From manual clinical letter checks to AI-assisted review - helping Northern Ireland’s largest private healthcare provider review letters at scale

The problem:

Kingsbridge Healthcare Group was experiencing growing demand across its services.

As patient volumes increased, so too did the volume of clinical correspondence that needed to be reviewed, checked and sent. Every letter had to be accurate, complete and appropriate before reaching a patient.

In a healthcare environment, that matters. A misspelled name, missing enclosure, misheard clinical term or incorrect follow-up action can create confusion, delay care or increase risk. It can also slow down how quickly patients receive the information they need after an appointment, procedure or consultation.

The existing process was heavily manual, but safe and thorough. Experienced admin teams reviewed each letter before it was issued, checking for errors, inconsistencies and missing information.

As volumes increased, that process became harder to scale.

Increasing team capacity was one option, but it brought cost, training and resourcing challenges, especially when much of the knowledge needed to spot issues sat with experienced staff.

Kingsbridge wanted to step back and ask whether there was a better way.

The question was whether AI could help review clinical correspondence more efficiently, reduce pressure on the team, improve turnaround times for patients and give management clearer insight into where issues were occurring, without removing the human judgement that a regulated healthcare environment demands.

The opportunity was not to replace the people in the process. It was to help them focus their time where it mattered most.

What we built:

We started with a pilot - a deliberate choice in a sector where accuracy, safety and human judgement matter.

Rather than trying to fully automate clinical correspondence from day one, we worked with Kingsbridge to design and build an AI-assisted clinical letter review tool that could be tested safely in a real world environment.

The goal was not to remove people from the process. It was to help experienced teams review patient correspondence more efficiently, with better visibility and clearer prioritisation.

The tool reviews clinical letters before they are issued, automatically checking for quality, accuracy and completeness - detecting spelling errors, missing words, grammar problems, incorrect patient details, missing enclosures and clinical content that may not align with the supporting patient record.

Rather than a simple pass or fail, each letter receives a quality score that reflects the nature and seriousness of any issues found. Letters with no issues can progress more quickly, while those requiring attention are flagged for review, showing reviewers exactly what has been identified and helping them focus their time where it matters most.

Information security was central to the design. Clinical correspondence contains sensitive patient information, so Kingsbridge needed confidence that data would remain secure and under its control. We used Microsoft Azure AI Foundry to give Kingsbridge control over the choice of models, the AI environment and the way data was processed. The solution was designed so that no data left Kingsbridge’s existing secure cloud environment.

To support safe use in a regulated healthcare environment, automated evaluation is built in to monitor the accuracy and completeness of the AI’s output. Performance is benchmarked against a defined set of correspondence, including after any prompt or model update, providing ongoing confidence in performance and an early warning if accuracy changes over time.

Why it worked:

It worked because the ambition was right sized from the start.

Introducing AI into clinical correspondence without a period of structured learning - in an environment where errors have real consequences for patients - would have been the wrong move. Starting with a pilot meant the tool could prove its value on live data, build confidence and surface real insight before being introduced more widely into the live process.

The decision to augment rather than fully automate was equally important. In a regulated healthcare environment, the safest answer is not always full automation. Keeping human reviewers in the process and using AI to support their judgement rather than replace it was not a compromise - it was the point.

Instead of every letter being treated in the same way, the tool created a clearer view of what needed attention and why. Letters with no significant issues could be identified more quickly, while letters needing experienced review were flagged with the context reviewers needed to act on.

The pilot gave Kingsbridge the confidence to move from testing to live use. The AI letter validator is now fully operational, automatically marking letters as ready to send when they pass its quality, accuracy and completeness checks without any issues being flagged for review. This is reducing the team’s manual review workload by up to 30%, easing pressure on the team, shortening turnaround times and helping patients receive correspondence sooner, while allowing experienced staff to focus their attention on the letters that genuinely need their attention.

And for the first time, management has clear visibility into where issues are occurring - making it possible to spot patterns, address root causes and improve the process further upstream, rather than managing issues letter by letter.

The result is a safer, more scalable clinical correspondence process: one shaped by evidence, grounded in real clinical correspondence and built around the judgement of experienced people.