For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.

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Hospital beds are a critical but limited resource shared between distinct classes of elective patients. Urgent elective patients are more sensitive to delays and should be treated immediately, whereas regular patients can wait for an extended time. Public hospitals in countries like China need to maximize their revenue and at the same time equitably allocate their limited bed capacity between distinct patient classes. Consequently, hospital bed managers are under great pressure to optimally allocate the available bed capacity to all classes of patients, particularly considering random patient arrivals and the length of patient stay. To address the difficulties, we propose data-driven stochastic optimization models that can directly utilize historical observations and feature data of capacity and demand. First, we propose a single-period model assuming known capacity; since it recovers and improves the current decision-making process, it may be deployed immediately. We develop a nonparametric kernel optimization method and demonstrate that an optimal allocation can be effectively obtained with one year’s data. Next, we consider the dynamic transition of system state and extend the study to a multiperiod model that allows random capacity; this further brings in substantial improvement. Sensitivity analysis also offers interesting managerial insights. For example, it is optimal to allocate more beds to urgent patients on Mondays and Thursdays than on other weekdays; this is in sharp contrast to the current myopic practice.

This paper studies the admission scheduling problem with considering the capacity usage of two interrelated resources (beds and operating rooms) between three consecutive stages of care during surgical patients' admissions to Chinese public hospitals, namely (1) pre-surgical inpatient bed, (2) surgery, (3) post-surgical inpatient bed. Demand comes from two types of patients: (1) emergency patients, who arise randomly and have to be admitted immediately, and (2) elective patients, whose admissions can be scheduled. The authors develop a Markov Decision Process (MDP) model that decides how many elective patients should be admitted each day, with the objective of optimally using both operating room and inpatient bed capacity. The authors demonstrate that the number of elective admissions scheduled each day is monotonically increasing in the state of the system and in the bed capacity, indicating that a higher level of waiting elective patients and available (or total) bed capacity pulls more elective admissions through the system. The total discounted expected cost of the system exhibits decreasing marginal returns as the capacity in each stage increases independently of other stages. Through numerical experiments, there is substantial value in making admission scheduling decisions by jointly considering inpatient beds and operating rooms.