This paper concerns the problem of inpatient bed allocation for two classes of patients(scheduled and non-scheduled)when there is uncertainty about daily available capacity.In the afternoon of each day,patients from t...This paper concerns the problem of inpatient bed allocation for two classes of patients(scheduled and non-scheduled)when there is uncertainty about daily available capacity.In the afternoon of each day,patients from the scheduled class,also called backlogged elective admissions,are selected from a waiting list,for the admission on the next day.The non-scheduled class,also called emergent admissions,are new requests that arise randomly each day with emergent needs.The capacity of available beds for a medical specialty to provide hospitalization services is uncertain when backlogged elective pa-tients are chosen.Admitting too many of elective patients may result in exceeding a day’s capacity,which can potentially necessitate"overflowing"or"postponing"some emergent requests that should be performed as soon as possible.Therefore,the problem faced by the medical specialty facility at the decision-making point of each day is how many of the backlogged elective patients can be admitted.We formulate this problem as a Markov decision process(MDP)and study the structural properties of the model to characterize the nature of the optimal policy.We propose easy-to-implement policies(the fixed quota policy and the best fixed quota policy),which perform well under fitted distributions.By reporting numerical analyses using real data from a Chinese public hospital,we finally compare the improvements that our proposed solutions could bring to the hospital with the existing practices under several different cost structures.展开更多
基金This research is supported by National Natural Science Foundation of China(No.71532007).
文摘This paper concerns the problem of inpatient bed allocation for two classes of patients(scheduled and non-scheduled)when there is uncertainty about daily available capacity.In the afternoon of each day,patients from the scheduled class,also called backlogged elective admissions,are selected from a waiting list,for the admission on the next day.The non-scheduled class,also called emergent admissions,are new requests that arise randomly each day with emergent needs.The capacity of available beds for a medical specialty to provide hospitalization services is uncertain when backlogged elective pa-tients are chosen.Admitting too many of elective patients may result in exceeding a day’s capacity,which can potentially necessitate"overflowing"or"postponing"some emergent requests that should be performed as soon as possible.Therefore,the problem faced by the medical specialty facility at the decision-making point of each day is how many of the backlogged elective patients can be admitted.We formulate this problem as a Markov decision process(MDP)and study the structural properties of the model to characterize the nature of the optimal policy.We propose easy-to-implement policies(the fixed quota policy and the best fixed quota policy),which perform well under fitted distributions.By reporting numerical analyses using real data from a Chinese public hospital,we finally compare the improvements that our proposed solutions could bring to the hospital with the existing practices under several different cost structures.