AI software that aims to reduce missed appointments is being rolled out to 10 NHS trusts following a ‘successful’ pilot.
The tool predicts likely missed appointments through algorithms and anonymised data and details the reasons why this might be the case using external insights, such as the weather, traffic or jobs. Appointments can then be rearranged for better times, for example it will give evening or weekend slots to those less able to take time off in the day. It also offers ‘back-up’ bookings to maximise efficiency.
In a six-month pilot of the technology at Mid and South Essex Foundation Trust, there was a 30% drop in non-attendance, with 377 DNAs prevented and an additional 1,910 patients seen.
NHS England said this tool, developed by Deep Medical, has the potential to save £27.5m a year for a trust of 1.2 million people and can help to bring down long waiting lists.
Dr Vin Diwakar, national director for transformation at NHS England, said: ‘Not only can these technologies help to free up doctors’ time to treat more patients and reduce waiting times for planned care, it means a significant amount of money can be invested in frontline care rather than lost to missed appointments.
‘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.’
Charlotte Williams, chief strategy and improvement officer at Mid and South Essex NHS Foundation Trust, said: ‘Embracing new technologies is something the Trust is passionate about, it also supports better access for people who are disabled and for working women, as a working mum I know how sometimes it can be hard to juggle work and childcare as well as managing your own health needs.’
Eight million outpatient appointments were not attended across NHS England in 2022/23, which equates to 6.4%, according to NHS figures. The highest proportion of missed appointments were physiotherapy (11%), cardiology (8.9%) and ophthalmology (8.8%).
