Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation

Swisher, James R.; Jacobson, Sheldon H.; Jun, Jongkun; Balcı, Osman

doi:10.1016/s0305-0548(99)00093-3

Cited by 151 publications

(81 citation statements)

References 7 publications

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“…Genericity in outpatient simulations is approached from a slightly different angle by Swisher et al (2001), which reports how a single clinic model was first built as a template and used, suitably parameterized, for other clinics in a network of family clinics in the US. This work is one of the examples of modelling of independent health-care facilities relying on a common scheduling and information centre.…”

Section: Outpatient Clinicsmentioning

confidence: 99%

Discrete event simulation for performance modelling in health care: a review of the literature

Günal

Pidd

2010

Journal of Simulation

446

286

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Discrete Event Simulation (DES) has been widely used in modelling health-care systems for many years and a simple citation analysis shows that the number of papers published has increased markedly since 2004. Over the last 30 years several significant reviews of DES papers have been published and we build on these to focus on the most recent era, with an interest in performance modelling within hospitals. As there are few papers that propose or illustrate general approaches, we classify papers according to the areas of application evident in the literature, discussing the apparent lack of genericity. There is considerable diversity in the objectives of reported studies and in the consequent level of detail: We discuss why specificity dominates and why more generic approaches are rare.

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Section: Outpatient Clinicsmentioning

confidence: 99%

Discrete event simulation for performance modelling in health care: a review of the literature

Günal

Pidd

2010

Journal of Simulation

446

286

View full text Add to dashboard Cite

show abstract

“…Ambulatory care facilities dimension their resources, such as staff, equipment and space, with the objective to (simultaneously) maximize clinic profit, patient satisfaction, and staff satisfaction [458]. To this end, provider capacity must be matched with patient demand, such that performance measures such as costs, access time and waiting time are controlled.…”

Section: Ambulatory Care Servicesmentioning

confidence: 99%

“…The number of consultation rooms that balances patient waiting times and doctor idle time with costs for consultation rooms [264,451,457,458].…”

Section: Ambulatory Care Servicesmentioning

confidence: 99%

“…Staff in the ambulatory care services concern for example doctors, nurses and assistants [35,282,348,421,450,451,454,457,458,508,515].…”

Section: Ambulatory Care Servicesmentioning

confidence: 99%

“…The waiting room is dimensioned such that patients and their companions waiting for consultation can be accommodated [458]. When capacity is dimensioned to cover average demand, variations in demand may cause long access and waiting times [471].…”

Section: Ambulatory Care Servicesmentioning

confidence: 99%

See 2 more Smart Citations

Integrated decision making in healthcare : an operations research and management science perspective

Hulshof¹

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Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

Hur

Bard

Morrice

2020

Naval Research Logistics

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The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.

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Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation

Cited by 151 publications

References 7 publications

Discrete event simulation for performance modelling in health care: a review of the literature

Discrete event simulation for performance modelling in health care: a review of the literature

Integrated decision making in healthcare : an operations research and management science perspective

Appointment scheduling at a multidisciplinary outpatient clinic using stochastic programming

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