利用电子健康记录数据实时估算外科住院医师的工作时间和工作量。
Estimation of Surgical Resident Duty Hours and Workload in Real Time Using Electronic Health Record Data.
机构信息
Department of Surgery, University of California San Francisco, San Francisco, California.
Department of Medicine, University of California San Francisco, San Francisco, California.
出版信息
J Surg Educ. 2021 Nov-Dec;78(6):e232-e238. doi: 10.1016/j.jsurg.2021.08.011. Epub 2021 Sep 8.
OBJECTIVE
To explore the use of electronic health record (EHR) data to estimate surgery resident duty hours and monitor real time workload.
DESIGN
Retrospective analysis of resident duty hours logged using a voluntary global positioning system (GPS)-based smartphone application compared to duty hour estimates by an EHR-based algorithm. The algorithm estimated duty hours using EHR activity data and operating room logs. A dashboard was developed through Plan-Do-Study-Act cycles for real-time monitoring of workload.
SETTING
Single tertiary/quaternary medical center general surgery residency program with approximately 90 categorical, preliminary, and integrated residents at eight clinical sites.
PARTICIPANTS
Categorical, preliminary, and integrated surgery residents of all clinical years who volunteered to pilot a GPS application to track duty hours.
RESULTS
Of 2,623 work periods by 59 residents were logged with both methods. EHR-estimated work periods started later than GPS logs (median 0.3 hours, interquartile range [IQR] -0.1 - 0.3); EHR-estimated work periods ended earlier than GPS logs (median 0.1 hours, IQR -0.7 - 0.3); and EHR-estimated duty hour totals were less than totals logged by GPS (median -0.3 hours, IQR -0.8 - +0.1). Overall correlation between weekly duty hours logged by EHR and GPS was 0.79. Correlations between the 2 systems stratified from PGY-1 through PGY-5 were 0.76, 0.64, 0.82, 0.87, and 0.83, respectively. The algorithm identified six 80-hour workweek violations (averaged over 4 weeks), while GPS logs identified 8. EHR-based duty hours and operational data were integrated into a dashboard to enable real time monitoring of resident workloads.
CONCLUSIONS
EHR-based estimation of surgical resident duty hours has good correlation with GPS-based assessment of duty hours and identifies most workweek duty hour violations. This approach allows for dynamic workload monitoring and may be combined with operational data to anticipate and prevent duty hour violations, thereby optimizing learning.
目的
探索使用电子健康记录(EHR)数据来估计手术住院医师的工作时间并实时监测实际工作量。
设计
使用自愿使用基于全球定位系统(GPS)的智能手机应用程序记录住院医师工作时间的回顾性分析,与基于 EHR 的算法估算的工作时间进行比较。该算法使用 EHR 活动数据和手术室日志估算工作时间。通过计划-执行-研究-行动(PDCA)循环开发了一个仪表板,用于实时监测工作量。
地点
单一大型教学医院普外科住院医师培训计划,有 8 个临床站点,约有 90 名住院医师,包括普通外科、初级外科和综合外科住院医师。
参与者
所有临床年级的普通外科、初级外科和综合外科住院医师自愿试用 GPS 应用程序来跟踪工作时间。
结果
59 名住院医师共记录了 2623 个工作周期,两种方法均记录了工作时间。EHR 估算的工作时间比 GPS 日志开始晚(中位数为 0.3 小时,四分位距 [IQR] -0.1 - 0.3);EHR 估算的工作时间比 GPS 日志结束早(中位数 0.1 小时,IQR -0.7 - 0.3);EHR 估算的总工作时间少于 GPS 记录的总工作时间(中位数 -0.3 小时,IQR -0.8 - +0.1)。EHR 记录的每周工作时间与 GPS 记录的每周工作时间之间的总体相关性为 0.79。按 PGY-1 至 PGY-5 分层的两种系统之间的相关性分别为 0.76、0.64、0.82、0.87 和 0.83。该算法发现了 6 起 80 小时工作周违规事件(平均持续 4 周),而 GPS 日志发现了 8 起。EHR 基础的工作时间和运营数据已整合到一个仪表板中,以实现对住院医师工作量的实时监控。
结论
基于 EHR 的外科住院医师工作时间估算与基于 GPS 的工作时间评估具有良好的相关性,并确定了大多数工作周工作时间违规行为。这种方法允许动态的工作量监测,并可结合运营数据来预测和防止工作时间违规,从而优化学习。