Overview. C2-Ai’s patient prioritisation system has been shown through NHS reports to reduce emergency admissions by 8 percent, saving 125 bed-days per 1,000 patients and potentially thousands of years of surgeon time.
Why? As well as saving staff time and reducing pressures, the system helps the NHS to avoid selection bias through its algorithm; it incorporates the impacts of the social determinants of health on clinical need, such as the number and severity of comorbidities. In addition, risk-adjusted outcomes are analysed post operatively to ensure that vulnerable groups are not being disadvantaged.
What happened? The system builds on 30 years of research and 400m episodes of care, processed from 46 countries. It uses historic data to consider many different permutations to derive a patient’s medical conditions. These are combined with the prospective operation to derive a priority score – built on detailed mortality/complication risks for the patient today, and if the procedure is delayed. As well as the 8 percent reduction in emergency admissions, data shows 27 percent reduction in patients waiting more than 52 weeks, reduced deterioration in patients which would increase length of stay and cost, financial savings, positive staff response and more.
Looking ahead. In the initial and expanded deployment, a Clinical Senate was created to share learning and case studies from the initial project to facilitate rollout across all the acute trusts (funded by NHS England), including development of an ‘operational playbook’ to share real world evidence of benefits achieved across the region. The findings have been shared by regional AHSNs via conferences, webinars and blogs across the NHS. The approach was also included in the GIRFT best practice guide as an exemplar.
https://www.integratedhlth.co.uk/integrated-health-awards-2023-best-elective-care-recovery-initiative/
