September 4, 2020
Donald Rucker, MD
National Coordinator for Health Information Technology
Department of Health & Human Services
Dear Dr. Rucker:
Thank you for the opportunity to comment on strategies to improve patient identity matching, which is a critical component to interoperability and our ability to measure quality and attribute patients to the correct clinicians and other providers.
Patient matching today is a cumbersome effort to align names that can vary for the same person over time or be the same for different individuals, birth dates, addresses, etc., and is fraught with potential for errors.
Improving patient matching is essential for patient safety, public health, and quality reporting for improvement and value-based payment models. All of these critical uses of clinical information depend on accurate patient identification and attribution.
Patient matching also is crucial for efforts by the National Committee for Quality Assurance (NCQA) and others to move to digital quality measures (dQM). Unlike traditional measures or electronic clinical quality measures (eCQMs), dQMs obtain needed data from an array of electronic sources, including electronic health records (EHRs), health information exchanges (HIEs), registries, claims, etc.
Because dQMs utilize so many different data sources, the potential for patient mismatching becomes even more problematic. Improved patient matching technologies is thus essential for the move to dQMs and better enable the ability to measure and improve healthcare quality.
dQMs allow us to measure more of what matters with the rich data in the disparate electronic data sources. For example, dQMs can target measurement to patients’ own individual risks rather than what is best for average patients, which requires ensuring the identity of the right individual patient. dQMs greatly reduce reporting burdens, and can improve accuracy of results because we can verify the data at hubs like HIEs to a degree not practical for clinician offices. dQMs also align with Health & Human Services’ (HHS) National Health Quality Roadmap which prioritizes obtaining data for quality measurement from information produced as part of typical clinical workflows collected electronically.
Establishing unique patient identifiers (UPI) is the most efficient and effective way to ensure optimal patient matching. Previous opposition to UPIs has substantially waned, as reflected in a July House of Representatives approval by voice vote of a bipartisan amendment removing the ban on federal funding for UPIs.
We therefore urge you to work with Congress and other stakeholders to obtain Senate approval for removing the federal funding ban as the best way to improve patient matching, support dQMs, and advance the National Health Quality Roadmap.
Margaret E. O’Kane