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Sarah Cumbers (RSS) presenting Florence Nightingale Award to Strategy Unit staff Tom Jemmett, Jac Grout, YiWen Hon & Chris Beeley
News 16/07/2025

Strategy Unit demand model wins prestigious Florence Nightingale Award

Our open-source demand model, developed in collaboration with the New Hospital Programme, has been named the 2025 winner of the Florence Nightingale Award for Excellence in Health and Care Analytics.

Hospital building
News 08/04/2025

Our role in the New Hospital Programme

​​Learn how the Strategy Unit’s innovative model is transforming hospital planning by providing a consistent, data-driven approach to forecast future demand and evidence-based decision making.

Report 29/09/2023

Learning about what works in urgent community response

The initial report from the national urgent community response (UCR) evaluation, along with an economic modelling tool to help service providers and systems understand the impact of UCR, is now available.

woman in winter dress holding heart
Report 22/03/2023

The NHS as an anchor institution: addressing fuel poverty

The number of households in fuel poverty in Staffordshire and Stoke-on-Trent (SSoT) is higher than the national average. As anchor institutions, NHS organisations can use their assets to influence the health and wellbeing of their local communities. The Strategy Unit was asked by the Midlands NHS Greening Board to evaluate a cross-sector initiative in SSoT to help alleviate fuel poverty using savings generated through solar panels on NHS buildings. The project is called Keep Warm, Keep Well.

Blog post 13/05/2022

Urgent Community Response – What Works?

The Strategy Unit, with our partners Ipsos, has been commissioned by NHS England and NHS Improvement (NHSEI) to provide a long-term national evaluation of the Urgent Community Response programme rolled-out across England. The programme aims to shift resources to home and community-based services as part of the NHS commitment to providing the right care, to the right people, at the right time. And there are a range of outputs from the early work that provide learning for local systems as they develop their services.

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