Patient Appointments in Ambulatory Care

Gupta, Diwakar; Wang, Wen Ya

doi:10.1007/978-1-4614-1734-7_4

Cited by 32 publications

(22 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That is, if a patient arrives early (i.e., within interval E), his/her waiting time is measured from his/her scheduled time. This is consistent with the prior literature (see, eg, Gupta & Wang, 2012; Cayirli & Veral, 2003). Although declining a very late patient may be mathematically optimal in terms of collectively minimizing metrics (i)‐(iii), it may impact patients' health and lead to patient dissatisfaction.…”

Section: Formulations Of Soaspsupporting

confidence: 93%

“…It follows that constraint (2l) is relaxed and w j = max{ s j − t i , 0} by (2m) and (2r), that is, the waiting time of this patient is the difference between his/her actual start time and the scheduled arrival time. Recall from Section 3.1 that, consistent with the prior literature, we do not incorporate the part of the waiting time due to the early arrival of a patient (see, eg, Gupta & Wang, 2012; Cayirli & Veral, 2003).…”

Section: Formulations Of Soaspmentioning

confidence: 99%

See 1 more Smart Citation

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Shehadeh

Cohn

Jiang

2020

Naval Research Logistics

View full text Add to dashboard Cite

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.

show abstract

Section: Formulations Of Soaspsupporting

confidence: 93%

Section: Formulations Of Soaspmentioning

confidence: 99%

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Shehadeh

Cohn

Jiang

2020

Naval Research Logistics

View full text Add to dashboard Cite

show abstract

“…In Phase II it is assumed that whenever any given customer calls for an appointment, the scheduler knows from the database the particular parameter values of the customer calling, that is, her

w_{j}

q_{j}

, and

z_{j}

, and where these values fit within the distribution of the entire client pool. Gupta and Wang () refer to Phase I as the clinic profile setup problem and to Phase II as the appointment booking problem .…”

Section: Sequential Schedulingmentioning

confidence: 99%

Appointment Scheduling with No‐Shows and Overbooking

Zacharias

Pinedo

2014

Production and Operations Management

171

View full text Add to dashboard Cite

W e study an overbooking model for scheduling arrivals at a medical facility under no-show behavior, with patients having different no-show probabilities and different weights. The scheduler has to assign the patients to time slots in such a way that she minimizes the expected weighted sum of the patients' waiting times and the doctor's idle time and overtime. We first consider the static problem, where the set of patients to be scheduled and their characteristics are known in advance. We partially characterize the optimal schedule and introduce a new sequencing rule that schedules patients according to a single index that is a function of their characteristics. Then we apply our theoretical results and conclusions from numerical experiments to sequential scheduling procedures. We propose a heuristic solution to the sequential scheduling problem, where requests for appointments come in gradually over time and the scheduler has to assign each patient to one of the remaining slots that are available in the schedule for a given day. We find that the no-show rate and patients' heterogeneity have a significant impact on the optimal schedule and should be taken under consideration.

show abstract

“…Healthcare organizations the world over experience frustration from failed appointments and last minute cancellations 1–3 . Certainly, some circumstances dictate that a last minute change of plans for a patient may result in a wasted appointment for a healthcare provider.…”

Section: Introductionmentioning

confidence: 99%

Using business intelligence and data visualization to understand the characteristics of failed appointments in dental school clinics

Weber

DaSilva

Dault

et al. 2021

Journal of Dental Education

View full text Add to dashboard Cite

Purpose/Objectives Broken appointments are an important cause of waste in health care. Patients who fail to attend incur costs to providers, deny trainees learning opportunities, and impact their own health as well as that of other patients who are waiting for care. Methods A total of 410,000 appointment records over 3 years were extracted from our electronic health record. We conducted exploratory data analysis and assessed correlations between appointment no‐shows and other attributes of the appointment and the patient. The University of Michigan Medical School's Committee on Human Research reviewed the study and deemed that no Institutional Review Board oversight was necessary for this quality improvement project that was, retrospectively, turned into a study with previously de‐identified data. Results The patient's previous attendance record is the single most significant correlation with attendance. We found that patients who said they are “scared” of dental visits were 62% as likely to attend as someone reporting “no problem.” Patients over 65 years of age have better attendance rates. There was a positive association between receiving email/text confirmation and attendance. A total of 94.9% of those emailed a reminder and 92.2% of those who were texted attended their appointment. Conclusion(s) We were able to identify relationships of several variables to failed and attended appointments that we were previously unknown to us. This knowledge enabled us to implement interventions to support better attendance at Dental Clinics at the University of Michigan, improving patient health, student training, and efficient use of resources.

show abstract

Patient Appointments in Ambulatory Care

Cited by 32 publications

References 59 publications

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

Appointment Scheduling with No‐Shows and Overbooking

Using business intelligence and data visualization to understand the characteristics of failed appointments in dental school clinics

Contact Info

Product

Resources

About