Training Disruptions: Predicting Risk of Deficiency Using Resident Case Logs

Tilva, K. R.; Christian, N.; Travis, C.; Truitt, M. S.; Shames, B. D.

Introduction: Surgical case logs are tracked via online database. No tools are available to determine whether a resident will meet minimum case requirements. Disruptions to surgical case volume, such as pandemics or leaves, make predicting case log progress difficult. The purpose of this study was to create a webpage-based risk tool to predict areas of concern based on current case logs, program case volume, and disruptions to training. Methods: A surgical case volume risk calculator was created in MATLAB then converted into a website. Baseline data were from a large, single training institution. A first order differential equation model calculates risk of deficiency in 13 major case categories. The model assumes a linear relationship between average case rate per resident per category, including qualifiers such as training site, disruption to case volume, and current case volume. The model calculates risk ranging from a recoverable deficit (moderate) with case redistribution, to critical deficit (high) based on inadequate program total case volume. Results: Using baseline model institutional data, a 12-month disruption to normal case volume from COVID-19 pandemic for a second-year surgical resident, critical deficiencies were predicted in breast and endoscopy (Fig. 1). Conclusion: Disruptions in clinical training result in case deficiencies, and risk evaluation can be captured. Modification of schedules, rotations, and curricula will be different based on major category, and moderate vs critical deficits. The next steps are to expand the core data set to multiple institutions and validate with external sites and varied disruptions. [Formula presented]