‘ALERT’, a machine learning predictive model to identify six-month mortality in patients with advanced cancer: Perception of the utility and acceptability among patients.

Abstract

189 Background: Machine learning (ML) models can predict mortality and guide end-of-life (EOL) care for patients with advanced solid cancers. Early advanced care planning (ACP) leads to more goal-concordant care, yet many patients, especially from minoritized groups, do not engage in serious illness conversations (SIC). Reasons for delay include inability of oncologists to identify patients at high short-term mortality risk, and uncertainties about prognosis. While ML may improve prognosis accuracy, research on its use and impact on racial biases affecting SIC is limited. This study aims to evaluate the acceptability of deploying a ML model to predict 6-month mortality among patients with advanced solid cancers. Methods: In order to inform the implementation of ‘ALERT,’ a predictive mortality model into a clinical alert system, we conducted one-on-one semi-structured interviews with patients with advanced solid cancer receiving care at a single institution (N=26). Interviews were recorded, transcribed verbatim, coded, and themes were reviewed by a team of interdisciplinary investigators. Results: Benefits and challenges to guide the future implementation of ALERT were identified. Patient-perceived benefits were: 1) EOL Planning: preparing family members (e.g., financial, emotional); 2) Emotional Freedom: knowledge would give freedom to live out life more carefree due to limited time left; 3) Medical Autonomy: the treatment could be worse than the actual disease, if at any point it was not helping, knowing allows individuals to make the decision to stop. Challenges identified were: 1) Loss of Control: “when you don’t know [the outcome], you just keep moving, you just keep pushing”- knowing can result in a loss of control patients may desire for their course of treatment 2) Emotional Impact: knowledge of mortality could lead to grief among patients and families leading to distress around acceptability; 3) Faith: mortality prediction can be seen “playing God,” many patients believed when their time comes, it will happen and a ML tool cannot accurately predict when that time will be. Conclusions: Patients expressed equal balance of acceptability and resistance of ALERT for themselves and their families. This resulted in the difference of preferences regarding whether or not the knowledge this tool could provide would be helpful. This could lead to clinicians taking a patient-centered approach where the delivery of results using ALERT can be based on patient preference. The findings underscore the complexity of implementing ALERT, balancing significant benefits with notable challenges. These insights will guide the refinement of ALERT, ensuring it supports patient care while ensuring ALERT’s benefits are maximized and challenges are overcome.