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Appl Clin Inform. 2018 Apr;9(2):422-431. doi: 10.1055/s-0038-1656548. Epub 2018 Jun 13.

Using Clinical Data Standards to Measure Quality: A New Approach.

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Diameter Health, Inc., Farmington, Connecticut, United States.
Boston University Metropolitan College, Boston University, Boston, Massachusetts, United States.
Kansas Health Information Network, Topeka, Kansas, United States.
School of Biomedical Informatics, University of Texas-Memorial Hermann Center for Healthcare Quality and Safety, University of Texas Health Science Center, Houston, Texas, United States.
Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States.
Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.



Value-based payment for care requires the consistent, objective calculation of care quality. Previous initiatives to calculate ambulatory quality measures have relied on billing data or individual electronic health records (EHRs) to calculate and report performance. New methods for quality measure calculation promoted by federal regulations allow qualified clinical data registries to report quality outcomes based on data aggregated across facilities and EHRs using interoperability standards.


This research evaluates the use of clinical document interchange standards as the basis for quality measurement.


Using data on 1,100 patients from 11 ambulatory care facilities and 5 different EHRs, challenges to quality measurement are identified and addressed for 17 certified quality measures.


Iterative solutions were identified for 14 measures that improved patient inclusion and measure calculation accuracy. Findings validate this approach to improving measure accuracy while maintaining measure certification.


Organizations that report care quality should be aware of how identified issues affect quality measure selection and calculation. Quality measure authors should consider increasing real-world validation and the consistency of measure logic in respect to issues identified in this research.

Conflict of interest statement

John D'Amore, Chun Li, and Jonathan Niloff receive salaries from and have an equity interest in Diameter Health, Inc., whose software provided the quality measure calculation used in this research. Dean Sittig serves as a scientific advisor with an equity interest in Diameter Health.

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