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Analytical Approaches to Operating Room Management

Projects at Lucile Packard Children’s Hospital Stanford
Conference paper
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Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 210)

Abstract

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.

Keywords

Healthcare Operations management Optimization Machine learning Queueing 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Management Science and EngineeringStanford University, Lucile Packard Children’s Hospital StanfordStanfordUSA
  2. 2.Department of Management Science and EngineeringStanford UniversityStanfordUSA

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