Machine learning is both a powerful tool and a source of complexity in clinical care. Understanding how risk stratification tools will be used by clinicians is crucial for developing effective machine learning methods. The context in which these scores are applied—whether as alerts, passive tools, or reference points—shapes their design and implementation in healthcare settings.
