Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents

Abstract

Objective: To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue.

Design: 8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness").

Setting: Greater Washington, DC area hospitals RESULTS: As shift lengths increased, effectiveness scores decreased and the time spent below criterion increased. Additionally, 11.13% of time on shift was below the effectiveness criterion and 42.7% of shifts carried excess sleep debt. Sleep prediction was similar to actual sleep, and both predicted similar performance (p ≤ 0.001).

Conclusions: Surgical resident sleep and shift patterns may create fatigue risk. Biomathematical modeling can aid the prediction of resident sleep patterns and performance. This approach provides an important tool to help educators in creating work-schedules that minimize fatigue risk.

Keywords: ACGME; Duty-hours; Residents; Shift scheduling.