Reducing Delays for Medical Appointments: A Queueing Approach

Green, Linda V.; Savin, Sergei

doi:10.1287/opre.1080.0575

Cited by 240 publications

(234 citation statements)

References 18 publications

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“…More similar to our research are the studies by Jiang et al (2012), Creemers and Lambrecht (2010), Kortbeek et al (2014), Green and Savin (2008), and Liu and Ziya (2014). Jiang et al (2012) propose an M/D/1 queue -with Poisson (M ) arrivals and deterministic (D) service times -with state-dependent balking as a suitable model for specialty clinics.…”

Section: Literature Reviewsupporting

confidence: 70%

“…Since in our clinic data, as illustrated in Figure 1, the no-show probability does not show a strong increasing trend with respect to waiting time, we assume a fixed no-show probability in the first two models. However, to extend the applicability of our models to situations where such trend does exist, as in the clinics studied by Green and Savin (2008) and Liu et al (2010), we develop a third model where no-show probability is an increasing function of the size of appointment backlog. …”

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confidence: 99%

“…We also demonstrate how this model can be used in practice for finding the appointment capacity needed for achieving a given waiting time target. To illustrate the application of our third model, we use the data provided for an MRI clinic in the US in the study of Green and Savin (2008) where the decision variable is considered to be the patient panel size rather than appointment capacity. Our experiments show that as the variability in the daily number of appointment requests decreases, the transition to unmanageable backlogs occurs at larger values of panel sizes but at a faster pace, i.e.…”

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confidence: 99%

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Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

Izady

2015

Operations Research

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Specialty clinics provide specialized care for patients referred by primary care physicians, emergency departments, or other specialists. Urgent patients must often be seen on the referral day, while non-urgent referrals are typically booked an appointment for the future. To deliver a balanced performance, the clinics must know how much 'appointment capacity' is needed for achieving a reasonably quick access for non-urgent patients. To help identify the capacity that leads to the desired performance, we model the dynamics of appointment backlog as novel discrete-time bulk service queues, and develop numerical methods for efficient computation of corresponding performance metrics. Realistic features such as arbitrary referral and clinic appointment cancellation distributions, delay-dependent no-show behaviour and rescheduling of no-shows are explicitly captured in our models. The accuracy of the models in predicting performance as well as their usefulness in appointment capacity planning is demonstrated using real data. We also show the application of our models in capacity planning in clinics where patient panel size, rather than appointment capacity, is the major decision variable. Permanent repository link:

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Section: Literature Reviewsupporting

confidence: 70%

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confidence: 99%

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confidence: 99%

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Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

Izady

2015

Operations Research

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“…Green and Savin [8] use queuing models and simulation to demonstrate the impact of panel size on the no-show rate, physician utilization, and the probability of getting a same-day appointment. They find that the backlog of appointments grows with panel size and as a result the no-show rate does as well, since patients booked well into the future will have a greater probability of no-show.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the same way, although no-shows are not a part of our model, well designed panels can only reduce the impact of no-shows, by improving time to earliest available appointments. See [8] for a discussion. Finally, patients with more comorbidities are more likely to have longer appointments than healthy patients.…”

Section: Conclusion and Implications For Practicementioning

confidence: 99%

The impact of case mix on timely access to appointments in a primary care group practice

Ozen

Balasubramanian

2012

Health Care Manag Sci

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At the heart of the practice of primary care is the concept of a physician panel. A panel refers to the set of patients for whose long term, holistic care the physician is responsible. A physician's appointment burden is determined by the size and composition of the panel. Size refers to the number of patients in the panel while composition refers to the case-mix, or the type of patients (older versus younger, healthy versus chronic patients), in the panel. In this paper, we quantify the impact of the size and case-mix on the ability of a multi-provider practice to provide adequate access to its empanelled patients. We use overflow frequency, or the probability that the demand exceeds the capacity, as a measure of access. We formulate problem of minimizing the maximum overflow for a multi-physician practice as a non-linear integer programming problem and establish structural insights that enable us to create simple yet near optimal heuristic strategies to change panels. This optimization framework helps a practice: (1) quantify the imbalances across physicians due to the variation in case mix and panel size, and the resulting effect on access; and (2) determine how panels can be altered in the least disruptive way to improve access. We illustrate our methodology using four test prac-A. Ozen

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Analysis of Variation in Organizational Definitions of Primary Care Panels

Mayo‐Smith

Robbins

Murray³

et al. 2022

JAMA Netw Open

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Key Points Question Is there variation in how organizations define primary care panels, and if so, how much does this contribute to variation in reported panel size? Findings This systematic review of 74 articles reporting on 29 health care systems and 5 empanelment implementation guides found wide variation in the rules used to add and remove patients from primary care panels and to count primary care resources. Different rules were associated with large differences in reported panel size, independent of the actual clinician workload. Meaning These findings suggest that caution is needed comparing reported panel sizes, and research is needed on the benefits of different approaches.

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Reducing Delays for Medical Appointments: A Queueing Approach

Cited by 240 publications

References 18 publications

Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

Appointment Capacity Planning in Specialty Clinics: A Queueing Approach

The impact of case mix on timely access to appointments in a primary care group practice

Analysis of Variation in Organizational Definitions of Primary Care Panels

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