Balancing operating theatre and bed capacity in a cardiothoracic centre

Bowers, John

doi:10.1007/s10729-013-9221-7

Cited by 31 publications

(24 citation statements)

References 25 publications

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“…The theorem above suggests a diminishing return as the capacity upstream or downstream increases. This trend has been described previously in a simulation study by Bowers (). With greater investments in capacity, we eventually experience lower marginal returns when patient demand remains the same.…”

Section: Relationship To Capacitysupporting

confidence: 83%

Integrated Scheduling and Capacity Planning with Considerations for Patients’ Length‐of‐Stays

Liu

Truong

Wang³

et al. 2019

Production and Operations Management

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Despite the fact that hospital care is often delivered in successive stages, current healthcare scheduling and capacity planning methods usually treat different hospital units in isolation. To address such a shortcoming, we introduce the first Markov decision process model for scheduling surgical patients on a daily basis, explicitly taking into account patient length‐of‐stay in hospital after surgeries and inpatient census. By way of a simple and yet innovative variable transformation, we reveal the hidden submodularity structure in our model. This transformation, in particular, allows us to show that the optimal number of patients to admit increases when the waitlist of surgical patients is longer, given the number of patients recovering downstream is fixed. We conduct extensive simulation experiments to study the applicability of our theoretical model in various settings. Our simulations based on real data demonstrate substantial values in making integrated scheduling decisions that simultaneously consider capacity usage at all locations in a hospital, especially when demand and system capacities are balanced or more elective patients present in the patient mix. The traditional scheduling policy, which is solely driven by operating room usage, however, can lead to significantly suboptimal use of downstream capacity and, as our numerical experiments show, may result in up to a three‐fold increase in total expenses. In contrast, a scheduling policy based on downstream capacity usage often performs relatively close to the integrated scheduling policy, and therefore may serve as a simple, effective scheduling heuristic for hospital managers—especially when the downstream capacity is costly and less flexible.

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Section: Relationship To Capacitysupporting

confidence: 83%

Integrated Scheduling and Capacity Planning with Considerations for Patients’ Length‐of‐Stays

Liu

Truong

Wang³

et al. 2019

Production and Operations Management

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“…Recently, Ferrand et al [96] have researched a setting with a combination of dedicated and flexible ORs and show that it outperforms, in terms of patient waiting time and OR overtime, both the settings with shared ORs as well as the ones Table 3 The type of patient that is considered in articles is not always specified and, especially for the elective patient case, it is not always clear whether an inpatient or outpatient setting is researched Elective Inpatient [1,2,12,13,14,15,21,22,24,33,35,40,41,47,49,57,59,69,85,88,98,101,104,111,123,132,135,136,144,146,155,156,164,165,166,175,176,177,182,188,189,190,201,206,211,212,214,224,233,234,…”

Section: Patient Characteristicsmentioning

confidence: 99%

“…Different solution techniques are used in the literature: analytical procedures (e.g., queueing theory), mathematical programming, dedicated branch-and-bound, scenario analysis (or sensitivity analysis), simulation and various heuristicsSimulationDiscrete-event[2,7,9,12,13,14,16,20,25,33,34,35, 40,41, 59, 67, 68, 69, 83, 87, 88, 94, 95, 96, 97, 107, 111, 116,123,130,131,136,140, 143, 144, 153, 156, 160, 162, 164, 165, 170, 171, 172, 174, 182, 185, 189, 190,202,204,205,211,213,223,226,227,228,238,243,246,249,254,272,278] …”

mentioning

confidence: 99%

Scheduling operating rooms: achievements, challenges and pitfalls

Samudra

Riet

Demeulemeester

et al. 2016

J Sched

166

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In hospitals, the operating room (OR) is a particularly expensive facility and thus efficient scheduling is imperative. This can be greatly supported by using advanced methods that are discussed in the academic literature. In order to help researchers and practitioners to select new relevant articles, we classify the recent OR planning and scheduling literature into tables using patient type, used performance measures, decisions made, OR supporting units, uncertainty, research methodology and testing phase. Additionally, we identify promising practices and trends and recognize common pitfalls when researching OR scheduling. Our findings indicate, among others, that it is often unclear whether an article mainly targets researchers and thus contributes advanced methods or targets practitioners and consequently provides managerial insights. Moreover, many performance measures (e.g., overtime) are not always used in the correct context. Furthermore, we see that important information that would allow readers to determine whether the reported research results are relevant to them is often missing. In order to avoid these pitfalls, we conclude that researchers need to state whether they target researchers or practitioners, motivate the choice of the used performance measures and mention both setting and method specific assumptions.

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“…They use stochastic patient demand and length of stay and compare a mixed integer program that employs an approximated objective function with a simulated annealing approach. Bowers () presents a simulation model to analyze the relationship between OR schedules and ICU capacity demand. He shows a negative correlation between OR and ICU utilization and derives managerial implications about matching OR and ICU resources.…”

Section: Related Literaturementioning

confidence: 99%

An Integrated Strategic and Tactical Master Surgery Scheduling Approach With Stochastic Resource Demand

Fügener

2015

J of Business Logistics

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T his study discusses a master surgery scheduling approach that maximizes hospital revenues while considering the impact on the downstream resources-intensive care units and general patient wards. Demand for these resources is modeled by a stochastic patient path model. I apply an integer programming model to provide the optimal allocation of how many (strategic planning problem) and what (tactical planning problem) operating room (OR) blocks to assign to each medical specialty. I demonstrate further applications of the model, such as the analysis of the value of flexible resources and the simulation of specific resource expansions. The approach is tested with real-life data from a German university hospital and achieves significant revenue increases.

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Balancing operating theatre and bed capacity in a cardiothoracic centre

Cited by 31 publications

References 25 publications

Integrated Scheduling and Capacity Planning with Considerations for Patients’ Length‐of‐Stays

Integrated Scheduling and Capacity Planning with Considerations for Patients’ Length‐of‐Stays

Scheduling operating rooms: achievements, challenges and pitfalls

An Integrated Strategic and Tactical Master Surgery Scheduling Approach With Stochastic Resource Demand

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