Why Doesn’t Healthcare Trust Its Own Data?
Health care depends on data for decisions that affect care, cost and innovation. Learn why fragmented, incomplete information creates a trust deficit, and what leaders can do to build confidence in the data powering quality measurement, value-based care and AI.
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About The Guests
Burke Burnett leads market engagement and growth work at NCQA. His experience spans partnership strategy, digital quality solutions and technology products supporting quality, risk adjustment and value-based care.
Vik Wadhwani leads NCQA product development and transformation initiatives. He brings more than 25 years of leadership experience in business and technology transformation, including work across payer, provider, health care technology and value-based care organizations.
Dr. David Kendrick is a physician, engineer and clinical informaticist who chairs the Department of Medical Informatics at the University of Oklahoma School of Community Medicine. He is founder of MyHealth Access Network and previously served as Senior Counsel for Interoperability to the National Coordinator for Health IT. His work focuses on health information exchange, analytics, interoperability and the use of trusted data to improve care and quality measurement.
Episode Description
In the premiere of Data Quality Perspectives, host Burke Burnett explores a fundamental question: Why doesn’t healthcare trust its own data? He is joined by NCQA Chief Product and Transformation Officer Vik Wadhwani and physician, engineer and clinical informaticist Dr. David Kendrick.
Together, they examine how health care’s dependence on data has outgrown trust models built for a simpler era. Data now moves across organizations, vendors, platforms and use cases, while decisions tied to prior authorization, care coordination, treatment, payment and organizational performance carry higher stakes.
The conversation also explores why data quality is no longer only a reporting or compliance concern. Wadhwani explains how incomplete or poorly governed data can limit AI performance and automated decision-making. Kendrick examines completeness as a persistent blind spot when a patient’s information is distributed across many care settings.
Listeners will learn why data quality must become a continuous, enterprise-wide capability. The discussion offers practical questions leaders can ask about data sources, gaps, fitness for purpose, authorization and lineage before relying on information for strategic, operational or clinical decisions.
Timestamps
(00:00) Introducing Data Quality Perspectives
(00:46) Why Doesn’t Health Care Trust Its Own Data?
(01:20) A More Complex Data Ecosystem
(03:12) Why Higher Stakes Require Greater Trust
(04:14) AI Raises the Urgency of Data Quality
(06:18) From Compliance Issue to Strategic Business Issue
(07:11) Completeness as Health Care’s Biggest Blind Spot
(09:14) The Operational, Clinical and Financial Costs of Untrusted Data
(10:12) Why Continuous Data Quality Monitoring Matters
(10:59) Questions Health Care Leaders Should Ask
(11:53) Building Enterprise-Wide Ownership
(12:51) Key Takeaways and What Comes Next
