AI Centre for Value Based Health Care featured in new European Observatory on Health Systems and Policies book on AI and Health Policy

“Demystifying artificial intelligence in health: What health policy-makers need to know” is a new publication written for those working in, or alongside, the health sector who are increasingly required to engage with artificial intelligence (AI). The book aims to demystify AI for health policy-makers, offering a clear-eyed view of its capabilities, limitations and implications. It equips leaders with the foundations for informed decision-making, helping them ask the right questions, understand trade-offs, and shape a future in which AI serves public health goals. Crucially, it argues that realising AI’s potential requires principled adoption, rigorous evaluation and inclusive governance arrangements, ensuring that AI remains a means to an end rather than an end in itself.

Among the case studies included is the OneLondon Secure Data Environment (SDE) and the work delivered within it by the AI Centre for Value Based Health Care. The case study explores how secure, well-governed data infrastructure can provide the foundations for responsible and scalable AI deployment within the NHS.

The OneLondon SDE provides a secure data environment that enables real-time access to linked health data to support system-wide decision-making across London. Applications include optimising care pathways for long-term conditions, identifying high-risk patients for targeted intervention, and generating population health insights to inform planning and service transformation. By embedding strong governance, transparency and oversight into its architecture, the SDE establishes the trusted conditions necessary for advanced analytics and AI.

Within this secure environment, The AI Centre has developed reusable and adaptable analytic pipelines and interoperable building blocks to reduce duplication, enable reproducibility and move beyond static, one-off datasets towards dynamic, real-time data flows. The infrastructure supports multimodal data analysis across hospital trusts through federated learning approaches that allow AI model development without physically transferring sensitive patient data. In parallel, efforts to standardise data from proprietary hospital systems and to apply advanced natural language processing to transform unstructured clinical notes into structured, analysable information are expanding the scope and richness of the data available.

Public trust remains central to this work. Through public engagement initiatives, including citizen summits and collaboration with Integrated Care Boards and patient committees, OneLondon has embedded patient and public voice into the governance of data use. This commitment to transparency and accountability reinforces the principle that responsible AI depends not only on technical capability, but on public trust.

The inclusion of this case study highlights an important message for health systems: sustainable AI cannot be built on isolated pilots or fragmented datasets. It requires strong foundations, secure environments, interoperable systems, robust governance and meaningful public engagement, aligned with policy priorities and the public interest.

As AI technologies move from research environments into health systems, the book provides a timely and practical guide for decision-makers navigating this complex landscape and seeking to translate technological capability into measurable value for patients and populations.

To read the publication use below link:

Demystifying artificial intelligence in health: what health policy-makers need to know