Lessons Learned and the Future of AI in Primary Care

Looking back now in August 2025, from that optimistic March morning when we first switched on our AI scribe to the humid July evening when we finally achieved go-live, I'm struck by both how far we've come and how much further we still have to go. The promise of AI in primary care remains compelling, but the reality of implementation has taught me lessons that no policy document could have prepared me for.

The True Cost of Transformation

If I could tell practices one thing about embarking on this journey, it would be this: budget not just for the technology adoption, but for the hidden costs that nobody mentions in the marketing materials. The implementation required substantial time investment from clinical staff, practice manager, and IT support.

The hidden expenses were significant. Practices faced plenty of unexpected costs: training sessions, potential infrastructure upgrades, extended IT support, and countless hours spent on regulatory compliance documentation. The Data Protection Impact Assessment alone required significant clinical time to understand and localise Steve Durbin's comprehensive template to our specific practice context.

Barriers: The Gap Between Promise and Practice

The barriers we encountered fell into predictable categories, yet their impact was more profound than anticipated.

Regulatory compliance consumed enormous bandwidth. North Central London's Integrated Care Board's (NCL ICB) AI implementation guidance, while comprehensive, revealed just how unprepared our systems were for AI adoption. The DCB0160 clinical safety documentation process alone took six weeks to complete, requiring inputs from clinical safety officers, information governance teams, and technical specialists. It was obvious that many of them were learning the scope of regulation alongside us.

Staff resistance was more nuanced than expected. Younger clinicians embraced the technology readily, but our most experienced GPs remained sceptical. This was a clear exhaustion from decades of poorly implemented digital transformations.

A Practical Roadmap: Annotating the Theory with Reality

Having navigated NCL's guidance framework, I can offer practical annotations based on lived experience:

Step 1-3 (Planning and Risk Assessment): Triple your time estimates. What the guidance suggests takes two weeks actually required 4-6 weeks when accounting for stakeholder availability and the iterative nature of safety documentation.

Step 4-6 (Procurement and Legal): Engage your ICB support early. We waited four weeks for our initial safety consultation, delaying our entire timeline. The centralised CSO service is helpful but stretched thin.

Step 7-9 (Technical Implementation): Assume your EHR integration won't work. Develop plans for manual processes while technical issues are deployed and resolved.

Step 10-11 (Training and Go-Live): Staff training is ongoing, not a one-time event. Plan for refresher sessions and peer mentoring programs.

System-Level Recommendations: What Must Change

My experience has crystallised four critical system-level needs:

Centralised Clinical Safety Assurance: While the ICB's CSO service helps, every practice still needs to create extensive localised documentation. NHS England should provide pre-approved safety frameworks for common AI applications, dramatically reducing implementation overhead.

Proper Funding for Digital Transformation: The current expectation that practices self-fund AI implementation while managing day-to-day operational pressures is unrealistic. A dedicated transformation fund, ringfenced from revenue budgets, is essential.

True API Integration: The promised seamless EHR integration remains largely theoretical. NHS England must mandate open APIs and standardised integration protocols as a condition of EHR contracts.

Standardised Training and Support: Ad-hoc training approaches are inefficient and inconsistent. Regional training hubs should provide standardised AI literacy programs, with ongoing support built into the service model.

Early Outcomes: Mixed but Promising

After implementing Heidi, has AI reduced burnout and improved our practice? The answer is cautiously optimistic but nuanced.

The Implementation Guidelines suggested that expected benefits could include enhanced patient interaction through more eye contact and active listening, efficient note-taking that's detailed and accurate, and potential time savings. Some pilots reported saving 1-1.5 hours daily, which could be used to see more patients or reduce clinician stress.

The ICB documentation highlighted that AI scribes were "intended to be used by clinicians as a productivity and workflow efficiency tool to help alleviate the burden of clinical documentation." However, the reality of achieving these benefits depended heavily on successful implementation, staff buy-in, and managing the verification requirements that maintained clinical safety.

Patient feedback has generally been positive when properly informed about the AI assistance, though the DPIA rightly noted ongoing concerns about privacy and data use that need careful management.

What I'd Do Differently

Starting again, I would:

• •Negotiate a longer pilot period with staged implementation rather than practice-wide rollout
• •Invest earlier in staff engagement, including protected time for concerns and feedback sessions
• •Build stronger relationships with our ICB support teams before beginning, rather than during crisis moments
• •Set more realistic expectations about timeline and disruption—under-promise and over-deliver
• •Focus initially on specific use cases (routine follow-ups) before expanding to complex consultations

The Vision: Transformative but Conditional

The promise of AI in primary care remains compelling. If the systemic barriers were addressed, I can envision a transformed healthcare system where:

• •Clinicians practise at the top of their licence, freed from administrative burdens that contribute nothing to patient care
• •Diagnostic decision support helps catch conditions earlier, particularly in time-pressured 10/15-minute appointments
• •Population health insights emerge from improved data quality, enabling proactive rather than reactive care
• •Workforce sustainability improves as the administrative burden decreases and job satisfaction increases

But this vision requires coordinated action across multiple levels of the system.

Final Reflections: Innovation with Safety

The tension between innovation and safety in primary care is real and necessary. We cannot allow the urgency of workforce crisis to bypass proper safety frameworks, but neither can we let perfect be the enemy of good in addressing burnout and administrative burden.

My journey with AI implementation has reinforced my belief that technology is a tool—potentially powerful, but only when properly implemented with adequate support and within appropriate safety frameworks.

The current approach of expecting individual practices to navigate complex implementation processes alone is unsustainable. We need system-level coordination that matches the ambition of our digital transformation goals with the resources and support necessary to achieve them safely and effectively.

Recommendations for Key Stakeholders

For ICBs: Continue developing and expanding AI implementation teams with dedicated resources. The NCL ICB CSO service is a good model that needs more capacity. Create standardised implementation pathways with clear timelines and milestones, building on the templates already developed.

For NHS England: Move beyond guidance documents to operational support. Fund regional AI implementation hubs with technical, clinical, and regulatory expertise. Mandate EHR interoperability standards with enforced compliance.

For Technology Vendors: Stop overselling integration capabilities. Provide realistic implementation timelines and invest in proper NHS-specific integration testing. Partner with practices for genuinely collaborative development.

The future of AI in primary care will be determined not by the sophistication of the technology, but by our commitment to implementing it thoughtfully, safely, and with proper support for the clinicians who will use it daily.

Looking back over these five months, I remain cautiously optimistic. We've proven that AI can enhance primary care when properly implemented, but we've also identified the substantial systemic changes needed to realise its full potential. The question now is whether our healthcare system has the will and resources to address these challenges at scale.

The promise of AI in primary care remains, but it demands more than good intentions. It requires systemic transformation to match our technological ambitions.

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This concludes the 6-part series documenting the implementation of Heidi AI Scribe in NHS primary care. ← Part 5: The Roadmap to Go-Live | Return to Part 1 →