Background 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. Objective This research evaluates the use of clinical document interchange standards as the basis for quality measurement. Methods 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. Results 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. Conclusion 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.

Objective Accurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. Materials and Methods Data were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data. Results A total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations. Discussion These results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality. Conclusions Federal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.

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