C2-Ai PTL System | Risk Stratified Patient Tracking List
Fast, evidence -based triage of surgical waiting lists, ensuring patients are risk stratified on their individual clinical need. Our approach (as featured on BBC) prioritises patients based on quantifiable risk and likelihood of deterioration whilst on the waiting list.
“Helping put the right patient, in the right environment, with the right team, at the right time”. Mr Pritchard-Jones: MD NHS Trust
Delivering more effective/objective prioritisation and validation of waiting lists. Our system uniquely takes into account wait list deterioration
C2-Ai’s Patient Tracking List Triage system has proven that it is possible to meet the requirements of the NHS Charter and the commitment to Equality, Diversity and Inclusivity by ensuring that patients are risk stratified on their individual clinical need.
Enabling clinicians to rapidly assess patients against their risk of mortality and complications using proven methodologies and importantly, including calculation of the impact of deterioration caused by the patient’s time on the waiting list.
This means clinicians can quickly position them more accurately onto the patient treatment list.
C2-AI systems have been in use by the NHS and globally for 12 years, building on 30 years of research and the World’s largest referential patient dataset (approaching 200m records from 46 countries). Our approach (as featured on BBC in September 2020) prioritises patients based on quantifiable risk and likelihood of deterioration whilst on the waiting list.
The system integrates into existing pathway management tools, triage hundreds of thousands of patients a day, processing and reprocessing the waiting list dynamically at scale to deliver:
Faster clearing of the backlog
Lower patient harm and mortality
Better use of surgeon time (currently 15 minutes spent for every patient review)
Detailed view of clinical risk for each patient
Optimisation of sites to and routing across trusts/regions to match patient risk
Helps identify which patient to optimise prior to operation