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Closing the Healthcare Data Gap with Better RWD Integration

Explore how fragmented health data impacts outcomes and how bridging these data gaps improves real-world evidence and value-based healthcare decisions.

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Closing the Healthcare Data Gap with Better RWD Integration

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  1. MIND THE DATA GAP AI models trained on U.S.-centric data often fail when applied to global populations, leading to poor generalizability

  2. Roughly 71% of AI healthcare datasets are skewed toward high-income countries

  3. Only 13% of FDA-approved AI medical devices explicitly report racial subgroup analysis

  4. Roughly 30–50% of AI diagnostic errors are linked to non-representative data inputs

  5. Over 60% of medical devices and diagnostics AI pilots in India are concentrated in top 8 cities

  6. Only 12% of AI health research includes data from public hospitals or rural health centers

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