Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics

Liu, Jiayi; Xie, Jingui; Yang, Kum Khiong; Zheng, Zhichao

doi:10.2139/ssrn.2783646

Cited by 4 publications

(7 citation statements)

References 53 publications

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“…, Kim and Chan , Liu et al. , Singh KC and Terwiesch ). Future research can identify suitable instrumental variables and further examine this endogeneity bias.…”

Section: Resultsmentioning

confidence: 99%

Individualized No‐Show Predictions: Effect on Clinic Overbooking and Appointment Reminders

Tang

Johnson

et al. 2019

Production and Operations Management

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Patient no‐shows and late cancellations lead to clinic inefficiency, high clinic costs and low patient satisfaction. The two main strategies clinics employed to alleviate the adverse effects of no‐shows are overbooking and patient appointment reminders. Developing effective overbooking schedules depends on accurately predicting each patient’s no‐show probability, while developing effective reminder systems requires a patient‐level estimate of communication sensitivity. Current methods of estimating no‐show probabilities do not produce such patient‐level predictions. To remedy this, we develop a Bayesian nested logit model which utilizes appointment confirmation data and estimates individual‐level coefficients for patient‐specific predictors. A log‐likelihood comparison of model fit on 12 months of appointment data shows that the Bayesian model outperforms the standard logit model by about 30% improvement in model fit. Additionally, our Bayesian model allows categorization of patients based on their appointment confirmation behavior. Finally, using patient‐specific no‐show probabilities as an input to a simulated appointment scheduler we find that the Bayesian model improves clinic profit over the standard logit model. The benefit comes mainly from waiting cost reduction when no‐show probability is low and from physician overtime and idle time cost reduction when no‐show probability is high. Our study has two managerial implications. First, the Bayesian method allows customizing appointment reminder effort based on patient’s confirmation behavior. Second, the Bayesian method also allows improved overbooking scheduling especially in clinics that experience large patient throughput.

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“…, Kim and Chan , Liu et al. , Singh KC and Terwiesch ). Future research can identify suitable instrumental variables and further examine this endogeneity bias.…”

Section: Resultsmentioning

confidence: 99%

Individualized No‐Show Predictions: Effect on Clinic Overbooking and Appointment Reminders

Tang

Johnson

et al. 2019

Production and Operations Management

View full text Add to dashboard Cite

show abstract

“…Our study contributes to this stream of literature by linking no-show influencing factors to different appointment service stages and providing corresponding coping strategies. As emphasised in the study of Liu et al (2019a), a consumer's ultimate attendance may follow a series of scheduling decisions, which forms a dynamic process. Whether customers visit on the scheduled day is highly correlated with their evaluations and perceptions of the preceding stages (Sulek et al, 2006).…”

Section: Factors Influencing No-show Behaviourmentioning

confidence: 99%

“…In addition, appointment reminders are used to prevent no-shows caused by either forgetting or getting confused about the date, time or location of a scheduled appointment (Arora et al, 2015). Active rescheduling allows customers to update their appointments to more preferred times, which can significantly reduce the likelihood of no-show (Liu et al, 2019a) No-show behaviour in appointment confirmed that direct coping strategies are more effective in dealing with no-show issues (Spadea et al, 2010).…”

Section: Coping Strategies For No-show Behaviourmentioning

confidence: 99%

“…, 2015). Active rescheduling allows customers to update their appointments to more preferred times, which can significantly reduce the likelihood of no-show (Liu et al. , 2019a).…”

Section: Related Literaturementioning

confidence: 99%

“…Previous studies have proposed several coping strategies for no-show behaviour in appointment services, such as reminders, fines, overbooking, open-access scheduling, rescheduling, cancellation policies and changing panel size (Bech, 2005;Huang and Zuniga, 2014;Li et al, 2019;Liu et al, 2019a;Tsai and Teng, 2014). However, consumers' no-show behaviour still frequently arises.…”

Section: Introductionmentioning

confidence: 99%

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Coping with no-show behaviour in appointment services: a multistage perspective

2022

JSTP

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PurposeThis study aims to help businesses cope with consumers' no-show behaviour from a multistage perspective. It specifically identifies no-show reasons at each stage of appointment services and proposes the corresponding coping strategies.Design/methodology/approachBy focusing on an outpatient appointment service, we interviewed 921 no-show patients to extract no-show reasons, invited 18 hospital managers to propose coping strategies for these reasons using a Delphi method and evaluated the proposed strategies based on EDAS (Evaluation based on Distance from Average Solution).FindingsThe results reveal ten reasons for no-show behaviour (i.e. system service quality, overuse, did not know the appointment, self-judgment, forget, waiting time, lateness, uncontrollable problems, time conflict and service coordination), which have nine coping strategy themes (i.e. prepayment, system intelligence, target, subjective norm, system integration, ease of navigation, reminder, confirmation and cancellation). We classify the ten reasons and nine themes into scheduling, waiting and execution stages of an appointment service.Originality/valueThis study provides a package of coping strategies for no-show behaviour to deal with no-show reasons at each appointment service stage. It also extends the research in pre-service management through appointment services.

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Partially partitioned templating strategies for outpatient specialty practices

Bai

Berg

Sir

et al. 2023

Production and Operations Management

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Templating strategies specify policies on capacity allocation and appointment booking, which are central to patient access management in outpatient specialty practices (SPs). In the widely implemented partitioned templating strategy, appointment capacity is allocated exclusively to each patient group defined by a combination of patient attributes and medical conditions, which often results in utilization challenges for SPs. Motivated by problems faced by an SP within a large academic medical center, we propose partially partitioned templating strategies that cluster patient groups into access classes and allocate appointment slots to these classes. We formulate a twostage stochastic optimization model to simultaneously optimize decisions on patient group clustering and capacity allocation in the templating strategy design. We develop an efficient solution algorithm for the problem, which otherwise poses a combinatorial challenge, based on Benders' cuts and an anticipatory approximation. Numerical experiments using real-world data show that the partially partitioned strategies outperform benchmark strategies used by SPs through maintaining a balance of high capacity utilization and providing timely access for priority patients. In addition, we study a wide range of SP setups for transferable insights on templating strategies based on practice specifics.

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Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics

Cited by 4 publications

References 53 publications

Individualized No‐Show Predictions: Effect on Clinic Overbooking and Appointment Reminders

Individualized No‐Show Predictions: Effect on Clinic Overbooking and Appointment Reminders

Coping with no-show behaviour in appointment services: a multistage perspective

Partially partitioned templating strategies for outpatient specialty practices

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