Internal Medicine residents use heuristics to estimate disease probability
| Autor: | Pietro Ravani, Bruce Wright, Kevin McLaughlin, Jeffrey P. Schaefer, Sen Han Phang |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Medicine (General)
medicine.medical_specialty Heuristic Computer science Residents Bayesian reasoning Education (General) Major Contribution/Research Article Attribute substitution Empirical probability Bayesian inference Representativeness heuristic Confidence interval Odds R5-920 Internal medicine medicine Heuristics General Materials Science L7-991 Probability |
| Zdroj: | Canadian Medical Education Journal, Vol 6, Iss 2 (2015) Canadian Medical Education Journal |
| ISSN: | 1923-1202 |
| DOI: | 10.36834/cmej.36653 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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