Coordinated Patient Appointment Scheduling for a Multistation Healthcare Network

Wang, Dongyang Ester; Muthuraman, Kumar; Morrice, Douglas J.

doi:10.1287/opre.2018.1816

Cited by 31 publications

(20 citation statements)

References 42 publications

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“…The most significant difference between the Markov chain models and other approaches is patients' status during a specific period of time [ 60 , 61 ], called different wait time penalties. A key factor is the order of patient treatment, i.e., first-come-first-serve (FCFS).…”

Section: Results and Analysismentioning

confidence: 99%

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Ala

Chen

2022

Journal of Healthcare Engineering

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This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.

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Section: Results and Analysismentioning

confidence: 99%

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Ala

Chen

2022

Journal of Healthcare Engineering

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“…For instance, in some settings patients may be required to visit the lab for some tests before seeing a physician or to visit providers in different physical locations. Wang et al (2019) refer to this kind of system using the term "multistation network" and develop and analyze an appointment scheduling model for coordinating patient care in this environment. Given the complexity of the system's structure, the paper focuses on a myopic scheduling approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Patient‐controlled use of nonphysician providers: Appointment scheduling in mixed‐provider settings

Taiwo

Savin

Chen

et al. 2023

Production and Operations Management

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The aging population and increasing chronic disease load are rapidly changing the face of primary care delivery, with mid‐level (e.g., nurse) practitioners providing growing proportion of patient care. Potential differences in the quality of care offered by physicians and nurse practitioners may affect patient preferences, thus leading to patient choice behavior. This paper focuses on the problem of appointment scheduling for physician–nurse teams in the presence of patient choice and no‐shows. We propose a novel model that accounts for patient choices in a system with two provider types. Despite the increased structural complexity of the model, we derive sufficient conditions under which the problem is efficiently solvable. To counter the computational challenges arising in the general setting, we propose an easy‐to‐implement heuristic, which is proven to be optimal in the absence of patient no‐shows. Our numerical study shows how the ratio of qualities of care delivered by nurses and physicians affect the profitability of the medical practice, enabling the analysis of the trade‐offs involved in hiring a nurse practitioner. This paper introduces a patient‐controlled approach to incorporating nonphysician providers into physician‐led outpatient care delivery systems and compares it to widely used “ice breaker” and “standalone” modes of using nonphysician providers. Our findings reveal that clinical practices that employ mixed (physicians and nonphysicians) provider pools can significantly improve their financial and operational performance by moving away from the “ice breaker” and “standalone” use of nonphysician providers by delaying the selection of an appropriate care provider till the actual day of care delivery.

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“…Wang et al. (2019) study patient appointment scheduling in a multistage healthcare network, and they propose a practical myopic scheduling policy. Liu et al.…”

Section: Introductionmentioning

confidence: 99%

Advance admission scheduling via resource satisficing

Zhou

Lam

2022

Production and Operations Management

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We study the problem of advance scheduling of ward admission requests in a public hospital, which affects the usage of critical resources such as operating theaters and hospital beds. Given the stochastic arrivals of patients and uncertain usage of resources, it is often infeasible for the planner to devise a risk‐free schedule to meet these requests without violating resource capacity constraints and creating adverse effects that include healthcare overtime, long patient waiting times, and bed shortages. The difficulty of quantifying these costs and the need to safeguard against resource overutilization lead us to propose a resource satisficing framework that renders the violation of resource constraints less likely and also diminishes its impact whenever it occurs. The risk of resource overutilization is captured by our resource satisficing index (RSI), which is calibrated to reflect a risk‐adjusted utilization rate for a better interpretation to the healthcare planner. Unlike the expected utilization rate, RSI is risk‐sensitive and serves to mitigate the risks of overutilization better whenever overutilization can be avoided in expectation. Our satisficing approach aims to balance out the overutilization risks by minimizing the maximal RSI among all resources and periods. Under our proposed partial adaptive scheduling policy, the resource satisficing model can be formulated and solved via a converging sequence of mixed‐integer linear optimization problems. A computational study establishes that our approach reduces resource overutilization risks to a greater extent than the benchmark methods.

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Coordinated Patient Appointment Scheduling for a Multistation Healthcare Network

Cited by 31 publications

References 42 publications

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Patient‐controlled use of nonphysician providers: Appointment scheduling in mixed‐provider settings

Advance admission scheduling via resource satisficing

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