Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations

Liu, Nan; Ziya, Serhan; Kulkarni, Vidyadhar G.

doi:10.1287/msom.1090.0272

Cited by 285 publications

(194 citation statements)

References 23 publications

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“…The result of the proposed method can be used to develop more effective appointment scheduling (8)(9)(10)(11)(12). It can also be used for developing effective strategies, such as selective overbooking for reducing the negative effects of disturbances and filling appointment slots while maintaining short waiting times (13)(14)(15).…”

Section: Discussionmentioning

confidence: 99%

Predicting appointment misses in hospitals using data analytics

Devasahay¹,

Karpagam²,

Laik³

2017

mHealth

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Process followed for booking appointmentsAn appointment with the doctor is typically made when either the clinic or the patient calls the hospital phone line. Original ArticlePredicting appointment misses in hospitals using data analytics Background: There is growing attention over the last few years about non-attendance in hospitals and its clinical and economic consequences. There have been several studies documenting the various aspects of non-attendance in hospitals. Project Predicting Appoint Misses (PAM) was started with the intention of being able to predict the type of patients that would not come for appointments after making bookings.Methods: Historic hospital appointment data merged with "distance from hospital" variable was used to run Logistic Regression, Support Vector Machine and Recursive Partitioning to decide the contributing variables to missed appointments.Results: Variables that are "class", "time", "demographics" related have an effect on the target variable, however, prediction models may not perform effectively due to very subtle influence on the target variable.Previously assumed major contributors like "age", "distance" did not have a major effect on the target variable.Conclusions: With the given data it will be very difficult to make any moderate/strong prediction of the Appointment misses. That being said with the help of the cut off we are able to capture all of the "appointment misses" in addition to also capturing the actualized appointments.

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Section: Discussionmentioning

confidence: 99%

Predicting appointment misses in hospitals using data analytics

Devasahay¹,

Karpagam²,

Laik³

2017

mHealth

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“…In sequential scheduling on the other hand appointment requests arrive gradually over time and the scheduler has to fit each patient to one of the available slots. See, for example, Gerchak et al (1996), Patrick et al (2008), Liu et al (2010), andFeldman et al (2014). Our investigation here is at a more strategic level and can be considered as a prerequisite to advance and online scheduling: we find the capacity needed for pre-booked non-urgent patients so that a reasonably quick access can be guaranteed.…”

Section: Literature Reviewmentioning

confidence: 95%

“…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%

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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“…This includes an analytical comparison of traditional and advanced access appointment systems [23]; the impact of no-shows [5,11,12,17]; the importance of considering patient preferences [10,26]; and capacity allocation methods that allow practices to offer a blend of prescheduled (non-urgent) and same-day (urgent) appointments [2,20].…”

Section: Literature Reviewmentioning

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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Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations

Cited by 285 publications

References 23 publications

Predicting appointment misses in hospitals using data analytics

Predicting appointment misses in hospitals using data analytics

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

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