This article refers to'A machine learning derived echocardiographic algorithm identifies people at risk of heart failure with distinct cardiac structure, function, and response to spironolactone: Findings from the HOMAGE trial' by M. Kobayashi et al., published in this issue on pages 1284-1289.The management of heart failure (HF) has increasingly improved these recent years. 1 New therapeutic options have emerged. Nevertheless, the prescription of these treatments is not systematic and perhaps should not be systematic but adapted to individual specificities. 2 In these past years, the relevance of the 'heart team decision-making process' has been emphasized and even recommended by guidelines. 1,3 Heart teams are gathering colleagues, experts, that are supposed to decide based on the clinical files of a patient what should be the optimal management. It is much more efficient than a decision-making process based on a single view. However, it could be biased by many factors or parameters considered by the heart team or not. The decisions made are most often appropriate but the same patient discussed in different heart teams or at different heart team meetings may end up with different medical managements.Therefore, risk stratification and management of complex diseases like HF could take advantage of new approaches. Recently, a 'decision support tool' has been demonstrated to improve the appropriateness of mineralocorticoid receptor antagonist (MRA) prescription. 4 An automated, patient-specific, electronic health record-embedded alert was found to increase MRA prescription. It has been validated in a study including 2211 patients using a cluster randomized methodology. 4