A practical approach for defining outcomes and thresholds for predictive healthcare algorithm development using real-world dataOut

Eric NormanLEAPS, Whitepapers

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As the healthcare system evolves towards value-based care, predictive algorithms can play a critical role, but their findings must be perceived as meaningful, substantial, and actionable by those outside the data science community.

A practical, multi-stakeholder, fit-for-purpose metric identification process that is applicable to real-world evidence (RWE) can be executed in only a few months, as was demonstrated by the NEWDIGS LEAPS Project with the development of the METRICS process.

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