Abstract

© 2021 Elsevier B.V.Management of hospital beds is a high-impact issue for two-tier healthcare systems, due principally to their critical importance and high costs. Bed capacity in the public sector is generally insufficient to provide immediate care to all critical patients and thus a significant proportion of public expenditure is assigned to the diversion of patients for treatment in the private sector. We formulate and approximately solve a discounted infinite-horizon Markov Decision Process (MDP) that seeks to identify cost-effective policies for transferring ICU patients between hospitals or diverting them to private clinics. The solution approach employs an affine architecture for approximating the value function of the MDP model and solves the equivalent linear programming model using column generation. The approach can handle a high level of dimensionality, enabling it to consider the arriving patients’ many different diagnostic groups and their corresponding lengths of stay. The decisions generated through this approach often differ from the intuitive ones produced in a typical day-by-day decision process, that does not consider the impact of the current day's decisions on the future performance of the system. In particular, the resulting policies will in many cases proactively transfer patients to a different public facility or divert them to a private one even though the hospital they first arrived at had beds available. The performance of the proposed method was evaluated by simulating a case study based on data from a hospital network in Santiago, Chile, producing savings of almost 49% due mostly to reduced usage of private services.