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EVIDENCE-BASED MEDICINE PRACTICE

Evidence-based medicine is the “conscientious, explicit, and judicious use of current best evidence in making medical decisions about the care of individual patients” (Sackett et al., 1999 [163]). There has been a general consensus that continuous, comprehensive practice of evidence-based medicine has tremendous potential to improve quality of care and reduce practice variation. However, there is also a widely acknowledged gap between physicians’ awareness of these care standards and physicians’ consistent application of them in practice. While mounting evidence has shown that clinical decision support systems can improve physician guideline compliance, and thus patient health, widespread use of such systems has not become available due to numerous technological, behavioral, and organizational barriers. These facts motivate the present research. In this thesis, I report findings from a seven-year effort to design, implement, and evaluate a clinical decision support system, called Clinical Reminder System (CRS), in two ambulatory primary care clinics at the Western Pennsylvania Hospital. CRS aims to improve quality of care by providing clinicians just-in-time alerts and advisories using evidence-based medicine guidelines. First, I describe the technical aspects of the system and introduce a computational ontology that enables structured acquisition and automated execution of evidence-based medicine guidelines. A mixed evaluation approach is presented next, which combines quantitative developmental trajectory modeling with qualitative assessments to examine the users’ technology adoption and acceptance behavior. Synthesizing the findings, I critique and extend the Technology Acceptance Model (TAM)—a widely used theory for studying technology diffusion in the information systems area. I show that while TAM explains certain usage behavior at discrete times, methodological pluralism illustrated in this thesis helps reveal and understand more subtle, longitudinal behavior that spans the entire technology diffusion process. Next, I take into account the social context in which the users of CRS are situated. I find that social contagion, particularly through structural equivalence of friendship networks, has great impact on the users’ level of adoption. Finally, I use sequential pattern analysis and a first order Markov chain model to analyze the temporal event sequences recorded in CRS. The results lead to a software design pattern for system reengineering, which calibrates the system’s user interface so that the within-application workflow is aligned with clinicians’ mental model in medical problem-solving. In the last chapter, I present future research extensions and long-term plans for evaluating the system’s effectiveness on physician guideline compliance and patient health outcomes. I conclude that this research enhances our understanding of medical, technological, behavioral, and institutional challenges associated with diffusion of decision support technologies into health care practice. The methods and findings may also provide methodological and practical insights into design, implementation, and evaluation issues of other health informatics applications, as well as information systems more generally.