Predictive and prescriptive analytics for multi-site modelling of frail and elderly patient services

Williams, Elizabeth ORCID: https://orcid.org/0000-0003-4515-441X, Gartner, Daniel ORCID: https://orcid.org/0000-0003-4361-8559 and Harper, Paul ORCID: https://orcid.org/0000-0001-7894-4907 2025. Predictive and prescriptive analytics for multi-site modelling of frail and elderly patient services. Journal of the Operational Research Society 10.1080/01605682.2025.2487650

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Abstract

Many economies are challenged by the effects of an ageing population, particularly in sectors where resource capacity planning is critical, such as healthcare. This research addresses the operational challenges of bed and staffing capacity planning in hospital wards by using predictive and prescriptive analytical methods, both individually and in tandem. We applied these methodologies to a study of 165,000 patients across a network of 11 hospitals in the UK. Predictive modelling, specifically Classification and Regression Trees, forecasts patient length of stay based on clinical and demographic data. On the prescriptive side, deterministic and two-stage stochastic optimisation models determine optimal bed and staff planning strategies to minimise costs. Linking the predictive models with the prescriptive optimisation models, generates demand forecasts that inform the optimisation process, providing accurate and practical solutions. The results demonstrate that this integrated approach captures real-world variations in patient LOS and offers a 7% cost saving compared to average-based planning. This approach helps healthcare managers make robust decisions by incorporating patient-specific characteristics, improving capacity allocation, and mitigating risks associated with demand variability. Consequently, this combined methodology can be broadly extended across various sectors facing similar challenges, showcasing the versatility and effectiveness of integrating predictive and prescriptive analytics.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Mathematics
Publisher:	Taylor and Francis Group
ISSN:	0160-5682
Date of First Compliant Deposit:	2 April 2025
Date of Acceptance:	29 March 2025
Last Modified:	07 May 2025 13:55
URI:	https://orca.cardiff.ac.uk/id/eprint/177345

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