Stochastic bed balancing of an obstetrics hospital.

Health care is one of the largest industries in the developed world and the top domestic industry in the United States (US). Over the past thirty years there has been a dramatic increase in healthcare costs in the US, of which about one-third can be attributed to hospital spending. One of the key factors in hospital cost containment and revenue enhancement is effective and efficient bed planning and capacity analysis. This study aims to balance bed unit utilizations across an obstetrics hospital and minimize the blocking of beds from upstream units within given constraints on bed reallocation. The methodology includes the assessment and effect of time-dependent patterns of monthly, daily, and hourly demand. Queuing networks are first used to assess the flows between units, establish target utilizations of bed units, and involve stakeholders in a flow characterization that they understand. Discrete-event simulation is then used to maximize the flow through the balanced system including non-homogeneous effects, non-exponential lengths of stay, and blocking behavior. Results of the models are validated against actual data collected from the hospital. Several 'what if' scenarios are studied showing that 38% more patient flow can be achieved with only 15% more patient beds. The results of the study have been implemented.