Biomathematical Modeling Predicts Fatigue Risk in General Surgery Residents.

Area of Science:

• Medical Education
• Sleep Science
• Occupational Health

Background:

• Resident fatigue poses significant risks in surgical settings.
• Objective and predicted sleep data are crucial for understanding fatigue.
• Biomathematical models offer a quantitative approach to fatigue assessment.

Purpose of the Study:

• To evaluate resident fatigue risk using objective and predicted sleep data.
• To assess the utility of a biomathematical model in predicting surgical resident performance.
• To inform the development of optimized work schedules for surgical residents.

Main Methods:

• Collected 8 weeks of sleep and shift schedule data from 24 surgical residents.
• Utilized a biomathematical model to predict resident performance (effectiveness).
• Analyzed the relationship between shift length, sleep debt, and performance metrics.

Main Results:

• Increased shift lengths correlated with decreased effectiveness scores.
• 11.13% of on-shift time fell below the effectiveness criterion.
• 42.7% of shifts were associated with excess sleep debt.
• Predicted sleep data closely mirrored actual sleep and performance (p ≤ 0.001).

Conclusions:

• Surgical resident sleep patterns and shift demands contribute to fatigue risk.
• Biomathematical modeling effectively predicts resident sleep and performance.
• This tool assists educators in designing work schedules to mitigate fatigue risk.