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An Industrial Process View of Information Delivery to Support Clinical Decision Making

Implications for Systems Design and Process Measures

Affiliation of the authors: University of Minnesota, Health Computer Sciences, Minneapolis, MN.

Correspondence and reprints: Robert Elson, MD, University of Minnesota/Health Computer Sciences, Box 511 UMHC, 420 Delaware St. SE, Minneapolis, MN 55455.E-mail: relson{at}umnhcs.labmed.umn.edu

Abstract Clinical decision making is driven by information in the form of patient data and clinical knowledge. Currently prevalent systems used to store and retrieve this information have high failure rates, which can be traced to well-established system constraints. The authors use an industrial process model of clinical decision making to expose the role of these constraints in increasing variability in the delivery of relevant clinical knowledge and patient data to decision-making clinicians. When combined with nonmodifiable human cognitive and memory constraints, this variability in information delivery is largely responsible for the high variability of decision outcomes. The model also highlights the supply characteristics of information, a view that supports the application of industrial inventory management concepts to clinical decision support. Finally, the clinical decision support literature is examined from a process-improvement perspective with a focus on decision process components related to information retrieval. Considerable knowledge gaps exist related to clinical decision support process measurement and improvement.

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