AI in the NHS: From Policy to Practice – What the 10-Year Health Plan Means for the Future of Care
- Sep 3
- 4 min read
Artificial intelligence (AI) is no longer a distant prospect for the NHS. Recent policy documents, including the 10-Year Health Plan for England, set out an ambitious vision: to make the NHS the most AI-enabled healthcare system in the world. By 2035, AI will be embedded across nearly every clinical pathway, transforming diagnosis, prevention, workforce productivity, and patient engagement.
But how do these policies translate into practice? And what should NHS leaders, clinicians, and patients expect from this digital revolution?
The NHS App: Becoming the Digital Front Door
Central to the strategy is the transformation of the NHS App. By 2028, it is intended to serve as the “full front door” to the NHS.
An AI-powered My NHS GP tool will provide instant triage for non-urgent care, guiding patients towards self-care, community pharmacy, general practice, or emergency services.
This aims to reduce demand for GP appointments while providing patients with faster, more personalised support.
Beyond triage, the App will integrate patient records, feedback tools, and personalised health advice powered by generative AI.
For patients, this means greater convenience. For the system, it promises efficiency — but only if AI advice is trusted, safe, and equitable.
AI Scribes: Reducing the Administrative Burden
One of the most tangible early wins for AI is ambient “AI Scribe” technology.
These tools automatically capture consultation notes, draft referral letters, and enter data into records.
Trials suggest they can save up to 90 seconds per appointment, equivalent to 2,600 full-time GPs worth of capacity.
For clinicians, this means more time with patients and less time on paperwork.
If rolled out carefully, AI scribes could help address some of the workload pressures driving NHS staff burnout.
Genomics and Predictive Analytics: Personalised Prevention
Another cornerstone of the plan is the creation of a genomics population health service by the end of the decade.
Newborn genomic testing, supported by AI, will enable earlier detection of risks and inform prescribing to reduce adverse drug reactions.
Predictive analytics combined with AI could allow the NHS to intervene years before symptoms appear — shifting from treatment to prevention.
The vision is healthcare “free at the point of risk,” not just the point of need.
This is perhaps the most transformative use of AI — but also the most complex to deliver, requiring secure data governance and public trust.
AI for Diagnostics and Clinical Support
AI is already reshaping diagnostics.
Dermatology hubs are using AI for triage of suspected skin cancer, with the approach expected to become standard by 2028.
Similar AI-driven support is planned for ophthalmology, cardiology, respiratory medicine, radiology, and pathology.
AI decision-support tools will integrate with the Single Patient Record, helping clinicians order tests, make referrals, and prescribe in line with evidence-based protocols.
Faster, more accurate diagnostics could dramatically reduce waiting lists — but only if deployment avoids past IT pitfalls that added complexity rather than streamlining care.
Training the Most AI-Enabled Workforce in the World
The government’s ambition is bold: to create the most AI-enabled workforce in the world.
Training curricula will be overhauled in the next three years to include AI and digital skills.
Upskilling programmes will give clinicians confidence in AI-supported care, from data privacy principles to interpreting AI outputs.
Success depends on ensuring AI is seen as a trusted assistant, not a replacement.
Without genuine workforce buy-in, even the best AI systems will struggle to achieve adoption at scale.
AI for Financial Sustainability and Productivity
Underlying much of the policy is a drive for productivity.
AI is expected to deliver at least 2% year-on-year productivity gains for the next three years.
By automating admin and streamlining pathways, the NHS hopes AI can ease workforce pressures and improve financial sustainability.
Applications range from ambulance dispatch optimisation to drug discovery through AI-assisted molecular modelling.
For leaders, the challenge will be ensuring that productivity gains translate into better patient outcomes, not just cost-cutting measures.
Balancing Innovation with Inclusivity
The policy documents emphasise an important caveat: AI must not repeat past mistakes. Poorly designed digital tools have historically excluded certain patient groups or added to staff burden rather than reducing it.
To succeed, AI in the NHS must:
Be transparent and explainable, building public trust.
Include rigorous safeguards for data privacy and security.
Be designed inclusively, ensuring equitable access for patients and staff.
Conclusion: From Ambition to Delivery
The NHS’s AI policy framework is ambitious, wide-ranging, and full of potential. From triage apps to genomic prevention, AI promises to reshape healthcare for patients and professionals alike.
But success depends on more than technology. It requires investment, governance, workforce readiness, and cultural change. If these conditions are met, the NHS can indeed become a global leader in safe, ethical, and effective use of AI in healthcare.
At Healthcare Innovation Consultancy, we help NHS leaders translate policy into practice. Whether you’re exploring AI-enabled diagnostics, workforce training, or governance frameworks, our expertise ensures safe, effective, and patient-centred adoption.
Book a discovery call today to explore how we can help you secure the NHS’s AI-powered future.