Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application

Sandeep Rath (), Kumar Rajaram () and Aman Mahajan ()
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Sandeep Rath: Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
Kumar Rajaram: University of California, Los Angeles (UCLA) Anderson School of Management, Los Angeles, California 90095
Aman Mahajan: Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California 90095

Operations Research, 2017, vol. 65, issue 6, 1460-1478

Abstract: We consider the problem of minimizing daily expected resource usage and overtime costs across multiple parallel resources such as anesthesiologists and operating rooms, which are used to conduct a variety of surgical procedures at large multispecialty hospitals. To address this problem, we develop a two-stage, mixed-integer stochastic dynamic programming model with recourse. The first stage allocates these resources across multiple surgeries with uncertain durations and prescribes the sequence of surgeries to these resources. The second stage determines actual start times to surgeries based on realized durations of preceding surgeries and assigns overtime to resources to ensure all surgeries are completed using the allocation and sequence determined in the first stage. We develop a data-driven robust optimization method that solves large-scale real-sized versions of this model close to optimality. We validate and implement this model as a decision support system at the UCLA Ronald Reagan Medical Center. This system effectively incorporates the flexibility in the resources and uncertainty in surgical durations, and explicitly trades off resource usage and overtime costs. This has increased the average daily utilization of the anesthesiologists by 3.5% and of the operating rooms by 3.8%. This has led to an average daily cost savings of around 7% or estimated to be $2.2 million on an annual basis. In addition, the insights based on this model have significantly influenced decision making at the operating services department at this hospital.

Keywords: healthcare operations; mixed integer stochastic dynamic programming; robust optimization (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (23)

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