Case study of the prediction of elective surgery durations in a New Zealand teaching hospital. (14th October 2020)

• Record Type: Journal Article
• Authors: Soh, Kian Wee; Walker, Cameron; O'Sullivan, Michael; Wallace, Jonathan; Grayson, David
• Is Part Of: International journal of health planning and management. Volume 35:Number 6(2020)
• Page Start: 1593
• Page End: 1605
• Publication Date: 2020-10-14
• Subjects: elective surgery -- electronic booking form -- linear regression -- prediction -- scheduling; Health planning -- Periodicals; Health services administration -- Periodicals; Santé publique -- Planification -- Périodiques; Santé, Services de -- Administration -- Périodiques; 362.1068
• DOI: 10.1002/hpm.3046
• Languages: English
• ISSNs: 0749-6753

Abstract

Summary: We present an elective surgery redesign project involving several New Zealand hospitals that is primarily data‐driven. One of the project objectives is to improve the predictions of surgery durations. We address this task by considering two approaches: (a) linear regression modelling, and (b) improvement of the data quality. For (a) we evaluate the accuracy of predictions using two performance measures. These predictions are compared to the surgeons' estimates that may subsequently be adjusted. We demonstrate using the historical surgical lists that the estimates from our prediction techniques improve the scheduling of elective surgeries by minimising the occurrences of list under‐ and over‐runs. For (b), we discuss how the surgical data motivates a review of the surgery procedure classification which takes into account the design of the electronic booking form. The proposed hierarchical classification streamlines the specification of surgery types and therefore retains the potential for improved predictions.