An Industrial Process View of Information Delivery to Support Clinical Decision Making
Implications for Systems Design and Process Measures
- 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
- Received 17 October 1996
- Accepted 3 March 1997
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.
Footnotes
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Supported in part by Grant T15 LM-07041 (Training in Medical Informatics) from the National Library of Medicine, Bethesda, MD (Drs. Elson and Faughnan).
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↵* The focus here is on encounters related to arriving at recommendations for treatment or further diagnostic testing.
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↵† Decision “outcome” as used here refers to the actual decision made, not to the outcome of care that may have resulted from testing or treatment related to a decision.
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↵‡ Other important products besides decisions result from clinician-patient encounters, such as satisfied or educated patients, but these are not considered here. This is not intended to minimize the importance of aspects of patient care not mechanistically related to formulating recommendations.
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↵§ In addition to face-to-face interactions, decision-based encounters include telephone calls, prescription refill requests, and the clinician review of test results even when a patient is not present.
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↵∥ The variation in these estimates is due to differing methods used to quantify information needs, ranging from mail survey-based recall,89 immediate postencounter interviewer-stimulated recall,48, delayed interviewer-stimulated recall,90 video-stimulated recall85 to tape-recorded84 and anthropologist-based83 observation of recognized and verbally stated questions.83 84
