8 months ago
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The expansion to ten more NHS trusts follows a successful pilot in Mid and South Essex NHS Foundation Trust, which has seen the number of did not attends (DNAs) slashed by almost a third in six months.
Created by Deep Medical and co-designed by a frontline worker and NHS clinical fellow, the software predicts likely missed appointments through algorithms and anonymised data, breaking down the reasons why someone may not attend an appointment using a range of external insights including the weather, traffic, and jobs, and offers back-up bookings.
The appointments are then arranged for the most convenient time for patients – for example, it will give evening and weekend slots to those less able to take time off during the day.
The system also implements intelligent back-up bookings to ensure no clinical time is lost while maximising efficiency.
It has been piloted for six months at Mid and South Essex NHS Foundation Trust, leading to a 30% fall in non-attendances. A total of 377 DNAs were prevented during the pilot period and an additional 1,910 patients were seen. It is estimated the trust, which supports a population of 1.2 million people, could save £27.5 million a year by continuing with the programme.
Deep Medical was co-founded by Dr Benyamin Deldar and AI expert David Hanbury. Dr Deldar said: “We’ve already seen how the AI software has helped reduce missed appointments by 30% and gets other patients into the remaining 70% of missed appointments.
“This means, over time, Deep Medical will allow more and more appointments to be utilised; saving money and providing vital care to the public.”
The AI software is now being rolled out to ten more trusts across England in the coming months.
Focus on elective recovery
As part of a focus to recover elective care following the pandemic and bring down long waits for routine care, the NHS is embracing new technology and innovations like AI to reduce hundreds of thousands of missed hospital appointments every month, ensuring that clinical time is used effectively and meaning patients on the waiting list can be seen more quickly.
Published data shows that of 124.5 million outpatient appointments across the NHS in England last year, eight million (6.4%) were not attended by the patient. It is estimated this level of missed appointments has an annual cost to the NHS of £1.2 billion.
Figures for last year also show the highest proportion of missed appointments were physiotherapy – with more than one in 10 appointments marked as DNAs (11%) – followed by cardiology (8.9%), ophthalmology (8.8%), and trauma and orthopaedics (7.9%).
Dr Vin Diwakar, national director for transformation at NHS England, said: “The NHS has long been a pioneer of innovation, embracing new ways of working so patients get the help they need in a timely way, and the use of AI to help reduce the number of missed appointments is another example of how new technologies are helping to improve care for patients, and ensuring the health service is making the best and most efficient use of taxpayers’ money.
“And the work being done across the country through these AI pilots shows that initiatives like this can deliver results in a short period of time, while also supporting patients to take control over their own care and help to better understand and reduce health inequalities.”
Different approaches being piloted
At University Hospitals Coventry and Warwickshire (UHCW) NHS Trust they have been using AI to help improve patient care and pathways through “process mining”, which helps them see how well their processes are working, revealing bottlenecks and other areas of improvement.
Process mining also allows the Trust to look at a cohort of patients who may be being treated by several specialities and whether their appointments can be grouped together at the same time.
During the pilot, they used AI to look at DNAs – which are more common among those with high deprivation scores – and identified a spike in last-minute cancellations after two SMS reminders had been sent. Through this work, they found messaging patients 14 days before an appointment and a follow-up four days before was most effective, as it meant they could cancel earlier and re-book the appointment in plenty of time.
As a result, the trust saw their DNAs in this subset of patients drop from 10% to 4%, and they are now looking at expanding process mining to theatres to see where they can make efficiencies and improvements there.
