A person’s health is determined by a variety of factors beyond those captured by electronic health records or the genome. Many healthcare organizations recognize the importance of the social determinants of health (SDH) such as socioeconomic status, employment, food security, education, and community cohesion. Capturing such comprehensive portraits of patient data is necessary to transform a healthcare system and improve population health while simultaneously delivering personalized healthcare provisions. Machine learning (ML) is well-positioned to transform system-level healthcare through the design of intelligent algorithms that incorporate SDH into clinical and policy interventions, such as population health programs and clinical decision support systems. Innovations in health-tech through wearable devices and mobile health, among others, provide rich sources of data, including those characterizing SDH. The guiding metric of success should be health outcomes: the improvement of health and care at both the individual and population levels. This workshop will identify the needs of system-level healthcare transformation that ML may satisfy. We will bring together ML researchers, health policy practitioners, clinical organization experts, and individuals from all areas of clinic-, hospital-, and community-based healthcare.
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