Minimizing expected waiting in a medical appointment system

Bosch, Peter M. Vanden; Dietz, David

doi:10.1080/07408170008967443

Cited by 79 publications

(68 citation statements)

References 9 publications

(6 reference statements)

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“…Vanden Dietz (2000 and2001) are one of the few exceptions to study an AS spanning over multiple service sessions. Each service session i of length T s i is divided in a number of blocks B; t s and t e indicating the start of the first and the end of the last block respectively.…”

Section: Appointment System Literature Reviewmentioning

confidence: 99%

Queueing models for appointment-driven systems

Creemers

Lambrecht

2009

Ann Oper Res

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Many service systems are appointment-driven. In such systems, customers make an appointment and join an external queue (also referred to as the "waiting list"). At the appointed date, the customer arrives at the service facility, joins an internal queue and receives service during a service session. After service, the customer leaves the system. Important measures of interest include the size of the waiting list, the waiting time at the service facility and server overtime. These performance measures may support strategic decision making concerning server capacity (e.g. how often, when and for how long should a server be online). We develop a new model to assess these performance measures. The model is a combination of a vacation queueing system and an appointment system.

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Section: Appointment System Literature Reviewmentioning

confidence: 99%

Queueing models for appointment-driven systems

Creemers

Lambrecht

2009

Ann Oper Res

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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%

“…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. Fries and Marathe [10] derived a dynamic programming for the case with exponential service time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fries and Marathe [10] derived a dynamic programming for the case with exponential service time. Vanden Bosch and Dietz [26,27] develop two heuristic algorithms for general service times, with an assumption of different no-show rates amongst types of patients.…”

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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Surgery Planning and Scheduling

Erdogan

Denton

Fitts³

2011

Wiley Encyclopedia of Operations Research and Management Science

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In 2007, total health-care spending in the United States reached US $2.3 trillion, and continues to rise at the fastest rate in US history. The design and implementation of better planning and scheduling systems is an important area to study in order to reduce high costs and improve access to services in the health-care system. Surgery scheduling, in particular, is an area with significant potential for realizing greater efficiencies. Poor scheduling prevents health-care providers from matching patient demand with available capacity, causing inefficient use of resources, decreased return on investment, and long waiting lists for patients. It has been estimated that surgery accounts for more than 40% of a hospital's total revenues and expenses. Recent studies indicate that resource utilization, overtime, and on-time start performance within surgical suites could be improved at most hospitals. These important performance measures are influenced in part by the surgery scheduling systems and policies that are used in practice.Surgery scheduling systems impact a variety of expensive resources including operating rooms (ORs), the postanesthesia care unit (PACU), intensive care unit (ICU), hospital beds, equipment resources such as mobile diagnostic imaging devices, and human resources including surgeons, nurses, anesthesiologists, and other staff. The unpredictable nature of surgery results in uncertainty in the duration of surgery and patient recovery. This can be caused by many factors including the varying experience of surgeons and OR teams, the presence of residents or surgical fellows in

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Minimizing expected waiting in a medical appointment system

Cited by 79 publications

References 9 publications

Queueing models for appointment-driven systems

Queueing models for appointment-driven systems

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

Surgery Planning and Scheduling

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