Managing clinic variability with same-day scheduling, intervention for no-shows, and seasonal capacity adjustments

Kum-Khiong Yang and Tugba Cayirli

Journal of the Operational Research Society, 2020, vol. 71, issue 1, 133-152

Abstract: This study investigates demand and capacity strategies for managing clinic variability. These include (i) same-day scheduling to control random walk-ins, (ii) no-show intervention, where the clinic calls advance-booked patients a day before to identify and release cancelled slots to same-day patients, and (iii) adjustments to daily number of appointments for advance-booked patients to match seasonal variations in same-day demand. These strategies are tested over the individual-block/fixed-interval (IBFI) and the Dome appointment rules. Our results show that choosing the appropriate refinements in the order of appointment rules, same-day scheduling, no-show intervention, and capacity adjustment provides maximum improvement. The total cost benefit of demand strategies (i) and (ii) is 7 to 21%, whereas the benefit of capacity strategy (iii) is as high as 6%. Our study affirms the universality of the Dome rule to perform well when combined with the demand and capacity strategies across different environments.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2018.1557023 (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:1:p:133-152

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2018.1557023

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 ().