How many beds must be allocated to a specific clinical ward to meet production targets? When budgets get tight, what are the effects of downsizing a nursing unit? These questions are often discussed by medical professionals, hospital consultants, and managers. In these discussions the occupancy rate is of great importance and often used as an input parameter. Most hospitals use the same target occupancy rate for all wards, often 85%. Sometimes an exception is made for critical care and intensive care units. In this paper we demonstrate that this equity assumption is unrealistic and that it might result in an excessive number of refused admissions, particularly for smaller units. Queuing theory is used to quantify this impact. We developed a decision support system, based on the Erlang loss model, which can be used to evaluate the current size of nursing units. We validated this model with hospital data over the years [2004][2005][2006]. Finally, we demonstrate the efficiency of merging departments.

This study investigates the bottlenecks in the emergency care chain of cardiac in-patient flow. The primary goal is to determine the optimal bed allocation over the care chain given a maximum number of refused admissions. Another objective is to provide deeper insight in the relation between natural variation in arrivals and length of stay and occupancy rates. The strong focus on raising occupancy rates of hospital management is unrealistic and counterproductive. Economies of scale cannot be neglected. An important result is that refused admissions at the First Cardiac Aid (FCA) are primarily caused by unavailability of beds downstream the care chain. Both variability in LOS and fluctuations in arrivals result in large workload variations. Techniques from operations research were successfully used to describe the complexity and dynamics of emergency in-patient flow.

For capacity planning issues in health care, such as the allocation of hospital beds, the admissions rate of patients is commonly assumed to be constant over time. In addition to the purely random fluctuations, there is also typically a predictable pattern in the number of arriving patients. For example, roughly 2/3 of the admitted patients at an Intensive Care Unit arrives during office hours. Also, most of the scheduled admissions occur during weekdays instead of during the weekend.Using approximations based on the infinite-server queue, we analyze an M t /H /s/s model to determine the impact of the time-dependent arrival pattern on the required number of operational beds and fraction of refused admissions for clinical wards. In particular, the results show that the effect of the daily pattern is rather limited for clinical wards in contrast to the week-weekend pattern, for which the difference in the fraction of refused admissions across the week is considerable. We also show that an increased variability in length of stay distribution has a stabilizing effect on the time-dependent required number of beds. Finally, we demonstrate a method to determine the required number of beds across the week.Keywords Hospital capacity planning · Daily and weekly admission patterns · Time-dependent arrivals · Refused admissions · Infinite-server queues · Modified-offered-load approximation

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