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Machine learning for healthcare behavioural or: addressing waiting time perceptions in emergency care

Gartner, Daniel and Padman, Rema 2020. Machine learning for healthcare behavioural or: addressing waiting time perceptions in emergency care. Journal of the Operational Research Society 71 (7) , pp. 1087-1101. 10.1080/01605682.2019.1571005

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Recent research has highlighted the need to improve patient satisfaction by reducing perceived waiting times in hospitals. This study examines factors that are associated with waiting time estimation behaviour and how to control flow of patients who overestimate waiting times. Using data from more than 250 patients, we test the applicability of machine learning methods to understand under-, correct and overestimation behaviour of waiting times in two emergency department areas. Our attribute ranking and selection methods reveal that actual waiting time, clinical attributes, and the service environment are among the top ranked and selected attributes. The classification methods reveal that the precision to classify a patient to the true outcome of overestimating waiting times reaches almost 70% in the first waiting area. If a patient waits in a treatment room which is the second waiting area under study, this precision level reaches almost 78%. We developed a discrete-event simulation model which we linked with the machine learning models of each waiting area. Our scenario analysis revealed that changing staffing patterns can lead to a substantial drop-off in the number of patients overestimating waiting times. Our results can be employed to control waiting time perceptions and, potentially, increase patient satisfaction.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Publisher: Palgrave Macmillan / Taylor & Francis
ISSN: 0160-5682
Date of First Compliant Deposit: 17 January 2019
Date of Acceptance: 14 January 2019
Last Modified: 25 Nov 2020 08:59

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