Improvement of chemotherapy patient flow and scheduling in an outpatient oncology clinic

Liang, Bohui; Türkcan, Ayten; Ceyhan, Mehmet; Stuart, Keith

doi:10.1080/00207543.2014.988891

Cited by 78 publications

(89 citation statements)

References 13 publications

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“…Multiple authors consider the planning of flow-shop type multi-disciplinary systems, for example in oncology [23,40] and primary care practices [30]. Liang et al [23] analyze the impact of scheduling methods on the oncology clinic performance, where patients visit an oncologist and a nurse for chemotherapy treatment.…”

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

“…Liang et al [23] analyze the impact of scheduling methods on the oncology clinic performance, where patients visit an oncologist and a nurse for chemotherapy treatment. Although various patient routings are considered, they consider this as given in their model.…”

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

“…Simulation is used to analyze the performance of multiple clinics under a variety of scenarios, including various appointment rules and appointment schedules [9,18,23,25,31,40,43,44], which show significant improvements compared to the current practice in partnering health care centers.…”

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

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Stochastic integer programming for multi-disciplinary outpatient clinic planning

Leeftink

Vliegen

Hans

2017

Health Care Manag Sci

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Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multidisciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital's problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

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Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

Section: Multi-disciplinary Schedulingmentioning

confidence: 99%

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Stochastic integer programming for multi-disciplinary outpatient clinic planning

Leeftink

Vliegen

Hans

2017

Health Care Manag Sci

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“…In the blueprint, time slots are assigned to patient types [34,88,178], or to process stages [175]. Furthermore, the blueprint may prescribe when doctors can best have consultation hours [34,178]. A blueprint is usually designed based on expected arrival patterns and expected availability of capacity.…”

Section: Capacity Planningmentioning

confidence: 99%

“…Designing a blueprint schedule as a guideline for appointment planning is done with objectives to combine consultations on one day [88], to minimize waiting time on a day [178], or to minimize access time or throughput time [34,175]. In the blueprint, time slots are assigned to patient types [34,88,178], or to process stages [175].…”

Section: Capacity Planningmentioning

confidence: 99%

Organizing integrated processes in cancer care

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A stochastic programming approach for chemotherapy appointment scheduling

Demir

Gül

Çelik

2020

Naval Research Logistics

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Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in premedication and infusion durations. In this paper, we formulate a two‐stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real‐life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty.

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Improvement of chemotherapy patient flow and scheduling in an outpatient oncology clinic

Cited by 78 publications

References 13 publications

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Organizing integrated processes in cancer care

A stochastic programming approach for chemotherapy appointment scheduling

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