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Model-Based Operative Performance Expectations for Quantifying Competency in General Surgery.

Andrew E Krumm^1,2,3, Kayla Marcotte^1,3, Brian C George^1,3,4

¹Department of Learning Health Sciences, University of Michigan, Ann Arbor.

|March 8, 2023

Summary

This summary is machine-generated.

Past surgical performance predicts future success. This study used workplace-based assessment (WBA) data to develop metrics for surgeon competence, advancing graduate medical education.

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Area of Science:

Medical Education
Surgical Training
Competency Assessment

Background:

Translating workplace-based assessment (WBA) ratings into objective metrics is crucial for evaluating surgeon competency.
Current methods lack standardized approaches to quantify surgical skill progression.

Purpose of the Study:

To assess the association between past and future performance in general surgery trainees.
To develop a comprehensive system for evaluating point-in-time surgical competence.

Main Methods:

Utilized longitudinal WBA ratings from the Society for Improving Medical Professional Learning (SIMPL) system (2015-2021).
Included 2605 general surgery residents across 70 US programs, rated by 1884 attending surgeons.
Employed Bayesian generalized linear mixed-effects models and marginal predicted probabilities for analysis.

Main Results:

A significant positive association was found between prior and future surgical performance (β=0.13).
Postgraduate year was the largest source of performance variation, followed by rater, procedure, and case complexity.
The developed model demonstrated strong discrimination (AUROC=0.81) and good calibration for predicting performance.

Conclusions:

Prior surgical performance is a reliable predictor of future operative success.
The proposed modeling strategy offers a robust method for quantifying surgical competence based on performance expectations.