Health care offers tantalizing opportunities to disrupt with ML-powered products, but we fail routinely to realize full value. ML capabilities have the potential to advance populations health and clinical decision support, automation of operational tasks, actuarial risk assessments, and network optimization in value-based care. But, foundational gaps in skill mix on teams, data availability and sparsity, interoperability, scalability, interpretability, and bias have plagued current efforts. We have an opportunity to address these through increase development of end-to-end ML systems that address these gaps.
AGENDA:
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- ML opportunities and early proof points in health care
- Why do we routinely fail to realize value?
- Example and process for realized value