Reducing Operating Room Labor Costs: Capturing Workload Information & Dynamic Adjustments of Staffing Level
TITLE: Reducing Operating Room Labor Costs: Capturing Workload Information & Dynamic Adjustments of Staffing Level
SPEAKER: Professor Polly Biyu He
We study the problem of setting nurse staffing levels in hospital operating rooms when there is uncertainty about the daily workload. We demonstrate in this healthcare service setting how information availability and choices of decision models affect a newsvendor's performance. We develop empirical models to predict the daily workload distribution and study how its mean and variance change with the information available. In particular, we consider different information sets available at the time of decision: no information, information on number of cases, and information on number and types of elective cases. We use these models to derive optimal staffing rules based on historical data from a US teaching hospital and prospectively test the performance of these rules. Our empirical results suggest that hospitals could potentially reduce their staffing costs by an average of 39-49% (depending on the absence or presence of emergency cases) by deferring the staffing decision until procedure-type information is available. However, in reality, contractual and scheduling constraints often require operating room managers to reserve staffed hours several months in advance, when little information about the cases is known. This motivates us to consider the problem of adjusting the staffing level given information updates. Specifically, we develop decision models that allow the OR manager to adjust the staffing level with some adjustment costs when he or she has better information. We study how adjustment costs affect the optimal staffing policy and the value of having the flexibility to adjust staffing. We also demonstrate how to implement our adjustment policies by applying the optimal decision rules derived from our models to the hospital data.
Joint work with Stefano Zenios, Franklin Dexter and Alex Macario.