NURSE STAFFING UNDER DEMAND UNCERTAINTY TO REDUCE COSTS AND ENHANCE PATIENT SAFETY

Abstract

Hospitals must maintain safe nurse-to-patient ratios in patient care units to offer adequate and safe patient care. Since the patient demand is highly variable, during high patient demand periods temporary or overtime nurses are hired to ensure safe nurse-to-patient ratios. These overtime nurses incur higher expense, and are often less effective. We study the problem of permanent nurse staffing level estimation under demand uncertainty as a newsvendor model. Our models are based on limited moment information of the demand distribution. Additionally, we introduce the use of asymmetric cost functions representing overstaffing and understaffing nursing costs. Findings using data from the general surgery and intensive care units at hospitals in Chicago, IL and Augusta, GA are presented. Computational results based on publically available cost data show that 3.1% and 7.3% annual cost savings result by introducing salvage value and newsvendor optimization in intensive care and general care units respectively. This new staffing scheme also improves patient safety as shifts are staffed with more permanent nurses.