Improving the efficiency of the operating room environment with an optimization and machine learning model View Full Text


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Article Info

DATE

2018-11-01

AUTHORS

Michael Fairley, David Scheinker, Margaret L. Brandeau

ABSTRACT

The operating room is a major cost and revenue center for most hospitals. Thus, more effective operating room management and scheduling can provide significant benefits. In many hospitals, the post-anesthesia care unit (PACU), where patients recover after their surgical procedures, is a bottleneck. If the PACU reaches capacity, patients must wait in the operating room until the PACU has available space, leading to delays and possible cancellations for subsequent operating room procedures. We develop a generalizable optimization and machine learning approach to sequence operating room procedures to minimize delays caused by PACU unavailability. Specifically, we use machine learning to estimate the required PACU time for each type of surgical procedure, we develop and solve two integer programming models to schedule procedures in the operating rooms to minimize maximum PACU occupancy, and we use discrete event simulation to compare our optimized schedule to the existing schedule. Using data from Lucile Packard Children's Hospital Stanford, we show that the scheduling system can significantly reduce operating room delays caused by PACU congestion while still keeping operating room utilization high: simulation of the second half of 2016 shows that our model could have reduced total PACU holds by 76% without decreasing operating room utilization. We are currently working on implementing the scheduling system at the hospital. More... »

PAGES

1-12

References to SciGraph publications

  • 2017-08. Improving predictions of pediatric surgical durations with supervised learning in INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
  • 2009. Additive Models, Trees, and Related Methods in THE ELEMENTS OF STATISTICAL LEARNING
  • 2009. Boosting and Additive Trees in THE ELEMENTS OF STATISTICAL LEARNING
  • 2011. Introduction to Stochastic Programming in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10729-018-9457-3

    DOI

    http://dx.doi.org/10.1007/s10729-018-9457-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1107967391

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30387040


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