Bed Assignment and Bed Management

Hall, Randolph W.

doi:10.1007/978-1-4614-1734-7_8

Cited by 20 publications

(5 citation statements)

References 42 publications

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“…A survey of bed assignment and bed management is given by Hall [1], who refers to some literature on the policies of admission and discharge scheduling. However, little seems to have been reported so far about the algorithms of allocating individual patients to the beds/rooms in the wards of clinical departments.…”

Section: Operational Analysis and Simulation Of Inpatient Flowsmentioning

confidence: 99%

Progress in the Healthcare Service Innovation Project at the Tsukuba University Hospital

Takagi

Kurata

Okada

et al. 2012

2012 Annual SRII Global Conference

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A joint team of hospital staff and OR/MS researchers launched a healthcare service innovation project at the University of Tsukuba, Japan, in April 2011. We plan to develop a web-based software system for bed allocation to patients by mathematical optimization in the Tsukuba University Hospital. For attaining this goal successfully, we analyze the unique medical network system of Japan and study the expectation of potential patients as well as hospital staff. This is done in parallel with the operational analysis and simulation of patient flows inside the university hospital. In this paper, we describe the scope of our project and share the first-year progress with expert participants in the conference.

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Section: Operational Analysis and Simulation Of Inpatient Flowsmentioning

confidence: 99%

Progress in the Healthcare Service Innovation Project at the Tsukuba University Hospital

Takagi

Kurata

Okada

et al. 2012

2012 Annual SRII Global Conference

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“…Effective schedules are widely studied in manufacturing ( Han, Zhang, Lu, & Lin, 2015;Jonsson & Ivert, 2015;Oyetunji, 2009;Pinedo, 2009;2012;Rahman, Sarker, & Essam, 2015;Sawik, 2011) with the goals of meeting due dates, maximizing machine or labor utilization, and minimizing job lateness, response time, completion time, time in the system, overtime, idle time and work- in-process inventory. A review can be found in Framinan and Ruiz (2010).…”

Section: Related Appointment Scheduling Systemsmentioning

confidence: 99%

“…The management of the operating theatres has been one of the most studied topics in the last 60 years ( Cardoen, 2010;Hans & Vanberkel, 2012 ). Other widely studied topics are nurse scheduling ( Bai, Burke, Kendall, Li, & McCollum, 2010;Burke, De Causmaecker, Berghe, & Van Landeghem, 2004;Lim, Mobasher, Kardar, & Cote, 2012 ), patient appointments in ambulatory care ( Gupta & Wang, 2012 ), appointment scheduling in outpatient clinics ( Berg & Denton, 2012 ), bed assignments in hospitals ( Hall, 2012 ), scheduling of urgent patients ( Gerchak, Gupta, & Henig, 1996;Klassen & Rohleder, 2003;Torkki, Alho, Peltokorpi, Torkki, & Kallio, 2006 ), nurse and surgery scheduling ( Beliën & Demeulemeester, 2008 ), and trade-offs between the cancellation of scheduled elective surg-eries to accommodate urgent arrivals (Zonderland, Boucherie, Lit-vak, & Vleggeert-Lankamp, 2010 ).…”

Section: Related Appointment Scheduling Systemsmentioning

confidence: 99%

An appointment scheduling framework to balance the production of blood units from donation

Baş

Carello

Lanzarone

et al. 2018

European Journal of Operational Research

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“…In the healthcare literature, researchers have focused on surge demand and timely responses by coordinating the flows between the various units for solving patient congestion within a single hospital (Hall, 2012;Helm and Van Oyen, 2014). Schultz and Koenig (2006) assert a benchmark surge capacity of 20% to 30% of a hospital's normal capacity.…”

Section: Surge Demand and Timely Responsesmentioning

confidence: 99%

Optimal admission and queuing control with reneging behavior under premature discharge decisions

Yang

Zhang

et al. 2023

Int Trans Operational Res

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Queues or waiting lines are a common phenomenon in healthcare facilities. The increasing number of admissions to hospital emergency departments (EDs) has resulted in overcrowded EDs and decreased quality of care. Patients may renege with dissatisfaction while waiting, prolonged hospital stays, or the cancelation of surgeries, especially for those with acute diseases. This paper studies patient admission and queuing control policies, considering premature discharge decisions and reneging behavior. First, we formulate this problem as an infinite‐horizon total discounted cost Markov decision process to balance bed utility and experiences of patients waiting for admission to minimize the total expected cost for the hospital. Then, we characterize an optimal admission threshold policy by analyzing the structural properties of the optimal value function. Numerical experiments are employed to discuss how the optimal dynamic policy depends on some key parameters of the system. Finally, the performance of the optimal policy is discussed through comparison with the benchmark: A myopic rule (first come, first served) mimicking the decision‐making in practice under different scenarios. The numerical results show that the reneging behavior can affect the superiority of the optimal policy compared to the benchmark, and the optimal policy can significantly reduce the number of waiting patients without increasing the total expected cost when the system is overloaded.

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Bed Assignment and Bed Management

Cited by 20 publications

References 42 publications

Progress in the Healthcare Service Innovation Project at the Tsukuba University Hospital

Progress in the Healthcare Service Innovation Project at the Tsukuba University Hospital

An appointment scheduling framework to balance the production of blood units from donation

Optimal admission and queuing control with reneging behavior under premature discharge decisions

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