Komodo Health and the National Committee for Quality Assurance (NCQA) have launched a multiyear collaboration to accelerate the development, validation and refinement of healthcare quality measures. The partnership will combine NCQA's expertise in quality standards with Komodo's large-scale healthcare data and AI analytics capabilities, aiming to shorten the time required to evaluate new measures while maintaining transparency and scientific rigor.
NCQA will use Komodo's Healthcare Map, which includes more than 330 million de-identified patient journeys, to examine patterns of care across different insurance types, clinical settings and patient populations. The organization will also use Marmot, Komodo's healthcare-focused AI analytics platform, to analyze real-world evidence and generate reproducible findings. By bringing these capabilities together, the organizations aim to reduce the manual work involved in assembling and analyzing fragmented healthcare datasets.
The collaboration will support the continued development of HEDIS, the widely used measurement framework that enables commercial, Medicaid and Medicare health plans to evaluate healthcare quality and identify opportunities for improvement. More timely access to longitudinal data could allow NCQA to test and refine measures faster as clinical evidence, treatment patterns and standards of care evolve.
Komodo and NCQA have already applied the approach to a new HEDIS measure evaluating whether patients receive a timely colonoscopy after a positive non-invasive colorectal cancer screening test. While quantitative testing of new measures has traditionally taken two to three months, the organizations completed testing across Medicare Advantage, Medicaid and commercial populations in approximately one month. The measure is expected to be implemented in 2027.
The partnership reflects a broader shift toward more continuous and data-driven healthcare quality measurement. By combining longitudinal real-world data with AI-enabled analytics, Komodo and NCQA aim to make quality measures more responsive to changes in clinical practice while ensuring that results remain transparent, reproducible and actionable for healthcare organizations.
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