Musculoskeletal surgeons use mixed reasoning rather than pure Bayesian strategies in clinical practice

by Robert Parisien, Alexander Drost, Amin Razi, Sina Ramtin, David Ring, Stein J. Janssen

Objectives

To inform efforts to promote regular and normalized Bayesian reasoning, we studied factors associated with the degree to which surgeons use Bayesian reasoning to navigate uncertainty across different clinical scenarios.

Methods

Science of Variation Group members (153; 58% North America, 30% Europe, 69% over 15 years of experience) completed an online survey reading 8 scenarios of test and treatment decisions and chose one of 4 answer options with higher scores indicating more Bayesian reasoning. Internal consistency of the survey was assessed using Cronbach alpha.

Results

The average Bayesian reasoning score across all scenarios was 3.0 (IQR 2.7–3.2) on a 4-point scale, indicating a relative context-dependent variability. Completely non-Bayesian reasoning was selected least often (8.6%, 90 of 1,044) and fully Bayesian reasoning represented 29% (301 of 1,044) of responses. Most surgeons showed mixed patterns (defined as reasoning in which prior probability is acknowledged but underweighted, without explicit probabilistic updating): 85% (121 of 142) used fully Bayesian reasoning at least once (121 of 142) while 42% (60 of 142) used completely non-Bayesian reasoning at least once. The Cronbach alpha was 0.43 suggesting the scenarios measured different aspects of clinical reasoning rather a unified construct.

Conclusions

The finding that surgeons use relatively context-dependent reasoning suggests an opportunity for surgeons to develop and practice Bayesian reasoning strategies both in training programs and in practice.