A stochastic overbooking model for outpatient clinical scheduling with no-shows

Muthuraman, Kumar; Lawley, Mark

doi:10.1080/07408170802165823

Cited by 239 publications

(165 citation statements)

References 29 publications

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“…An evaluation of the different appointment rules under various scenarios has been conducted in [3,12,16,18,25,28,29]. Furthermore, the development of an appointment scheduling policy to minimize some objective function was examined in [4,6,10,11,20,22,26,27,31].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mathematical programming methods are used to develop an appointment scheduling policy in [6,10,22,26,27,31]. Fries and Marathe [10], Vanden Bosch and Dietz [26,27] schedule a given number of patients into equal slot intervals, with objectives to minimize patients' waiting time, physicians' idle time, and/or physicians' overtime.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Muthuraman and Lawley [22], Chakraborty et al [6], and Zeng et al [31] determine the maximum number of patients that can be scheduled into a given set of slots. These papers propose heuristic algorithms with stopping criteria to schedule patients' appointments into the provided slots.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Physicians' idle time and overtime, and patients' waiting time are the performance measurements. Muthuraman and Lawley [22] and Chakraborty et al [6] consider a patient scheduling problem with a unimodal objective function where the expected profit for a schedule is non-decreasing with the addition of some patient and then monotone decreasing. The former study assumed an exponential service time, while the latter study assumed a general distributed service time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…All of the above studies account for no-show rates but exclude the lateness of patients in their models. Fixed and equal slot intervals are assumed in [6,22], while fixed but different slot lengths are explored in [31].…”

Section: Literature Reviewmentioning

confidence: 99%

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Scheduling rules to achieve lead-time targets in outpatient appointment systems

Nguyen

Sivakumar

Graves

2016

Health Care Manag Sci

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This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment leadtime targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the request is not rejected, the rules prescribe how to assign the patient to an available slot. The intent of the scheduling rules is to maximize the utilization of the planned resource (i.e., the physician staff), or equivalently to maximize the number of patients that are admitted, while maintaining the service targets on the median, the 95th percentile, and the maximum appointment lead-times. We test the proposed scheduling rules with numerical experiments using real data from the chosen clinic of Tan Tock Seng hospital in Singapore. The results show the efficiency and the efficacy of the scheduling rules, in terms of the service-target satisfaction and the resource utilization. From the sensitivity analysis, we find that the performance of the proposed scheduling rules is fairly robust to the specification of the established lead-time targets.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Scheduling rules to achieve lead-time targets in outpatient appointment systems

Nguyen

Sivakumar

Graves

2016

Health Care Manag Sci

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Capacity Planning in Health Care

Patrick

Noghani

2011

Wiley Encyclopedia of Operations Research and Management Science

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Capacity planning in health care is greatly complicated by the nature of the service. The individuality of each patient leads to multiplicity of potential resource requirements often consumed in sequence and with varying degrees of urgency. Patients are also able to refuse treatment, lobby for a different type of treatment, or simply fail to show up for treatment. Finally, the dire consequences sometimes resulting from untimely access lead to much lower tolerances with regards to meeting performance targets. All these complexities make for significant challenges when seeking to plan capacity in the health care setting.

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Ambulatory Care

Liu

2018

Handbook of Healthcare Analytics

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A stochastic overbooking model for outpatient clinical scheduling with no-shows

Cited by 239 publications

References 29 publications

Scheduling rules to achieve lead-time targets in outpatient appointment systems

Scheduling rules to achieve lead-time targets in outpatient appointment systems

Capacity Planning in Health Care

Ambulatory Care

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