Using Clinical Data Standards to Measure Quality: A New ApproachFunding Support for this research was provided by the Kansas Health Information Network and Diameter Health, jointly donating time and resources to the research team.
12 January 2018
17 April 2018
13 June 2018 (online)
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.
KeywordsMedicare Access and Chip Reauthorization Act - quality - standards adoption - Continuity of Care Document - clinical data management - Merit-Based Incentive Payment System and Alternative Payment Models - health care reform
Protection of Human and Animal Subjects
This study was approved by the Institutional Review Board for the University of Texas, Health Science Center, Committee for the Protection of Human Subjects. Technical and administrative safeguards were utilized to protect the privacy of such information throughout this research.
- 1 Better Care. Smarter Spending. Healthier People: Paying Providers for Value, Not Volume. Centers for Medicare and Medicaid Services; 2015. Available at: https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2015-Fact-sheets-items/2015-01-26-3.html . Accessed January 7, 2018
- 2 Japsen B. UnitedHealth, Aetna, Anthem Near 50% Value-Based Care Spending . Forbes, Inc. Available at: https://www.forbes.com/sites/brucejapsen/2017/02/02/unitedhealth-aetna-anthem-near-50-value-based-care-spending/ . Accessed January 7, 2018
- 3 HEDIS & Quality Measurement. National Committee for Quality Assurance. Available at: http://www.ncqa.org/hedis-quality-measurement . Accessed January 7, 2018
- 4 Jung K. The impact of information disclosure on quality of care in HMO markets. Int J Qual Health Care 2010; 22 (06) 461-468
- 5 Eddy DM, Pawlson LG, Schaaf D. , et al. The potential effects of HEDIS performance measures on the quality of care. Health Aff (Millwood) 2008; 27 (05) 1429-1441
- 6 Mainous III AG, Talbert J. Assessing quality of care via HEDIS 3.0. Is there a better way?. Arch Fam Med 1998; 7 (05) 410-413
- 7 Participation continues to rise in Medicare Physician Quality Reporting System and Electronic Prescribing Incentive Program. Centers for Medicare and Medicaid Services; 2015. Available at: https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2015-Fact-sheets-items/2015-04-23-1.html . Accessed January 7, 2018
- 8 Hsiao C-J, Decker SL, Hing E, Sisk JE. Most physicians were eligible for federal incentives in 2011, but few had EHR systems that met meaningful-use criteria. Health Aff (Millwood) 2012; 31 (05) 1100-1107
- 9 2016 Report To Congress on Health IT Progress. The Office of the National Coordinator for Health Information Technology (ONC) Office of the Secretary, United States Department of Health and Human Services. Available at: https://www.healthit.gov/sites/default/files/2016_report_to_congress_on_healthit_progress.pdf . Accessed January 7, 2018
- 10 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 (05) 503-527
- 11 Torda P, Tinoco A. Achieving the promise of electronic health record-enabled quality measurement: a measure developer's perspective. EGEMS (Wash DC) 2013; 1 (02) 1031
- 12 Johnson SG, Speedie S, Simon G, Kumar V, Westra BL. Quantifying the effect of data quality on the validity of an eMeasure. Appl Clin Inform 2017; 8 (04) 1012-1021
- 13 Furukawa MF, King J, Patel V, Hsiao C-J, Adler-Milstein J, Jha AK. Despite substantial progress In EHR adoption, health information exchange and patient engagement remain low in office settings. Health Aff (Millwood) 2014; 33 (09) 1672-1679
- 14 D'Amore JD, Mandel JC, Kreda DA. , et al. Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Inform Assoc 2014; 21 (06) 1060-1068
- 15 Clough JD, McClellan M. Implementing MACRA: implications for physicians and for physician leadership. JAMA 2016; 315 (22) 2397-2398
- 16 Cottington S. PopHealth primer. ONC funds open-source software to streamline clinical quality measures reporting for meaningful use program. J AHIMA 2011; 82 (09) 48-50
- 17 D'Amore JD, Sittig DF, Wright A, Iyengar MS, Ness RB. The promise of the CCD: challenges and opportunity for quality improvement and population health. AMIA Annu Symp Proc 2011; 2011: 285-294
- 18 Taggart J, Liaw S-T, Yu H. Structured data quality reports to improve EHR data quality. Int J Med Inform 2015; 84 (12) 1094-1098
- 19 Gardner W, Morton S, Byron SC. , et al. Using computer-extracted data from electronic health records to measure the quality of adolescent well-care. Health Serv Res 2014; 49 (04) 1226-1248
- 20 Kern LM, Malhotra S, Barrón Y. , et al. Accuracy of electronically reported “meaningful use” clinical quality measures: a cross-sectional study. Ann Intern Med 2013; 158 (02) 77-83
- 21 Current Vendor Information. National Committee for Quality Assurance. Available at: http://www.ncqa.org/hedis-quality-measurement/data-reporting-services/emeasure-certification/Current-Vendor-Information . Accessed January 7, 2018
- 22 Quality Data Model from eCQI Resource Center. Centers for Medicare and Medicaid Services: Office of the National Coordinator for Health IT. Available at: https://ecqi.healthit.gov/qdm . Accessed January 7, 2018
- 23 Percent of Adults with Diagnosed Diabetes; 2015. Kansas Health Matters. Available at: http://www.kansashealthmatters.org/index.php?module=indicators&controller=index&action=view&indicatorId=2270&localeId=19 . Accessed January 11, 2018
- 24 Conway PH, Mostashari F, Clancy C. The future of quality measurement for improvement and accountability. JAMA 2013; 309 (21) 2215-2216
- 25 McCoy RG, Tulledge-Scheitel SM, Naessens JM. , et al. The method for performance measurement matters: diabetes care quality as measured by Administrative Claims and Institutional Registry. Health Serv Res 2016; 51 (06) 2206-2220
- 26 Amster A, Jentzsch J, Pasupuleti H, Subramanian KG. Completeness, accuracy, and computability of National Quality Forum-specified eMeasures. J Am Med Inform Assoc 2015; 22 (02) 409-416
- 27 Parsons A, McCullough C, Wang J, Shih S. Validity of electronic health record-derived quality measurement for performance monitoring. J Am Med Inform Assoc 2012; 19 (04) 604-609
- 28 Heisey-Grove DM, Wall HK, Wright JS. Electronic clinical quality measure reporting challenges: findings from the Medicare EHR Incentive Program's Controlling High Blood Pressure Measure. J Am Med Inform Assoc 2018; 25 (02) 127-134
- 29 HL7 CDA® R2 Implementation Guide: Quality Reporting Document Architecture - Category I (QRDA I) STU Release 5–US Realm. Health Level 7; 2017. Available at: http://www.hl7.org/implement/standards/product_brief.cfm?product_id=35 . Accessed March 1, 2018
- 30 Fast Health Interoperability Resource STU. 3. Health Level 7; 2017. Available at: https://www.hl7.org/fhir/ . Accessed Mar 1, 2018