120 Artificial Intelligence (AI) Risk-Based Stratification for Surgical Waiting List Prioritisation: A Pilot Study Addressing the NHS Growing Waiting Times

Abstract

Abstract Aim 336,733 patients in England awaited NHS hospital treatment in May 2021. The Royal College of Surgeons of England’s New Deal for Surgery report encouraged utilising new technology in healthcare to address this. During the pandemic’s second wave, this pilot study investigated utilising COMPASS Surgical List Triage (COMPASS SLT), an artificial intelligence-based system, in assisting surgical decision-making on patient prioritisation. Data generated from COMPASS SLT was compared to data from the British Association of Endocrine and Thyroid Surgeons’ prioritisation guidance. Method A cohort of Thyroidectomy and Parathyroidectomy patients on the surgical waiting list at Imperial College Healthcare NHS Trust was used. COMPASS SLT calculated individuals’ mortality and significant morbidity risk (risk >2.5%). Additional increase in mortality and morbidity due to treatment delay were calculated. Actual treatment time was aligned to the treatment delay (in weeks) experienced by each patient. Results 29 patients, with a median age and waiting time at the onset of the second wave of 43 years and 18 weeks respectively, were enrolled. Non-statistically significant differences (p=0.937) between the FSSA and BAETS classification existed. However, cohort size could promote a type II error. There was an increase in mean mortality and morbidity risk (p<0.001) arising from treatment delay, and decisions based on the FSSA and BAETS classifications. Conclusions COMPASS SLT can supplement clinical decision making. The FSSA and BAETS guidance lacks precision and responsiveness to solely inform patient prioritisation. An AI tool provides clinicians objectivity and flexibility in prioritising patients, with information on individual morbidity, mortality, and treatment delay costs.