Implementation of a data-driven robust optimization model for scheduling increased average daily utilization of anesthesiologists by 3.5% and operating rooms by 3.8%, saving ~$2.2 million annually.
Implementation of a stochastic dynamic programming model for surgery scheduling increased resource utilization and saved an estimated $2.2 million annually at a major medical center.
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. The e-companion is available at https: //doi. org/10. 1287/opre. 2017. 1634.
Rath et al. (Thu,) conducted a other in Surgical procedures. Two-stage, mixed-integer stochastic dynamic programming model (decision support system) was evaluated on Average daily utilization and cost savings. Implementation of a data-driven robust optimization model for scheduling increased average daily utilization of anesthesiologists by 3.5% and operating rooms by 3.8%, saving ~$2.2 million annually.