Assessing the impact of stochasticity for operating theater sizing

Tancrez, Jean‐Sébastien; Roland, B.; Cordier, Jean-Philippe; Riane, Fouad

doi:10.1016/j.dss.2012.10.021

Cited by 22 publications

(26 citation statements)

References 19 publications

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“…The second most studied department is the hospital ward (n = 52). Various types of wards are evaluated, like critical care units [45,50,87], Medical Assessment Units (MAU) [51,150,193,194], and Post Anesthetic Care Units (PACU) where patients recover after surgery [226]. The third most studied department is the OT (n = 36).…”

Section: Departmental Focusmentioning

confidence: 99%

“…Table 2.4 lists the publications per department and planning level. [68,199,225,226,253,263] tactical [2, 8, 17, 18, 68, 93, 125, 147, 171, 195, 225-228, 249, 262, 263] operational [2, 18, 32, 61, 63, 64, 77, 93, 106, 113, 126, 153-156, 184, 192, 201, 207, 247, 249 [33,35,73,79,90,120,123,141,143,174,204,215,260] operational [16,73,84,112,143,172,202,229,234,240] N/A 2.3. Results…”

Section: Hierarchical Planning Levelmentioning

confidence: 99%

“…When wards are included, the recovery of patients after surgery Ambulatory [29,33,90,157,174] [ 33,90,157,260] is modeled alongside the OR, and patients must be scheduled such that not only an OR is available, but also a post surgery bed. For example, [226] evaluates the use of emergency and dedicated ORs together with PACU beds available. Several elective, emergency and PACU bed configurations are considered and optimized with regards to occupancy and overtime.…”

Section: Departmentmentioning

confidence: 99%

“…Trade-offs are sought between the utilization of wards and beds, and the blocking probability of a ward (i.e., the probability a patient can not be admitted). Both more specialized wards such as PACUs [18,61,226] and medium and intensive care units (ICU) [2,17,18,125,169] are considered, as well as regular wards [18,147]. When wards are included, the recovery of patients after surgery Ambulatory [29,33,90,157,174] [ 33,90,157,260] is modeled alongside the OR, and patients must be scheduled such that not only an OR is available, but also a post surgery bed.…”

Section: Departmentmentioning

confidence: 99%

“…At the strategic planning level, studies investigate the size (e.g., how many beds and/or rooms to allocate) of EDs [49,107], wards [213,226] or both [90,157,159,213,237] in order to find the number beds that satisfies urgent care demand. At the strategic level, ambulatory services are taken into account in studies that model entire patient pathways [33,90,157], and resource capacity is dimensioned across services and wards.…”

Section: Strategic Planningmentioning

confidence: 99%

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Section: Departmental Focusmentioning

confidence: 99%

Section: Hierarchical Planning Levelmentioning

confidence: 99%

Section: Departmentmentioning

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Section: Departmentmentioning

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Part 2: Knowledge-Based ServicesInternational audienceThe operating theatre (OT) represents a significant component of the technical means centre. This facility is the largest cost and revenue centre. To be efficient, it needs an optimal operational pro- gramme, which takes into account maintenance activi- ties and not only surgical operations. To build such a programme, various methods have been used: mixed integer programming (MIP), three classic heuristics for Bin-Packing and a coupling of the first alterna- tive with a stochastic descent (SD). Then we compare the obtained results from generated data

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Stochastic radiotherapy appointment scheduling

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When scheduling the starting times for treatment appointments of patients in hospitals or outpatient clinics such as radiotherapy centers, minimizing patient waiting time and simultaneously maximizing resource usage is crucial. Significant uncertainty in the treatment durations makes scheduling those activities particularly challenging. In addition to the treatments themselves, also preparation times and exiting times have to be considered, which are uncertain as well. To address and analyze this type of problems, the current study develops a model for planning appointment times under uncertain activity durations for a medical unit with a single “core resource” (in our application case a radiotherapy beam device), several treatment rooms, and required preparation and exiting phases for each patient. We employ a novel buffer concept based on quantiles of duration distributions and introduce a reactive procedure that adapts a pre-determined baseline schedule to the actual patient flow. For heuristically solving the resulting stochastic optimization model, a combination of a Genetic Algorithm and Monte Carlo simulation is proposed. A case study uses real-world data on activity durations gathered from an ion beam therapy facility in Austria. Experimental results comparing different variants of the method are carried out. In particular, comparisons of the stochastic optimization approach to a simpler deterministic approach are given.

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Assessing the impact of stochasticity for operating theater sizing

Cited by 22 publications

References 19 publications

Managing urgent care in hospitals

Managing urgent care in hospitals

Programming Integrated Surgical Operations and Preventive Maintenance Activities

Stochastic radiotherapy appointment scheduling

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