Artificial intelligence is only as good as the data behind it. In healthcare, even small differences in inputs can lead to vastly different outcomes, making data quality and provenance critical to evaluating AI models.
This whitepaper introduces the Milliman MedInsight Data Confidence Model, a structured approach built on 25 years of experience matching the right data to the right model. Authors Jeff McGinn and Robert Richards explore the differences between open, closed, and paid claims; how factors like care-pathway completeness, adjudicated financials, and enrollment data affect analytic power; and why reference data can significantly improve model accuracy.
