Predictive Models for Primary Caregivers: Risky Business?

  1. Randolph A. Miller, MD
  1. Vanderbilt University Medical Center Nashville, TN 37232-8340 Grant Support: In part by grants 5-G08-LM-05443 and 1-R01-LM-06226 from the National Library of Medicine. Requests for Reprints: Randolph A. Miller, MD, Room 436, Eskind Library, 2209 Garland Avenue, Vanderbilt University Medical Center, Nashville, TN 37232-8340.

    By informing the opinions of health care providers, future decision support systems will improve clinical practice. Yet many practitioners still approach technologically based tools with unquestioning awe rather than by trying to understand the mechanisms, applicability, and limitations of the tools. The key question in evaluating computer-based decision support tools is whether those tools can augment the native abilities of health care providers during clinical practice, not how they function in isolation as “omniscient oracles” [1, 2]. Practitioners who lose sight of their own clinical judgment when using a decision support tool, instead of seeing the tool as part of a balanced overall approach, do a disservice to themselves and their patients [3].

    In this issue, Selker and colleagues [4] describe a practical decision support tool that rapidly estimates the patient-specific utility of thrombolytic therapy for patients with acute myocardial infarction. Predictive tools facilitate the distillation of a large volume of clinical experience into a convenient form that can be carried to the bedside and applied in a directed manner to a single patient. A 1991 study [5] suggested that as many as one fourth of physicians' unmet needs for information involve tailoring general information from the biomedical literature to fit individual patients. Predictive tools of the type described by Selker and colleagues could reduce critical delays in the initiation of therapies whose value depends on early, short-term administration. Used appropriately, a tool that accurately predicts the outcome of thrombolytic therapy in patients with acute myocardial infarction might save thousands of lives annually in the United States and could substantially reduce the rate of infarction-related illness. Similar tools might help reduce societal resistance to the adoption of medical technological innovations [6].

    However, the development and use of predictive instruments in primary care evokes a concern initially …

    This 100-word excerpt has been provided in the absence of an abstract.

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