TL;DR

Manchester University NHS Foundation Trust is piloting Microsoft Dragon Copilot with 150 doctors. The AI voice tool transcribes consultations and generates clinical notes, saving clinicians 3-5 minutes per patient. The trust estimates it could free up capacity to see a quarter of a million additional patients per year.

How the pilot works

Dr Charles Pearman, a cardiologist at the trust, uses Dragon Copilot to record patient consultations through a phone app. The system listens to conversations using ambient voice technology, then transcribes, organises, and adds the material directly to the patient record. Clinicians can later ask it to draft clinical notes or letters to GPs.

Before the pilot, Pearman would type notes during consultations and dictate correspondence afterwards. Now he faces patients directly throughout appointments. “It cuts out a lot of the work of making a first draft,” he said, adding that the tool produces output about 80% aligned with his preferences after customisation.

Scaling across the trust

Manchester University NHS Foundation Trust is the UK’s largest integrated trust, with 10 hospitals and more than 31,000 employees. Chief Executive Mark Cubbon said the trust would be expanding the use of ambient voice technology in coming weeks.

“The early indications are, with the adoption of this technology, we will be able to see and treat up to a quarter of a million new patients every single year,” Cubbon said. With 50 cardiologists on staff and waiting times stretching to 12 months, the potential throughput gains are significant.

Dr Henry Morriss, director of Clinical Informatics and an emergency department consultant, sees broader applications: paramedic handovers, multidisciplinary case discussions, and other conversations currently lost. “Clinical medicine is all about communication,” he said. “We use our voice.”

Looking forward

The trust plans to expand Dragon Copilot beyond the initial 150 doctors, supported by evaluation from Accenture. If the projected capacity gains hold at scale, the model could offer a template for other NHS trusts grappling with long waiting lists and administrative burden.