Balancing patient demand and hospital resources: a newsvendor-based approach to bed and nurse optimization

Abstract

Using a two-period Newsvendor-based model, this study examines a data-driven strategy for optimizing nurse staffing and bed allocation in private hospitals. Leveraging a framework initially aimed at optimizing airline seat inventory, the model incorporates probabilistic demand estimation and overbooking strategies to balance patient demand with hospital resources. Despite limitations in assumptions, such as linear cost structures and parameter estimation, the analysis based on data from a cardiac unit in India reveals valuable insights for hospital management, demonstrating the model’s potential to improve revenue and minimize costs associated with over and understaffing.