Testing and spreading AI in health care: the case for rapid revolution (The Health Foundation, 23 March 2026)

Summary

Productivity-enhancing technologies remain the big hope for sustaining a high-quality NHS in future. The Health Foundation Chief Executive, Jennifer Dixon, looks at efforts to adopt AI applications quickly and at scale.

Learning from the world’s most technology-enabled health care providers, Jennifer draws on case examples from some familiar places, such as Kaiser Permanente, the Mayo Clinic, Johns Hopkins Hospital, Massachusetts General Hospital and Memorial Sloan Kettering Cancer Centre. And some less familiar, such as Samsung Medical Centre (South Korea), Changi General Hospital (Singapore) and the Rigshospitalet in Denmark.

Content

Common ingredients for success

While the regulatory environment for each country is different, some common ingredients for success are emerging. Across these examples we tended to see: 

1. Significant investment made over the years in their data infrastructure.
2. Some kind of innovation centre or hub allowing access by in-house clinicians and scientists, and by vendor partners, to test ideas using patient-level data.
3. A balanced approach to AI development – part in-house, often led by clinicians, and part procured from an AI vendor.
4. A centre or unit focused on AI governance, including standard agreed rules for testing AI in real-world contexts. These focused on going beyond the early-stage development of AI models to investigate how things panned out ‘on the ground’ when implemented.
5. Partnerships with large technology companies, such as Amazon Web Services, Microsoft and Google, stretching over years.
6. Built-in training for staff on how to develop, test and use AI effectively.

Many of these health facilities focused on AI to tackle challenges common to many settings, such as: 

• Improving productivity and releasing clinical capacity, particularly by reducing administrative burden and improving operational efficiencies.
• Reducing waiting times by enabling earlier clinical interventions, streamlining processes and pathways, and speeding up discharge.
• Improving safety and clinical outcomes via predictive analytics to identify high-risk patients or post-operative complications.
• Promoting personalised medicine, combining genomics, imaging and electronic health record data to advance research and provide tailored treatments.