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) [21]. Second, Vincent’s framework for analyzing risk and safety proposes a hierarchy of factors that can potentially influence clinical practice [22]. Third, Carayon’s Systems Engineering Initiative for Patient Safety (SEIPS) model [23] 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 [24].

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 [25]. 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 [26], 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 [27]; developing a process for monitoring the effect of new CDS on the systems’ response time [28]; building tools to track the CDS that is in place [29]; 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 [30].