Image Theory's counting rule in clinical decision making: Does it describe how clinicians make patient-specific forecasts?
The field of clinical decision making is polarized by two predominate views. One holds that treatment recommendations should conform with guidelines; the other emphasizes clinical expertise in reaching case-specific judgments. Previous work developed a test for a proposed alternative, that clinical judgment should systematically incorporate both general knowledge and patient-specific information. The test was derived from image theory's two phase-account of decision making and its ``simple counting rule'', which describes how possible courses of action are pre-screened for compatibility with standards and values. The current paper applies this rule to clinical forecasting, where practitioners indicate how likely a specific patient will respond favorably to a recommended treatment. Psychiatric trainees evaluated eight case vignettes that exhibited from 0 to 3 incompatible attributes. They made two forecasts, one based on a guideline recommendation, the other based on their own alternative. Both forecasts were predicted by equally- and unequally-weighted counting rules. Unequal weighting provided a better fit and exhibited a clearer rejection threshold, or point at which forecasts are not diminished by additional incompatibilities. The hypothesis that missing information is treated as an incompatibility was not confirmed. There was evidence that the rejection threshold was influenced by clinician preference. Results suggests that guidelines may have a de-biasing influence on clinical judgment. Subject to limitations pertaining to the subject sample and population, clinical paradigm, guideline, and study procedure, the data support the use of a compatibility test to describe how clinicians make patient-specific forecasts.
Volume (Year): 7 (2012)
Issue (Month): 3 (May)
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- Alexander, Marcus & Christakis, Nicholas A., 2008. "Bias and asymmetric loss in expert forecasts: A study of physician prognostic behavior with respect to patient survival," Journal of Health Economics, Elsevier, vol. 27(4), pages 1095-1108, July.
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