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Phang SH, Ravani P, Schaefer J, et al. Internal Medicine residents use heuristics to estimate disease probability. Can Med Educ J. 2015;6(2):e71-7
Phang, S. H., Ravani, P., Schaefer, J., Wright, B., & McLaughlin, K. (2015). Internal Medicine residents use heuristics to estimate disease probability. Canadian medical education journal, 6(2), e71-7.
Phang, Sen Han, et al. "Internal Medicine residents use heuristics to estimate disease probability." Canadian medical education journal vol. 6,2 (2015): e71-7.
Phang SH, Ravani P, Schaefer J, Wright B, McLaughlin K. Internal Medicine residents use heuristics to estimate disease probability. Can Med Educ J. 2015 Dec 11;6(2):e71-7. eCollection 2015. PMID: 27004080; PMCID: PMC4795085.
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Can Med Educ J. 2015 Dec 11;6(2):e71-7. eCollection 2015.

Internal Medicine residents use heuristics to estimate disease probability.

Canadian medical education journal

Sen Han Phang, Pietro Ravani, Jeffrey Schaefer, Bruce Wright, Kevin McLaughlin

Affiliations

  1. Department of Medicine, University of Calgary, Calgary, Alberta.
  2. Office of Undergraduate Medical Education, University of Calgary, Calgary, Alberta.
  3. Department of Medicine, University of Calgary, Calgary, Alberta; Office of Undergraduate Medical Education, University of Calgary, Calgary, Alberta.

PMID: 27004080 PMCID: PMC4795085

Abstract

BACKGROUND: Training in Bayesian reasoning may have limited impact on accuracy of probability estimates. In this study, our goal was to explore whether residents previously exposed to Bayesian reasoning use heuristics rather than Bayesian reasoning to estimate disease probabilities. We predicted that if residents use heuristics then post-test probability estimates would be increased by non-discriminating clinical features or a high anchor for a target condition.

METHOD: We randomized 55 Internal Medicine residents to different versions of four clinical vignettes and asked them to estimate probabilities of target conditions. We manipulated the clinical data for each vignette to be consistent with either 1) using a representative heuristic, by adding non-discriminating prototypical clinical features of the target condition, or 2) using anchoring with adjustment heuristic, by providing a high or low anchor for the target condition.

RESULTS: When presented with additional non-discriminating data the odds of diagnosing the target condition were increased (odds ratio (OR) 2.83, 95% confidence interval [1.30, 6.15], p = 0.009). Similarly, the odds of diagnosing the target condition were increased when a high anchor preceded the vignette (OR 2.04, [1.09, 3.81], p = 0.025).

CONCLUSIONS: Our findings suggest that despite previous exposure to the use of Bayesian reasoning, residents use heuristics, such as the representative heuristic and anchoring with adjustment, to estimate probabilities. Potential reasons for attribute substitution include the relative cognitive ease of heuristics vs. Bayesian reasoning or perhaps residents in their clinical practice use gist traces rather than precise probability estimates when diagnosing.

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