Ruiz-Hernández, D., García-Heredia, D., Delgado-Gómez, D. et al. (1 more author) (2019) A probabilistic patient scheduling model for reducing the number of no-shows. Journal of the Operational Research Society, 71 (7). pp. 1102-1112. ISSN 0160-5682
Abstract
No-shows in medical centres cause under-utilisation of resources and increase waiting times in specialty health care services. Although this problem has been addressed in literature, behavioural issues associated with the patient's socio-demographic characteristics and diagnosis have not been widely studied. In this article, we propose a model that includes such behavioural issues in order to reduce impact of no-shows in medical services. The objective is maximising the health centre's expected revenue by using show-up probabilities estimated for each combination of patient and appointment slot. Additionally, the model considers the requirements imposed by both the health centre's management and the health authorities. An extension of the model allows overbooking in some appointment slots. Experimental results show that the proposed model can reduce the waiting list length by 13%, and to attain an increase of about 5% in revenue, when comparing to a model that assigns patients to the first available slot.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019 Operational Research Society. This is an author-produced version of a paper subsequently published in Journal of the Operational Research Society. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | appointment scheduling; no-shows; overbooking; healthcare; behavioural OR |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Oct 2019 10:28 |
Last Modified: | 16 Dec 2021 10:35 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/01605682.2019.1658552 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151553 |