数据驱动的大型医院手术室运营预测与优化方法研究

Optimizing Large Hospital Operating Rooms with Data Analytics

【目的】研究相关性分析、关联规则挖掘和时间序列预测方法在手术室运营预测与优化的应用。【方法】基于50000余例手术记录数据,分别提出手术指标相关性分析、手术室资源关联规则挖掘和手术量时间序列预测方法,并据此探讨大型医院手术室运营预测与优化策略。【结果】75%的手术操作时长与其他手术指标呈强线性相关性。FP-Growth算法在最小置信度0.85下能获得可靠的手术室资源使用规律。利用周手术量时间序列提高至少37.5%的预测精确度。【局限】所用的手术室运营数据没有与其他医疗信息系统的数据关联,限制了该方法应用在医院其他部门的运营优化。同时,该方法在不同的医院运营环境中还需要进一步检验。【结论】所提方法为实现数据驱动的大型医院手术室运营预测与优化目标提供方法论指导。

[Objective]This study aims to optimize the management of large hospital operating rooms with the help of data analytics.[Methods]We collected about fifty thousand surgical cases from one large hospital in China.Then,we conducted regression analysis for correlation of surgical indicators,cluster analysis and association rule mining for resource usage,as well as the time series forecasting to predict the number of surgical cases.Finally,we discussed optimization strategies for operating rooms.[Results]The duration of 75%surgical procedures showed significant ties with other indicators.The FP-Growth algorithm with minimum confidence of 0.85 identified reliable patterns of resource usage.The accuracy of time series forecasting was increased by at least 37.5%due to the use of weekly numbers of surgical procedures.[Limitations]We did not link the operating room dataset with records from other medical information systems.Therefore,the proposed method might not work for other hospital departments.Meanwhile,our method needs to be examined with data from other hospitals.[Conclusions]This study could help to optimize the large hospital operating rooms.