Scaling preventative AI using better data for better decisions
This month, over 200 healthtech founders, investors, clinicians, and policymakers convened in London to confront a critical challenge: transforming healthcare through the strategic use of artificial intelligence (AI) and data.
Attendees from across the health and care ecosystem gathered at the latest networking event from The Future Health, sponsored by NTT DATA, Better, MBI Health, Lifelight, and Insource.
The evening featured candid fireside chats that spanned two central themes: scaling preventive AI and the critical need to unlock better clinical decisions through improved data.
The unmet promise of preventative AI
The first discussion explored how we could use AI to move healthcare from reactive treatment to proactive, preventive care on a large scale.
Whilst the NHS 10-Year Plan advocates for this, panellists noted that systemic, cultural, and financial obstacles persist.
For example, a hypertension solution achieved adherence rates of up to 80% and significantly reduced the risks of stroke and heart attack. Yet adoption stalled because clinicians felt the burden of extra work without seeing the direct benefit.
A technology-enabled care pilot delivered both cost savings and improved outcomes, but did not move forward because the NHS and local authorities could not agree on who should pay.
Such issues reflect a fragmented NHS, with siloed budgets, and built on a doctor-centred, paternalistic model.
Aligned incentives and ring-fenced investment must back rhetoric about prevention.
Evidence, trust and the equity challenge
Evidence and trust were also highlighted as barriers. Enthusiastic technologists can share innovations such as an AI-driven smartphone tool for measuring blood pressure.
However, broader clinical adoption requires peer-reviewed studies, regulatory approval, and real-world validation.
Technologies that are technically sound often stall because clinicians demand robust proof. AI’s “black box” nature can heighten this scepticism.
Perhaps reframing AI as “statistical algorithms” could reduce anxiety and build confidence?
A question about liability highlighted clinician anxiety. Despite the benefits of automation, responsibility for patient outcomes remains with clinicians.
Regulators are seeking to address such ethical questions and keep humans “in the loop” – a key step in achieving widespread adoption.
Meeting equity and investment needs
Equity was a recurring theme.
AI promises truly personalised medicine, from adaptive obesity treatments to tailored drug dosages. However, underserved populations risk being excluded without diverse, high-quality datasets.
At the same time, NHS procurement culture can prioritise “tech” over “solutions,” buying novel innovation rather than equity-focused solutions.
It is a significant issue that impacts AI usage for the NHS and elsewhere.
Where do we need to invest?
When asked how they would spend £100 million, panellists resisted funding “shiny new tools.”
They called for investment in clinician education, digital literacy, and support for the workforce so AI can become a valuable tool for staff.
AI needs better data for better decisions
If AI is to transform healthcare genuinely, it will need trusted data.
The second fireside chat focused on the NHS’s digital ambitions and the data challenges hindering them.
Without high-quality, reliable data, even the most advanced AI will fail.
True interoperability is not about apps talking directly but about building a unified data layer that enables safe, consistent, and scalable information exchange across all of health and care.
Tom Winstanley, CTO at NTT DATA UK&I, offered four steps toward achieving this:
- Define data stewards across hospitals, not just within CIO teams.
- Build education and awareness at operational levels for consistent data capture.
- Embed governance into systems, codifying “who can do what, when, and why.”
- Leverage AI itself for data cleansing, maintenance, and bias detection.
Ultimately, the core barriers are less about the technology and more about people and processes.
Policy and funding must support digitisation and the cultural and infrastructural shift required to treat data as a strategic clinical asset.
The path forward
Both sessions converged on a powerful insight: AI and data are not silver bullets.
Their success is entirely dependent on systemic alignment—of incentives, evidence, trust, equity, and governance.
Embedding prevention and trusted data into the heart of care delivery will be decisive.
The Future Health event made it clear that while no single solution exists, creating spaces for collaboration between clinicians, technologists, investors, and policymakers is an essential step in forging a shared path forward.
Join us at our next session to progress the conversation.
