TITLE:   Reducing Operating Room Labor Costs: Capturing
Workload Information & Dynamic Adjustments of Staffing Level

SPEAKER:    Professor Polly Biyu He

ABSTRACT:

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.