual models that have been specifically developed for this purpose. Examples of models previously applied by HIT investigators include Rogers’ diffusion of innovations theory [3,4 5], Venkatesh’s unified theory of acceptance and use of technology [6,7,8,9], Hutchins’ theory of distributed cognition [10,11,12,13,14], Reason’s Swiss Cheese Model [15,16,17], and Norman’s 7-step human-computer interaction model [18,19,20]. Although all of these models account for one or more important facets of technology implementation, we believe that the scope of each model limits its utility to address the full range of factors that should be considered in the design, development, implementation, use, and evaluation of HIT interventions. For example, these models were not specifically designed to address the complex relationships between the HIT hardware, software, information content, and the human-computer interface. Furthermore, while most of these models provide general guidance to study the high-level aspects of HIT implementation within a given clinical environment, none of them includes a measurement and monitoring infrastructure (e.g., methods to routinely collect data, create or review reports or conduct surveillance of outcomes). Based on these limitations, our aim was to develop a more comprehensive model to integrate specific technological and measurement dimensions of HIT with other socio-technical dimensions (e.g., people, workflow, communication, organizational policies, external rules and regulations).
Four related socio-technical models have been particularly influential in providing the foundation of our proposed model. First, Henriksen’s model addresses (1) individual provider characteristics; (2) the nature or complexity of the work or task performed; (3) the physical environment where care takes place; (4) the human-system interfaces involved; and (5) various characteristics of the organization (social, environment, and management) . Second, Vincent’s framework for analyzing risk and safety proposes a hierarchy of factors that can potentially influence clinical practice . Third, Carayon’s Systems Engineering Initiative for Patient Safety (SEIPS) model  identifies three domains: (1) characteristics of providers, their tools and resources, and the physical/organizational setting; (2) interpersonal and technical aspects of health care activities; and (3) change in the patient's health status or behavior. Finally, Harrison et al.’s Interactive Socio-technical Analysis (ISTA) framework provides an excellent broad overview of the complex, emergent, inter-relationships between the HIT, clinicians, and workflows within any healthcare system .
While these socio-technical models include a "technology" component, none break down the "technology" into its individual components to enable researchers to dissect out the causes of particular HIT implementation or use problems, or to help identify specific solutions. We have found that many HIT problems we are studying revolve around the interplay of hardware, software, content (e.g., clinical data and computer-generated decision support), and user interfaces. Failing to acknowledge these specific technology-specific elements or attempting to treat them separately can hinder overall understanding of HIT-related challenges. For example, the "content" dimension of our model accounts for much of what informaticians do, that is, studying the intricacies of controlled clinical vocabularies that provide the cognitive interface between the inexact, subjective, highly variable world of biomedicine and the highly structured, tightly controlled, digital world of computers . A well-constructed, robust user interface vocabulary can make all the difference in the world to a busy clinician struggling to quickly and accurately enter a complex clinical order for a critically ill patient , and it is important to distinguish this aspect of technology from others that may contribute to additional challenges (e.g., a user interface that is difficult to navigate, an order entry application that is slow to respond, or computers that are only available at the main nursing station). Failure to do so, for example, leads to general statements such as "clinicians struggled with the new technology" or "it takes clinicians longer to complete their tasks using the new technology" without providing any insight into specific causes of the problems or their solutions. In this example, without a multidimensional understanding of the technological dimensions of the failed IT application, the researcher may incorrectly conclude that the hardware, application software, or user was responsible, when in fact a poorly designed or implemented clinical vocabulary might have been the root of the problem.
Finally the preceding models do not account for the special monitoring processes and governance structures that must be put in place while designing and developing, implementing, or using HIT. For example, identifying who will make the decision on what, when, and how clinical decision support (CDS) interventions will be added ; developing a process for monitoring the effect of new CDS on the systems’ response time ; building tools to track the CDS that is in place ; developing an approach for testing CDS; defining approaches for identifying rules that interact; developing robust processes for collecting feedback from users and communicating new system fixes, features, and functions; and building tools for monitoring the CDS system itself .
To overcome the limitations of previous models, we propose a new socio-technical model to study the design, development, use, implementation, and evaluation of HIT (Figure 1). Our comprehensive 8-dimensional model accounts for key factors that influence the success of HIT interventions. A major assumption of our model is that the 8 dimensions cannot be viewed as a series of independent, sequential steps. As with other components of complex adaptive systems, these 8 interacting dimensions must be studied in relationship to each other. Clearly, several of our model’s components are more tightly coupled than others, for example, the hardware, software, content, and user interface are all completely dependent on one another. However, all the other social components also exert strong influences on these technical components.
In our model, one cannot expect to gain an in-depth understanding of the intricacies of complex HIT interventions simply by integrating the results of studies performed within any single dimension of the model . Rather, HIT interventions must be understood in the context of their simultaneous effects across multiple dimensions of the model. For instance, a recent evaluation of a national program to develop and implement centrally stored electronic summaries of patients’ medical records in the UK revealed their benefits to be lower than anticipated and cautioned that complex interdependencies between many socio-technical factors at the clinical encounter-, organizational- and the national-level are to be expected in such evaluations . These study findings are illustrative of how and why our proposed model could be useful.
The 8 dimensions include:
Our research and experience has led us, and others, to conclude that HIT-enabled healthcare systems are best treated as complex adaptive systems . The most important result of this conclusion is that hierarchical decomposition (i.e., breaking a complex system, process, or device down into its components, studying them, and then integrating the results in an attempt to understand how the complete system functions) cannot be used to study HIT . As illustrated by the evaluation of centrally stored electronic summaries in the UK, complex interdependencies between various socio-technical dimensions are to be expected and our HIT model (had it existed at the time) might have potentially predicted some of them and allowed them to address them prior to go-live rather than in the evaluation stages of the project. Therefore, one should not view or use our model as a set of independent components which can be studied in isolation and then synthesized to develop a realistic picture of how HIT is used within the complex adaptive healthcare system. Rather, the key to our model is how the eight dimensions interact and depend on one another. They must be studied as multiple, interacting components with non-linear, emergent, dynamic behavior (i.e., small changes in one aspect of the system lead to small changes in other parts of the system under some conditions, but large changes at other times) that often appears random or chaotic. This is typical of complex adaptive systems, and our model reflects these interactions.
For example, a computer-based provider order entry (CPOE) system that works successfully on an adult, surgical nursing unit within a hospital may not work at all in the nearby pediatric unit for any number of potential reasons, including: 1) hardware/software (e.g., fewer computers, older computers, poor wireless reception, poor placement); 2) content (e.g., no weight- or age-based dosing, no customized order sets or documentation templates); 3) user-interface (e.g., older workforce that has trouble seeing the small font on the screen); or 4) personnel (e.g., no clinical champion within the medical staff). However, each of these dimensions has a potential relationship with one or more of the other dimensions. For instance, computers may have been few or old because of some organizational limitations, there may be no customized order sets because clinician-users did not agree on how best to do it, and there was no clinical champion because the organization did not provide any incentive for the additional time this role would entail. Other reasons could include problems with the user interface and the communication and workflow related to how nurses process new medication orders using the EHR and record administration of medications. These issues, in turn, may have been due to organizational policies and procedures. For example, the unit governance committee may have decided not to approve a request for mobile computers, with the result that nurses spent more time away from patients and therefore had a slower workflow related to processing new orders. The preceding example illustrates the interaction of six dimensions of our model: hardware/software, clinical content, user interface, people, workflow, and organizational policies. Additionally, some form of monitoring could have detected these issues. In summary, our model provides HIT researchers with several new avenues of thinking about key technology components and how these dimensions can be accounted for in future research.
The following sections illustrate how we have used the socio-technical model of safe and effective HIT use within our research. In an attempt to describe how the model can be applied across the breadth of HIT research and development, and to provide examples of different systems and interventions that can be analyzed within this new paradigm, we highlight key elements of our model in the context of several recent projects.
The design and development of CDS interventions within clinicians’ workflow presents several challenges. We conducted several qualitative studies to gain insight into the 8 dimensions of our model during the development of a CDS tool within a CPOE application. This CDS intervention was designed to alert clinicians whenever they attempted to order a medication that was contraindicated in elderly patients or one that had known serious interactions with warfarin. We used several methods, including focus groups, usability testing, and educational sessions with clinician users , to identify issues related to hardware/software, content, interface, people, measurement, workflow/communication, and internal policies and procedures. These efforts helped us, for example, to understand the need to meet with the organization’s Pharmacy and Therapeutics (P & T) committee (i.e., internal policy) to convince them to modify the medication formulary as well as the information technology professional (i.e., people) who was responsible for maintaining the textual content of the alerts (i.e., font size, contents and order of the messages) to fit within the constraints of the alert notification window (i.e., user interface) which eliminated the need to train clinicians to use the horizontal scrolling capability. This is just one simple example of how use of the 8 dimensional model paid huge dividends during the development and implementation stages of this highly successful project [52,53].
In a recent article we described lessons that could be learned from CPOE implementation at another site . One of the most important conclusions from this implementation was that problems could, and often do, occur in all 8 dimensions of the model (see Table 1) .
Safe and effective use of an EHR-based notification system involves many factors that are addressed by almost all dimensions of our model [56,57]. This CDS system generates automated asynchronous “alerts” to notify clinicians of important clinical findings. We examined communication outcomes of over 2500 such alerts that were specifically related to abnormal test results. We found that 18.1% of abnormal lab alerts and 10.2% of abnormal imaging alerts were never acknowledged (i.e., were unread by the receiving provider). Additionally, 7–8% of these alerts lacked timely follow-up, which was unrelated to acknowledgment of the alert.
Despite a notification system that ensured transmission of results, it was concerning that abnormal test results did not always receive timely follow-up, even when acknowledged. This study revealed complex interactions between users, the user interface, software, content, workflow/communication, and organizational policies related to who was responsible for abnormal test follow-up. Our findings thus highlighted the multiple dimensions of our model that need to be addressed to improve the safety of EHR-based notification systems and perhaps other forms of CDS (see Table 1) [59, 60, 61, 62]. We are now applying the socio-technical model to study barriers, facilitators, and interventions for safe and effective test result notification through EHRs.
Our model recently provided us guidance in HIT evaluation, reminding us that however technologically savvy we make our patient care processes, we must also carefully monitor their impact, effectiveness, and unintended consequences. We recently evaluated why, despite implementation of an automated notification system to enhance communication of fecal occult blood test (FOBT) results, providers did not take follow-up actions in almost 40% of cases . Again, our findings highlighted multiple dimensions corresponding to our socio-technical model. For instance, we found that clinician non-response to automated notifications was related to a software configuration error that prevented transmission of a subset of test results but we also found that if the institution was using certain types of workflows related to test performance and that if organizational procedures for computerized order-entry of FOBTs were different, the problem may not have occurred. Thus, we found our multi-dimensional approach, which accounted for interactions, to be useful for comprehensive evaluation of HIT after implementation.
The 8 dimensions of the safe and effective HIT use model introduced in this manuscript establish a new paradigm for the study of HIT. We have successfully applied this model to study several HIT interventions at different levels of design, development, implementation, use and evaluation. We anticipate that additional study of the 8 dimensions and their complex interactions will yield further refinements to this model and, ultimately, improvements in the quality and safety of the HIT applications that translate to better health and welfare for our patients.
We thank Donna Espadas and Adol Esquivel, MD, PhD for their help creating the graphical depiction of the model. We also thank the two reviewers of this paper for their constructive criticism. This research was supported in part by the National Library of Medicine R01- LM006942 (DFS), NIH K23 career development award (K23CA125585) to HS, the VA National Center of Patient Safety (DFS, HS), Agency for Health Care Research and Quality (R18 HS17820) to HS and in part by the Houston VA HSR&D Center of Excellence (HFP90-020) (HS). These sources had no role in the preparation, review, or approval of the manuscript. We also thank Andrea Bradford, PhD for editorial assistance.
The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the National Institutes of Health.
No conflicts of interest
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