Appointment Scheduling Under Time-Dependent Patient No-Show Behavior

Kong, Qingxia; Li, Shan; Liu, Nan; Teo, Chung‐Piaw; Yan, Zhenzhen

doi:10.1287/mnsc.2019.3366

Cited by 78 publications

(39 citation statements)

References 25 publications

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“…(2012), Zacharias and Pinedo (2014, 2017) and Kong et al. (2020). A well‐known strategy to offset the impact of no‐shows is overbooking, that is, booking more customers in a certain time slot than the service capacity.…”

Section: Literature Reviewmentioning

confidence: 98%

Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis

Zhou

Ding

Huh

et al. 2021

Production and Operations Management

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We consider the classical appointment scheduling problem that does not have a session length constraint. The decision is to determine job allowances to minimize total expected weighted costs of waiting times of jobs and idle times of the service provider. In particular, we study a simple yet effective scheduling policy—the so‐called “constant" policy, which allocates a constant job allowance for each appointment. Prior studies on appointment scheduling suggest a “dome”‐shaped structure for the optimal job allowance over the planning horizon. This implies that job allowance does not vary significantly in the middle of the schedule sequence, but varies at the beginning as well as the end of the optimal schedule. We show that an even simple scheduling policy—the constant policy, is asymptotically optimal thus provide theoretical justification for such a widely used policy. Using a dynamic programming formulation, we express each job's waiting time as the maximum of a random walk, which allows us to bound the performance gap between the constant policy and the optimal schedule based on classical results on D/G/1 queues. We derive an explicit upper bound for the performance gap when either of the following conditions holds: (i) the server idling cost is relatively small compared to the job waiting cost, and (ii) the number of appointments is sufficiently large. We also extend this result to a more general setting with multiple service types. Numerical experiments show that the constant policy is near optimal even when the number of appointments is small or when the server idling cost is moderately large, which complements our theoretical results. Our result provides a justification and strong support for the constant policy under certain mild conditions. Moreover, with minor modifications, the constant policy can be adapted to more general scenarios with patient no‐shows, or with heterogeneous appointment types.

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Section: Literature Reviewmentioning

confidence: 98%

Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis

Zhou

Ding

Huh

et al. 2021

Production and Operations Management

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“…Among the studies on outpatient scheduling, the ones that consider (i) patient sequencing (ii) appointment time decisions, or (iii) patient scheduling in a multiple resource setting are relevant to our study. As also pointed out in Ahmadi‐Javid et al (2017), most of the articles assume a predetermined patient sequence due to the complexity of sequencing problem (Begen & Queyranne, 2011; Denton & Gupta, 2003; Kong, Li, Liu, Teo, & Yan, 2020). In the context of single‐server scheduling, only a few articles consider sequencing and time setting decisions simultaneously (Berg, Denton, Erdogan, & Rohleder, 2014; Denton, Viapiano, & Vogl, 2007; Mak, Rong, & Zhang, 2014; Mancilla & Storer, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

A stochastic programming approach for chemotherapy appointment scheduling

Demir

Gül

Çelik

2020

Naval Research Logistics

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Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in premedication and infusion durations. In this paper, we formulate a two‐stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real‐life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty.

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“…Approximately 29% of patients reported receiving responses within 2 hours of a request, which is considered rapid. Numerous studies in the appointment scheduling area have shown the importance of managing waiting time in health care management [45][46][47]. Marketing research has shown that waiting time is a crucial determinant of customer satisfaction and loyalty [48].…”

Section: Main Findings and Comparison With Other Studiesmentioning

confidence: 99%

Factors Affecting Portal Usage Among Chronically Ill Patients During the COVID-19 Pandemic in the Netherlands: Cross-sectional Study

Kong¹,

Riedewald²,

Askari³

2021

JMIR Hum Factors

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Background The COVID-19 pandemic has impacted the capacity of the regular health care system, which is reflected in limited access to nonurgent care for patients who are chronically ill in the Dutch health care system. Nevertheless, many of them still depend on health care assistance to manage their illnesses. Patient portals are used to provide continued health care (remotely) and offer self-management tools during COVID-19 and potentially after. However, little is known about the factors influencing portal use and users’ satisfaction among patients who are chronically ill during the COVID-19 pandemic. Objective This study aims to examine predictors of patient portal use among patients who are chronically ill, the willingness to recommend the portal to others, and the likelihood of future use among portal nonusers. Methods An online self-administered questionnaire was distributed among patients who are chronically ill via social media in May 2020. The questionnaire consisted of four parts: (1) demographics including age and hours of daily internet use; (2) physical health status including COVID-19 infection, perceived level of control, and hospital visits; (3) mental health status including depression and life satisfaction; and (4) portal use including response waiting time and awareness. Descriptive, correlation, univariate, and multivariate analyses were conducted to identify factors that affect portal use, users’ willingness to recommend, and nonusers’ likelihood of future portal use. Results A total of 652 patients responded, and 461 valid questionnaires were included. Among the 461 patients, 67% (n=307) were identified as patient portal users. Of the nonusers, 55% (85/154) reported not being aware of the existence of a patient portal at their hospital. Significant predictors of portal use include level of control (P=.04), hospital visit time (P=.03), depression scale (P=.03), and status of life satisfaction (P=.02). Among portal users, waiting time to get a response via the portal (P<.001) and maximum acceptable waiting time (P<.001) were the strongest predictors for willingness to recommend the portal; among nonusers, the model predicted that those who were not aware of patient portals (P<.001) and were willing to wait moderately long (P<.001) were most likely to use the portal in the future. Conclusions This study provides insights into factors that influence portal use and willingness to recommend, based on which health care providers can improve the adoption of patient portals and their services. It suggests that health care providers should leverage efficient operations management to improve responsiveness and reduce waiting time to enhance user satisfaction and willingness to recommend use. Health care organizations need to increase portal awareness among nonusers and train their patients to increase both use and longer adoption of patient portals. Factors including depression and life satisfaction can influence portal use; therefore, future studies on determinants of portal use and nonuse in this specific population are needed.

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Appointment Scheduling Under Time-Dependent Patient No-Show Behavior

Cited by 78 publications

References 25 publications

Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis

Constant Job‐Allowance Policies for Appointment Scheduling: Performance Bounds and Numerical Analysis

A stochastic programming approach for chemotherapy appointment scheduling

Factors Affecting Portal Usage Among Chronically Ill Patients During the COVID-19 Pandemic in the Netherlands: Cross-sectional Study

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