Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6-month mortality and improve end-of-life outcomes for advanced cancer patients

Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6-month mortality and improve end-of-life outcomes for advanced cancer patients
Cancer Medicine; Nithya Krishnamurthy, Melanie Besculides, Ksenia Gorbenko, Melissa Mazor, Marsha Augustin, Jose Morillo, Marcos Vargas, Cardinale B. Smith; 3/25
There are significant disparities in outcomes at the end-of-life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions of the utility and challenges of implementing a machine learning [ML] model (ALERT) to predict 6-month mortality among patients with advanced solid cancers to prompt timely SIC. Our study found that clinicians expressed widespread acceptability of ALERT and identified clear benefits, particularly in triggering earlier SIC and standardizing prognosis discussions across care teams. [Additionally,] a recent study found that ML prognostic models decreased use of aggressive chemotherapy at EOL and increased SIC frequency fourfold.