NHS adopts AI to combat absenteeism and expedite elective care waiting times
The National Health Service (NHS) is on the brink of an innovative leap, deploying artificial intelligence (AI) across an additional ten trusts with the aim of curtailing missed appointments and thus liberating valuable staff hours. This strategic move is anticipated to significantly dent the backlog in elective care waiting lists.
This initiative’s expansion comes in the wake of a triumphant pilot programme at the Mid and South Essex NHS Foundation Trust. This particular programme witnessed a near one-third reduction in patient no-shows within a mere six-month span.
The pioneering software, a collaborative creation between Deep Medical and contributions from both a frontline worker and an NHS clinical fellow, utilises algorithms alongside anonymised patient data to foresee potential appointment absences. It ingeniously taps into various external factors, such as weather conditions, traffic situations, and employment statuses, to deduce possible reasons behind a patient’s failure to attend. To counteract these challenges, it proposes alternative booking options that align more closely with the patient’s availability, including after-hours and weekend slots for those unable to take daytime leave.
Moreover, the software cleverly incorporates a system of intelligent backup bookings, ensuring that no clinical time goes to waste and optimising overall operational efficiency.
The six-month trial at the Mid and South Essex NHS Foundation Trust produced remarkable results: a 30% decrease in no-shows, 377 prevented missed appointments, and an additional 1,910 patients seen. Given these outcomes, projections suggest the trust could save approximately £27.5 million annually by persisting with this programme, benefiting a population of 1.2 million.
Deep Medical, under the co-founder duo Dr Benyamin Deldar and AI connoisseur David Hanbury, is at the forefront of this technological advancement. Dr Deldar highlights the software’s dual benefit: drastically reducing missed appointments and repurposing these slots for other patients, thereby enhancing both financial savings and public healthcare delivery.
The imminent roll-out to an additional ten trusts across England marks a significant step forward in this AI-powered journey.
In a concerted effort to recuperate elective care services post-pandemic and address prolonged waits for routine procedures, the NHS is embracing cutting-edge technologies and innovations, including AI. This approach aims to tackle the prevalent issue of missed hospital appointments, which amounts to hundreds of thousands each month, thus ensuring more judicious use of clinical time and expedited access to care for waiting list patients.
Annual statistics reveal a startling 6.4% no-show rate among the 124.5 million outpatient appointments across NHS England, translating into a financial strain of £1.2 billion.
The analysis also uncovers that physiotherapy appointments bear the brunt of absenteeism, with an 11% no-show rate, followed closely by cardiology, ophthalmology, and trauma and orthopaedics.
Dr Vin Diwakar, NHS England’s National Director for Transformation, praises the NHS’s innovative spirit and its openness to novel operational methods that ensure timely patient care. He underscores the AI initiative’s capacity to not only refine patient services but also to foster a more economical utilisation of taxpayer funds.
Moreover, he emphasises the role of such AI pilots in empowering patients to manage their healthcare more effectively and in addressing health inequalities.
The University Hospitals Coventry and Warwickshire (UHCW) NHS Trust exemplifies another facet of AI application through ‘process mining’. This technique offers insights into the efficacy of existing processes, spotlighting bottlenecks and areas ripe for improvement.
Notably, during its pilot, the Trust identified a correlation between high deprivation scores and increased DNAs, with a marked surge in last-minute cancellations post two SMS reminders. Adapting their strategy to send reminders 14 days and then four days prior to an appointment significantly reduced no-show rates from 10% to 4% among a targeted patient group.
Encouraged by these results, the Trust is now exploring the application of process mining to theatre scheduling, aiming for further efficiency gains and enhancements