Cheshire and Merseyside Integrated Care System (ICS) has signed a new agreement with tech provider C2-Ai, to expand its waiting list initiative across all of its nine acute trusts in the region.
The AI-backed decision support model was initially deployed at St Helens and Knowsley Teaching Hospitals NHS Trust in 2020, to help find, prioritise and support some of the highest-risk patients on waiting lists. The C2-Ai technology is capable of flagging up patients who are at risk of deterioration, and then suggest specific actions clinicians could take according to parameters they set.
The technology marks a significant step change in the way patients on waiting lists are prioritised, giving healthcare professionals valuable intelligence that is improving patient outcomes, reducing A&E admissions, saving time, and reducing the length of the average hospital stay.
Professor Rowan Pritchard-Jones, medical director for NHS Cheshire and Merseyside, said: “Our use of AI as a tool to help prevent harm, has the potential to enable some of the biggest changes in 75 years around how waiting lists are safely managed in the NHS.
“Delivering successful healthcare at a time when resources are in high demand, means finding those in greatest need, and creating genuine impact for them. That’s what some of our pioneering teams have already been doing with the help of technology, and we will be working with many others to ensure they have an opportunity to embrace tools now available.”
Regional expansion
The technology is now set to be significantly expanded across the Cheshire and Merseyside region, as it is made available to nine acute trusts: Countess of Chester Hospital NHS Foundation Trust; East Cheshire NHS Trust; Mid Cheshire Hospitals NHS Foundation Trust; Liverpool Heart and Chest Hospital NHS Foundation Trust; Liverpool University Hospitals NHS Foundation Trust; Liverpool Women’s Hospital NHS Foundation Trust; Mersey and West Lancashire Teaching Hospitals NHS Trust; Warrington and Halton Teaching Hospitals NHS Foundation Trust and Wirral University Teaching Hospital NHS Foundation Trust.
The system works by taking information relating to patients across multiple care settings and calculating their individual clinical risks as information changes. Surgical teams are presented with suggested priority scoring, to deliver insight on where best to treat them in the region. It can also indicate required prehabilitation support for patients to prevent deterioration as they wait.
Dr Mark Ratnarajah, UK managing director for C2-Ai, said: “This means that patients on waiting lists can be managed based on a detailed and near real-time understanding of their changing clinical needs, impacts of social determinants of health and risks from decompensation either from their underlying condition and or their multimorbidity, rather than basing decisions only on time on waiting list and type of procedure.”
Early adopters
The ‘waiting well’ pre-habilitation programme produced excellent results within the early adopter hospitals. Surgeons were able to use the technology to quickly identify who their highest urgency patients were and use the clinical decision support to act and safely address backlogs in waiting lists.
An NHS England assessment of the 125,000 patients who were first managed through the system in Cheshire and Merseyside revealed a two-thirds reduction in the need for ICU for the highest risk patients. In addition, 125 bed days were saved for every 1,000 patients on the waiting list, there was an 8% reduction in emergency admissions, as well as notable reductions in avoidable harm.
The clinically-led AI model has been the subject of Royal College of Surgeons funded health economic analysis and looked to as an example of best practice internationally at the World Congress of Prehabilitiation this summer.
Across the country, other NHS trusts are now starting to replicate the work undertaken at Cheshire and Merseyside, with regional support from a number of organisations including the Innovation Agency and wider Academic Health Science Networks.