Analytical Approaches to Operating Room Management Projects at Lucile Packard Children's Hospital Stanford

被引:0
作者
Scheinker, David [1 ]
Brandeau, Margaret L. [2 ]
机构
[1] Stanford Univ, Lucile Packard Childrens Hosp Stanford, Dept Management Sci & Engn, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Management Sci & Engn, Stanford, CA 94305 USA
来源
HEALTH CARE SYSTEMS ENGINEERING | 2017年 / 210卷
关键词
Healthcare; Operations management; Optimization; Machine learning; Queueing; POSTANESTHESIA CARE-UNIT; ELECTIVE SURGERY; TIMES; MODEL; VARIABILITY; PREDICTION; IMPACT;
D O I
10.1007/978-3-319-66146-9_2
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In recent decades, healthcare has become increasingly expensive, creating pressure on healthcare providers to cut costs while maintaining or improving quality. Operations research can play an important role in supporting such efforts. A key challenge faced by hospital planners is scheduling and management of operating rooms, as operating rooms typically provide highly specialized care, require significant resources, and contribute significantly to a hospital's bottom line. We describe recent work on hospital operating room management at Lucile Packard Children's Hospital Stanford. We describe preliminary outcomes of three projects aimed at improving the efficiency of the hospital's operating rooms: machine learning to improve surgical case length estimation; queuing analysis to improve operational efficiency; and integer programming to schedule cases to reduce surgical delays.
引用
收藏
页码:17 / 26
页数:10
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