A probabilistic patient scheduling model for reducing the number of no-shows

Ruiz-Hernández, Diego; García-Heredia, David; Delgado-Gómez, David; Baca‐García, Enrique

doi:10.1080/01605682.2019.1658552

Cited by 7 publications

(16 citation statements)

References 29 publications

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“…Coping strategies derived from the prior and ongoing research have helped provide a brief overview of the mechanisms of how to deal with no-shows. Following the studies of Daggy et al (2010) and Ruiz-Hernandez et al (2020), coping strategies for no-show behaviour can be divided into two categories according to their managing patterns: direct and indirect. Direct strategies aim to increase an individual's intention to be present at the scheduled time.…”

Section: Coping Strategies For No-show Behaviourmentioning

confidence: 99%

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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Section: Coping Strategies For No-show Behaviourmentioning

confidence: 99%

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

2022

JSTP

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“…These environmental conditions were integrated into an online linear integer program to optimize patient allocation. Later on, Ruiz Hernández et al [ 11 ] proposed a mixed deterministic integer program. The model is probabilistic in the sense that it incorporates the expected income of the center weighted by the probabilities of no-show predicted for each patient.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We now present the results obtained in the experiment. The performance of the proposed model is compared with the following: (i) the system currently implemented in the health center which assigns the patient to the first available slot with a fixed duration of 30 minutes (FIFO constant); (ii) the system which would assign each patient to the first available slot but would use the estimate of the number of slots they would need (FIFO variable); and (iii) the model proposed by Ruiz-Hernandez et al [ 11 ] that assigns patients based on their probabilities using patient constant appointment time (Time constant). Our model will be referred to as time variable.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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A Probabilistic Patient Scheduling Model with Time Variable Slots

Carreras-García

Delgado-Gómez

Baca‐García

et al. 2020

Computational and Mathematical Methods in Medicine

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One of the current challenges faced by health centers is to reduce the number of patients who do not attend their appointments. The existence of these patients causes the underutilization of the center’s services, which reduces their income and extends patient’s access time. In order to reduce these negative effects, several appointment scheduling systems have been developed. With the recent availability of electronic health records, patient scheduling systems that incorporate the patient’s no-show prediction are being developed. However, the benefits of including a personalized individual variable time slot for each patient in those probabilistic systems have not been yet analyzed. In this article, we propose a scheduling system based on patients’ no-show probabilities with variable time slots and a dynamic priority allocation scheme. The system is based on the solution of a mixed-integer programming model that aims at maximizing the expected profits of the clinic, accounting for first and follow-up visits. We validate our findings by performing an extensive simulation study based on real data and specific scheduling requirements provided by a Spanish hospital. The results suggest potential benefits with the implementation of the proposed allocation system with variable slot times. In particular, the proposed model increases the annual cumulated profit in more than 50% while decreasing the waiting list and waiting times by 30% and 50%, respectively, with respect to the actual appointment scheduling system.

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“…Researchers have demonstrated gains in physician, staff, and patient satisfaction, improvement in the ability to match patients with their chosen physician, and reduction in the wait time to an appointment ( Murray & Berwick, 2003 ). Researchers have also analyzed the probability of a patient keeping a scheduled appointment by assessing various demographic and socioeconomic characteristics ( Huang & Marcak, 2015 ; Li et al, 2019 ; Ruiz-Hernández et al, 2019 ). These characteristics include, but are not limited to, the patient's insurance coverage, gender, age, and the type of appointment.…”

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confidence: 99%

The Influence of the Scheduling Horizon on New Patient Arrivals

Woodcock

Nokes

Bolton

et al. 2020

Journal of Ambulatory Care Management

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The goal of scheduling within an ambulatory enterprise is to appropriately accommodate patients; extending capacity to fulfill this aim in a large health care organization requires the management of a complex scheduling process. Understanding and handling the appointment lead time, referred to as the scheduling horizon, can positively influence capacity management. The analysis demonstrated an increased chance of nonarrived appointments of 16% for a specialty practice and 11% for a primary care practice for every 30-day delay in the scheduling horizon. By incorporating the management of the scheduling horizon, health care organizations can optimize the capacity of their ambulatory clinics.

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A probabilistic patient scheduling model for reducing the number of no-shows

Abstract: This is a repository copy of A probabilistic patient scheduling model for reducing the number of no-shows.

Cited by 7 publications

References 29 publications

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

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

A Probabilistic Patient Scheduling Model with Time Variable Slots

The Influence of the Scheduling Horizon on New Patient Arrivals

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