Scheduling doctors' appointments: optimal and empirically-based heuristic policies

Robinson, Lawrence W.; Chen, Rachel R.

doi:10.1080/07408170304367

Cited by 183 publications

(131 citation statements)

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“…Static systems can be complicated, with complexities that involve the number of providers, patient classification, patient no-show rates, service time, multi-steps, follow-up visits, etc. [23][24][25]. Most appointment systems in China are static in nature, wherein patients are given appointments ahead of time, but not for a specific time period.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

A simulation study of outpatient scheduling with multiple providers and a single device

Wu¹,

Khasawneh²,

Yue³

et al. 2014

IJCIS

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Effective outpatient appointment scheduling aims at reducing patient waiting time and operational costs, and improving resource utilization, especially given the stochastic nature of patient arrivals. Unlike many western developed countries, China faces challenges due to imperfect appointment systems and ineffective resource allocation. Those challenges lead to long patient waiting times and significant pressure to provide accurate and reliable medical diagnosis that can handle the increasing demand for patient care. In this paper, an outpatient appointment scheduling model for primary care settings was developed. The model was structured for a multiprovider clinic with a single medical device that allows for revisit (i.e., follow-up) procedures. A total of six scheduling rules were designed and tested. Then, sensitivity analysis was performed with respect to two parameters: the percentage of patients with fixed appointments and walk-ins, and the scheduled physician assessment times. The experimental results of the simulation model were used to determine the optimal values for the two parameters.Among the six scheduling rules tested, the one that involves patients with fixed appointments and walk-ins to be served alternatively every hour, led to the shortest waiting time and the highest equipment and staff utilization. In this scenario, patients with fixed appointments are served by Provider 1, while walk-ins are served by Providers 2 and 3. The sensitivity analysis under this rule revealed that when the percentage of patients with fixed appointments and walk-ins was 30-70%, and the scheduled service time was 20 minutes, the total patient waiting time and total resource idle time were minimized.

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Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

A simulation study of outpatient scheduling with multiple providers and a single device

Wu¹,

Khasawneh²,

Yue³

et al. 2014

IJCIS

View full text Add to dashboard Cite

show abstract

“…Denton and Gupta (2003) presented that the ASP can be expressed as a two-stage stochastic linear program and a standard L-shaped algorithm is employed to obtain optimal solutions. Robinson and Chen (2003) used the structure of the optimal solution as the basis for a simple closedform heuristic for setting appointment times and the heuristic is shown to perform on average within 2% (and generally within 0.5%) of the optimal policy. Bendavid and Golany (2011) employed the CrossEntropy method to solve the problem, which aimed to set gates so to minimize the sum of the expected holding and shortage costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal appointment scheduling with a stochastic server: Simulation based K-steps look-ahead selection method

Liu¹,

Xiang²

2018

10.5267/j.ijiec

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This paper studies the problem of scheduling a finite set of customers with stochastic service times for a single-server system. The objective is to minimize the waiting time of customers, the idle time of the server, and the lateness of the schedule. Because of the NP-hardness of the problem, the optimal schedule is notoriously hard to derive with reasonable computation times. Therefore, we develop a simulation based K-steps look-ahead selection method which can result in nearly optimal schedules within reasonable computation times. Furthermore, we study the different distributed service times, e.g., Exponential, Weibull and lognormal distribution and the results show that the proposed algorithm can obtain better results than the lag order approximation method proposed by Vink et al.

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“…The purpose is also to reduce waiting time of patients and increase utilization of physicians. Robinson and Chen (2003) propose an appointment system to balance the patients' waiting times and the doctors' idle time. Another main objective of appointment system is to maximize profit/revenue.…”

Section: Literature Reviewmentioning

confidence: 99%

An Integer Programming Formulation for Outpatient Scheduling with Patient Preference

Wang¹,

Fung²

2014

Industrial Engineering and Management Systems

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Patients' satisfaction while receiving medical service is affected by whether or not their preferences can be met, including time and physician preference. Due to scarcity of medical resource in China, efficient use of available resources is urgently required. To guarantee the utilization ratio, the scheduling decisions are made after all booking information is received. Two integer models with different objectives are formulated separately, maximizing the degree of satisfaction and revenue. The optimal value of the two models can be considered as the bound of corresponding objectives. However, it is improper to implement any of the extreme policies. Because revenue is a key element to keep the hospital running and satisfaction degree is related to the hospital's reputation, neither the revenue nor the satisfaction can be missed. Therefore, hospitals should make a balance. An integrated model is developed to find out the tradeoff between the two objectives. The whole degree of mismatching that is related to patient satisfaction and other separate mismatching degree are considered. Through a computational study, it is concluded that based on the proposed model hospitals can make their decisions according to service requirement.

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Scheduling doctors' appointments: optimal and empirically-based heuristic policies

Cited by 183 publications

References 1 publication

A simulation study of outpatient scheduling with multiple providers and a single device

A simulation study of outpatient scheduling with multiple providers and a single device

Optimal appointment scheduling with a stochastic server: Simulation based K-steps look-ahead selection method

An Integer Programming Formulation for Outpatient Scheduling with Patient Preference

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