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Integrating Bayesian reasoning into medical education using smartphone apps.

Benjamin Kinnear1, Philip A Hagedorn1, Matthew Kelleher2

  • 1Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, OH, USA.

Diagnosis (Berlin, Germany)
|March 1, 2019
PubMed
Summary

This study introduces practical Bayesian reasoning tools for medical education, including visual models and smartphone apps. Chief residents found these methods effective for learning and teaching diagnostic reasoning, potentially reducing errors.

Keywords:
Bayesiandiagnostic reasoninglikelihood ratioprobabilitiessmartphone

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Area of Science:

  • Medical Education
  • Clinical Reasoning
  • Bayesian Statistics

Background:

  • Diagnostic errors significantly impact patient morbidity and mortality.
  • Improving diagnostic reasoning is crucial in medical education.
  • Clinician educators often lack tools for applying Bayesian reasoning in practice.

Purpose of the Study:

  • To develop and evaluate an interactive workshop for teaching Bayesian reasoning in clinical practice.
  • To provide practical tools for educators to incorporate Bayesian concepts into medical training.
  • To address the gap in understanding and application of Bayesian principles in diagnostics.

Main Methods:

  • Developed an interactive workshop incorporating visual probability models and thresholds.
  • Utilized clinical case studies to illustrate Bayesian concepts.
  • Integrated readily available smartphone applications for practical learning.

Main Results:

  • High satisfaction reported from chief residents over three years of workshops.
  • Learners found visual and smartphone tools beneficial for applying Bayesian reasoning.
  • Positive feedback indicated the utility of the tools for future teaching.

Conclusions:

  • Visual models, clinical cases, and smartphone apps are effective tools for learning and teaching Bayesian reasoning.
  • The developed methods were well-received by internal medicine chief residents.
  • Further research is needed to assess the impact on diagnostic accuracy and patient outcomes.