Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives

Abstract

Background Electronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings.

Objective This study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices.

Methods A qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors.

Results Cooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a “hybrid strategy” of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs.

Conclusion Cooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Keywords

quality improvement - primary health care - electronic health records - quality assurance - health care

Protection of Human and Animal Subjects

The Northwestern University Institutional Review Board approved this study.

Publication History

Article published online:
04 May 2022

• References

• 1 Chatterjee P, Joynt KE. Do cardiology quality measures actually improve patient outcomes?. J Am Heart Assoc 2014; 3 (01) e000404
• 2 D'Amore JD, Li C, McCrary L. et al. Using clinical data standards to measure quality: a new approach. Appl Clin Inform 2018; 9 (02) 422-431
• 3 McClure RC, Macumber CL, Skapik JL, Smith AM. Igniting harmonized digital clinical quality measurement through terminology, CQL, and FHIR. Appl Clin Inform 2020; 11 (01) 23-33
• 4 eCQMs. About eCQI. Accessed April 1, 2019 at: https://ecqi.healthit.gov/ecqms
• 5 Marcotte L, Seidman J, Trudel K. et al. Achieving meaningful use of health information technology: a guide for physicians to the EHR incentive programs. Arch Intern Med 2012; 172 (09) 731-736
• 6 Payne TH, Corley S, Cullen TA. et al. Report of the AMIA EHR-2020 task force on the status and future direction of EHRs. J Am Med Inform Assoc 2015; 22 (05) 1102-1110
• 7 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
• 8 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
• 9 Colin NV, Cholan RA, Sachdeva B, Nealy BE, Parchman ML, Dorr DA. Understanding the impact of variations in measurement period reporting for electronic clinical quality measures. EGEMS (Wash DC) 2018; 6 (01) 17
• 10 Cholan RA, Weiskopf NG, Rhoton DL. et al. Specifications of clinical quality measures and value set vocabularies shift over time: a study of change through implementation differences. AMIA Annu Symp Proc 2018; 2017: 575-584
• 11 Balasubramanian BA, Marino M, Cohen DJ. et al. Use of quality improvement strategies among small to medium-size us primary care practices. Ann Fam Med 2018; 16 (Suppl. 01) S35-S43
• 12 Cohen DJ, Dorr DA, Knierim K. et al. Primary care practices' abilities and challenges in using electronic health record data for quality improvement. Health Aff (Millwood) 2018; 37 (04) 635-643
• 13 Kukhareva P, Weir CR, Staes C, Borbolla D, Slager S, Kawamoto K. Integration of clinical decision support and electronic clinical quality measurement: domain expert insights and implications for future direction. AMIA Annu Symp Proc 2018; 2018: 700-709
• 14 Goetz Goldberg D, Kuzel AJ, Feng LB, DeShazo JP, Love LE. EHRs in primary care practices: benefits, challenges, and successful strategies. Am J Manag Care 2012; 18 (02) e48-e54
• 15 Meyers D, Miller T, Genevro J. et al. EvidenceNOW: balancing primary care implementation and implementation research. Ann Fam Med 2018; 16 (Suppl. 01) S5-S11
• 16 Million hearts 2007 priorities. Accessed April 1, 2019 at: https://millionhearts.hhs.gov/
• 17 Borkan JM. Immersion/crystallization. In: Crabtree BF, Miller WL. eds. Doing Qualitative Research. Newbury Park, CA: Sage Publications; 1999
• 18 Borkan JM. Immersion–crystallization: a valuable analytic tool for healthcare research. Fam Pract. 2021;
• 19 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (Suppl. 03) i68-i74
• 20 Singh H, Sittig DF. Measuring and improving patient safety through health information technology: the Health IT Safety Framework. BMJ Qual Saf 2016; 25 (04) 226-232
• 21 Green LA, Potworowski G, Day A. et al. Sustaining “meaningful use” of health information technology in low-resource practices. Ann Fam Med 2015; 13 (01) 17-22
• 22 Kiefe CI, Allison JJ, Williams OD, Person SD, Weaver MT, Weissman NW. Improving quality improvement using achievable benchmarks for physician feedback: a randomized controlled trial. JAMA 2001; 285 (22) 2871-2879
• 23 Hermans MP, Elisaf M, Michel G. et al; OPTIMISE International Steering Committee. Benchmarking is associated with improved quality of care in type 2 diabetes: the OPTIMISE randomized, controlled trial. Diabetes Care 2013; 36 (11) 3388-3395
• 24 Resource lLibrary. Accessed March 28, 2022 at: https://qpp.cms.gov/resources/resource-library