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
10.1080/01605682.2025.2573825
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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 |
|---|---|
| Date Type: | Published Online |
| 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: | 24 Oct 2025 09:29 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/181545 |
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