A hybrid predictive and prescriptive modelling framework for long-term mental healthcare workforce planning

Halgamuwe Hewage, Harsha and Rostami-Tabar, Bahman ORCID: https://orcid.org/0000-0002-3730-0045 2025. A hybrid predictive and prescriptive modelling framework for long-term mental healthcare workforce planning. Journal of the Operational Research Society Item availability restricted.

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Abstract

Over the past decade, severe staffing shortages in mental healthcare have worsened due to rising demand, further exacerbated by COVID-19. This demand is expected to grow over the next decade, necessitating proactive workforce planning to ensure sustainable service delivery. Despite its critical importance, the literature lacks a comprehensive model to address long-term workforce needs in mental healthcare. Additionally, our discussions with UK NHS mental health practitioners highlight the practical need for such a model. To bridge this gap, we propose a hybrid predictive-prescriptive modelling framework that integrates long-term probabilistic forecasting with an analytical stock-flow model for mental health workforce planning. Given the pivotal role of nurses, who comprise one-third of the mental health workforce, we focus on forecasting nursing headcount while ensuring the model’s adaptability to broader healthcare workforce planning. Using statistical and machine learning methods with real-world NHS data, we first identify key factors influencing workforce variations, develop a long-term forecasting model, and integrate it into an analytical stock-flow framework for policy analysis. Our findings reveal the unsustainable trajectory of current staffing plans and highlight the ineffectiveness of blanket policies, emphasizing the need for region-specific workforce strategies.

Item Type:	Article
Status:	In Press
Schools:	Schools > Business (Including Economics)
Subjects:	H Social Sciences > HA Statistics
Uncontrolled Keywords:	OR in health service, forecasting, workforce planning, mental health, machine learning
Publisher:	Taylor and Francis Group
ISSN:	0160-5682
Date of First Compliant Deposit:	8 October 2025
Date of Acceptance:	8 October 2025
Last Modified:	09 Oct 2025 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/181545

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