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Model Formulation |
Affiliations of the authors: RAND Health, Santa Monica, CA (DSB, SC, RSM, ABL); Division of General Internal Medicine and Health Services Research, Department of Medicine, University of California, Los Angeles, School of Medicine, Los Angeles, CA (DSB). Dr. Cretin is currently Regional Director, American Friends Service Committee, Pasadena, CA; Mr. Landman is currently a medical student, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJ.
Correspondence and reprints: Douglas S. Bell, MD, PhD, UCLA Division of General Internal Medicine and Health Services Research, 911 Broxton Plaza, Room 314, Los Angeles, CA 90095-1736; e-mail: dbell{at}ucla.edu.
Received for publication: 03/31/03; accepted for publication: 09/19/03.
Objective: Electronic prescribing (e-prescribing) may substantially improve health care quality and efficiency, but the available systems are complex and their heterogeneity makes comparing and evaluating them a challenge. The authors aimed to develop a conceptual framework for anticipating the effects of alternative designs for outpatient e-prescribing systems.
Design: Based on a literature review and on telephone interviews with e-prescribing vendors, the authors identified distinct e-prescribing functional capabilities and developed a conceptual framework for evaluating e-prescribing systems' potential effects based on their capabilities. Analyses of two commercial e-prescribing systems are presented as examples of applying the conceptual framework.
Measurements: Major e-prescribing functional capabilities identified and the availability of evidence to support their specific effects.
Results: The proposed framework for evaluating e-prescribing systems is organized using a process model of medication management. Fourteen e-prescribing functional capabilities are identified within the model. Evidence is identified to support eight specific effects for six of the functional capabilities. The evidence also shows that a functional capability with generally positive effects can be implemented in a way that creates unintended hazards. Applying the framework involves identifying an e-prescribing system's functional capabilities within the process model and then assessing the effects that could be expected from each capability in the proposed clinical environment.
Conclusion: The proposed conceptual framework supports the integration of available evidence in considering the full range of effects from e-prescribing design alternatives. More research is needed into the effects of specific e-prescribing functional alternatives. Until more is known, e-prescribing initiatives should include provisions to monitor for unintended hazards.
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