Nwanaji-Enwerem Jamaji C, Ehrhardt Tori F, Gordon Brittney, Meyer Hannah, Cardell Annemarie, Selby Maurice, Wallace Bradley A, Gittinger Matthew, Siegelman Jeffrey N
Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA.
Gangarosa Department of Environmental Health, Emory Rollins School of Public Health, Atlanta, GA 30322, USA.
Healthcare (Basel). 2024 Mar 8;12(6):612. doi: 10.3390/healthcare12060612.
Few studies explore emergency medicine (EM) residency shift scheduling software as a mechanism to reduce administrative demands and broader resident burnout. A local needs assessment demonstrated a learning curve for chief resident schedulers and several areas for improvement. In an institutional quality improvement project, we utilized an external online cross-sectional convenience sampling pilot survey of United States EM residency programs to collect information on manual versus software-based resident shift scheduling practices and associated scheduler and scheduler-perceived resident satisfaction. Our external survey response rate was 19/253 (8%), with all United States regions (i.e., northeast, southeast, midwest, west, and southwest) represented. Two programs (11%) reported manual scheduling without any software. ShiftAdmin was the most popularly reported scheduling software (53%). Although not statistically significant, manual scheduling had the lowest satisfaction score and programs with ≤30 residents reported the highest levels of satisfaction. Our data suggest that improvements in existing software-based technologies are needed. Artificial intelligence technologies may prove useful for reducing administrative scheduling demands and optimizing resident scheduling satisfaction.
很少有研究将急诊医学（EM）住院医师排班软件作为一种减少行政工作需求和缓解住院医师职业倦怠的机制进行探索。一项本地需求评估显示，总住院医师排班人员存在学习曲线，且有几个需要改进的方面。在一项机构质量改进项目中，我们对美国急诊医学住院医师项目进行了一项外部在线横断面便利抽样试点调查，以收集有关基于手工排班与基于软件排班的住院医师排班实践以及相关排班人员和排班人员感知到的住院医师满意度的信息。我们的外部调查回复率为19/253（8%），涵盖了美国所有地区（即东北部、东南部、中西部、西部和西南部）。有两个项目（11%）报告采用手工排班，没有使用任何软件。ShiftAdmin是报告中最常用的排班软件（53%）。虽然无统计学意义，但手工排班的满意度得分最低，而住院医师人数≤30人的项目报告的满意度水平最高。我们的数据表明，需要改进现有的基于软件的技术。人工智能技术可能有助于减少行政排班需求并优化住院医师排班满意度。
