Institutes for Behavior Resources, Baltimore, Maryland.
Institutes for Behavior Resources, Baltimore, Maryland.
J Surg Educ. 2021 Nov-Dec;78(6):2094-2101. doi: 10.1016/j.jsurg.2021.04.007. Epub 2021 May 13.
To assess resident fatigue risk using objective and predicted sleep data in a biomathematical model of fatigue.
8-weeks of sleep data and shift schedules from 2019 for 24 surgical residents were assessed with a biomathematical model to predict performance ("effectiveness").
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).
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.
使用疲劳的生物数学模型中的客观和预测睡眠数据评估住院医师的疲劳风险。
使用生物数学模型评估 24 名外科住院医师 2019 年 8 周的睡眠数据和轮班时间表，以预测表现（“效果”）。
华盛顿特区地区医院。
随着轮班时间的延长，效果评分下降，低于标准的时间增加。此外，轮班时间的 11.13%低于效果标准，42.7%的轮班有额外的睡眠债务。睡眠预测与实际睡眠相似，两者都预测了相似的表现（p≤0.001）。
外科住院医师的睡眠和轮班模式可能会造成疲劳风险。生物数学模型可以帮助预测住院医师的睡眠模式和表现。这种方法提供了一个重要的工具，帮助教育工作者制定最大限度地减少疲劳风险的工作时间表。
