A probabilistic patient scheduling model for reducing the number of no-shows
Diego Ruiz-Hernández,
David García-Heredia,
David Delgado-Gómez and
Enrique Baca-García
Journal of the Operational Research Society, 2020, vol. 71, issue 7, 1102-1112
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.
Date: 2020
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1658552 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:71:y:2020:i:7:p:1102-1112
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20
DOI: 10.1080/01605682.2019.1658552
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald
More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().