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A Case Study with Diameter Health
As EHR adoption increased rapidly over the past decade following the passage of the 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act and Meaningful Use program, many providers now use their EHR to generate and submit quality reports.1 These quality reports typically use standardized measures, known as electronic clinical quality measures (eCQMs), to report performance to providers and payers.
However, despite the successful adoption of EHRs, current approaches to quality measurement continue to be challenging. Healthcare organizations face significant hurdles in implementing and reporting electronic quality reporting due to inconsistent data formats, concerns of data completeness and accuracy, lack of structured data, and inconsistency in measure logic implementation.2
Supporting quality measurement using interoperability standards, such as the Consolidated Clinical Document Architecture (C-CDA) or Fast Healthcare Interoperability Resources (FHIR), presents a major opportunity to address such challenges in quality measurement. The growth of standards-based exchange over the past decade makes this approach possible today and the use of data aggregators in particular, like Health Information Exchanges, present significant opportunities to strengthen eCQM measurement. By collecting clinical data across different EHRs and healthcare practices into a centralized repository, HIEs can provide a more complete longitudinal patient record of aggregated data across sources back to their participating member organizations. HIEs could make it possible to measure quality across institutions in a patient-centric manner by gathering data wherever a patient seeks care, thus more accurately reflecting the quality of care provided.3
Consider this first scenario: The result of quality measurement from a single EHR is a view of care quality that changes depending on where the data is reported. In the first encounter, Patient John’s record has documented hypertension and a blood pressure reading of 90/60, making him compliant for the measure. At John’s second encounter with a different provider, hypertension is not documented, so John is not eligible for the measure. At the third encounter, with yet a different doctor, hypertension is recorded as a problem, but there is no blood pressure reading recorded, so the blood pressure quality measure is non-compliant. Three EHRs, three different doctors, and three different quality measure results for the same patient.
Now consider the second scenario: The same three encounters, but the data is aggregated and reported through an HIE like HealtheConnections or another data aggregator. Collecting all the data across care settings enables more accurate quality measurement. Data from multiple sources fills gaps without needing to integrate natively with each EHR. In this case, the care team is rewarded for providing the right care because data from all the patient’s points of care are used in reporting quality. It’s a team sport.
Interestingly, traditional HEDIS reporting was originally developed in 1991 based on healthcare administrative data sources (e.g., membership, billing, claims, encounter data) collected across multiple practices and health delivery organizations.
Similarly, we believe a better model for ambulatory clinical quality reporting would be based on a longitudinal view of patient encounters throughout the reporting period. Research, like this from HealtheConnections demonstrates the effectiveness of such an approach. It adds no additional clinical workload and enables a more complete and accurate view of care provided to patients. As an additional example, the Journal of the American Medical Informatics Association (JAMIA) recently published peer-reviewed research5 authored by Diameter Health and KONZA, another leading HIE, that provides further evidence that calculating clinical quality measures using enriched health data within a health information exchange improves the accuracy of the quality scores. The evidence is accumulating that multi-source clinical data- if refined using best of breed technology- supports not only improved quality reporting but also use cases across the healthcare spectrum of administration, operations, and clinical use.
We would love to hear what you think about the value of multi-sourced clinical data integration and the future of ambulatory quality reporting in general. We can be reached at firstname.lastname@example.org, or submit a comment on the form found on this page.
1. Kern LM, Barrón Y, Dhopeshwarkar RV, et al. Electronic health records and ambulatory quality of care. J Gen Intern Med 2013; 28:496-503.
2. Ahmad FSS, Rasmussen LV, Persell SD et al. Challenges to electronic clinical quality measurement using third-party platforms in primary care practices: the healthy hearts in the heartland experience. JAMIA. 2019; 2(4): 423-428.
Chan KS, Fowles JB, Weiner JP. Review: electronic health records and the reliability and validity of quality measures: a review of the literature. Med Care Res Rev 2010; 67(5): 503-527.
Weiner JP, Fowles JB, Chan KS. New paradigms for measuring clinical performance using health records. Int J Qual Health Care 2012; 24(3): 200-205.
3. Shapiro, J., Clesca C.,Genes N., et al. Advancing Quality Measurement and Care Improvement with Health Information Exchange. https://digital.ahrq.gov/sites/default/files/docs/citation/r01hs021261-shapiro-final-report-2018.pdf, Accessed January 13, 2021.
4. Clyburn, Hallema Sharif. “Survey: Patients See 18.7 Different Doctors on Average” Practice Fusion. April 27, 2010 http://
5. John D D’Amore, Laura K McCrary, Jody Denson, Chun Li, Christopher J Vitale, Priyaranjan Tokachichu, Dean F Sittig, Allison B McCoy, Adam Wright, Clinical data sharing improves quality measurement and patient safety, Journal of the American Medical Informatics Association, 2021;, ocab039, https://doi.org/10.1093/jamia/ocab039