Trust, Compliance, Security: The Real Test for AI in Medical Documentation
Across healthcare, one challenge rises above the rest: time. Clinicians don’t have enough of it – and documentation is a major reason why.
AI-powered tools are stepping in to help, particularly in the high-friction space of medical note-taking. But for every promising advance, there’s a pressing question: how can we trust AI with sensitive patient records?
That question isn’t just technical. It’s strategic. Security, compliance, and clinician confidence will define whether AI succeeds in supporting care – or stalls at the pilot stage.
AI scribes can save clinicians hours every day, reduce burnout, and help deliver higher-quality documentation.
These tools are no longer speculative; they’re being used in clinics and hospitals across Europe today. But decision-makers know that benefits on paper are meaningless without guarantees around data protection and operational fit.
To earn trust, AI must prove it can do more than automate – it must secure, support, and scale safely.
A Practical Approach to Security
AI can ease the admin load – but only if it handles patient data responsibly. While end-to-end encryption often grabs headlines, it isn’t always practical in healthcare, where systems need to process information in real time and integrate directly with electronic health records.
Most clinical-grade tools – such as Tandem – take a more appropriate approach: encrypting data in transit and at rest.
This ensures information is protected while moving and while stored, with secure, temporary access during processing. It’s a model that balances performance with protection and meets the standards that matter – GDPR, ISO 27001, and the NHS Data Security and Protection Toolkit among them.
The result is clear: data remains secure, clinicians stay in control, and the technology works in the real world – not just on paper.
Trust is Built Through Transparency
Strong compliance is essential, but not enough on its own. AI needs to work with clinicians, not around them.
That means privacy-by-design frameworks, where data isn’t retained without consent and clinicians review, approve, and own the notes that are created.
This level of transparency helps counter a major barrier to adoption: a lack of confidence in how AI decisions are made.
When clinicians can see, shape, and override outputs, they’re far more likely to embrace the tech – not resist it.
There’s also a patient safety benefit. Some AI scribes now include real-time anomaly detection, flagging incomplete or inconsistent notes before they reach the patient record.
That supports quality improvement efforts and aligns with initiatives like Getting It Right First Time (GIRFT), which stress the importance of accurate, standardised documentation.
What Leaders Need to Ask
One of the most overlooked success factors in AI implementation is integration. If a solution doesn’t fit seamlessly into existing workflows, it creates more friction than it solves.
The most effective tools plug directly into electronic health records, generate usable notes instantly, and support downstream tasks like referral letters – all without disrupting care.
In a pressured system, tools that reduce complexity will always outperform those that add it.
Healthcare leaders don’t need to be AI experts, but they do need to be clear on the fundamentals. A credible solution is secure by design, meets NHS and regulatory standards, keeps clinicians in control of outputs, and integrates smoothly without introducing inefficiencies.
AI isn’t just a digital upgrade – it’s a trust issue. And trust, once earned, becomes a powerful driver of transformation.
By Dr Katie Baker, Director of UK and Ireland at Tandem Health
