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Qualitative evaluation of a cardiovascular quality improvement programmereveals sizable data inaccuracies in small primary care practices
  1. Megan McHugh1,
  2. Tiffany Brown2,
  3. David T Liss2,
  4. Stephen D Persell2,
  5. Milton Garrett3,
  6. Theresa L Walunas3
  1. 1Center for Health Services and Outcomes Research and Department of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
  2. 2Division of General Internal Medicine and Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
  3. 3Center for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
  1. Correspondence to DrMeganMcHugh; megan-mchugh{at}

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Among the most promising quality improvement (QI) interventions for small primary care practices are those led by practice facilitators (PFs), specially trained individuals who help practices develop capacity for continuous QI.1 2 They provide coaching on best practices for QI implementation, including using technology to improve care.3 PF-led QI initiatives are positively associated with guideline adoption,4 5 and may be cost-neutral if they reduce even a small number of high cost events (eg, admissions).6 As part of Healthy Hearts in the Heartland (H3), a programme from the Agency for Healthcare Research and Quality’s EvidenceNow initiative, PFs worked with small and medium-sized primary care practices to implement QI strategies for cardiovascular disease prevention.7 To identify lessons learnt from the programme, we interviewed practice leaders and PFs from practices that experienced the largest and smallest gains in quality scores to understand their experiences.


All participating practices were assigned a primary PF for 12 months who met with practices on demand, typically once a month. PFs offered practices QI interventions related to the ABCS of heart health (Aspirin therapy, Blood pressure control, Cholesterol management, and Smoking screening and cessation) with the goal of improving four ABCS measures that are used in national quality incentive programmes, such as the Merit-based Incentive Payment System.8 9 Information about the H3 intervention, outcome measures and study design can be found elsewhere.10 11

Practice leaders from 16 practices with large improvement on the ABCS measures after 12 months, and 15 practices with minimal improvement after 12 months received up to 6 contact attempts asking them to complete a 30 min telephone interview. Practice leaders were individuals at the practice who were most familiar with the intervention, generally physicians and QI managers. Following commitment from the practice leader, we invited the corresponding PF to complete a separate interview. Interviews were conducted between March and April 2018, ~8 months after the 12-month intervention period.

Semi-structured interview protocols were constructed based on the Consolidated Framework for Implementation Research.12 Interviews were digitally recorded and analysed iteratively and inductively for emergent themes and patterns using the constant comparison approach.


We completed interviews with practice leaders from 14 of 31 eligible practices (45%), and all 7 PFs assigned to those practices (table 1). On average, practices implemented 5.7 electronic health record (EHR)-based QI strategies (eg, clinical decision support prompts) and 7.4 non-EHR strategies (eg, workflow changes).

Table 1

Characteristics of participating practices

The practices experienced sizeable changes in ABCS performance measures—both positive and negative—over the 12-month assessment period (table 2). Although most practice leaders and PFs described H3 positively, and could offer examples of how H3 improved care in the practices, respondents typically noted that the largest changes in ABCS scores likely reflected improvements in documentation due to coaching or fixes to EHR data ‘glitches’ rather than changes in care delivery (eg,table 2, practice E). In other cases, respondents were puzzled by observed changes in measured performance, but could not attribute large improvements (or declines) in performance to the H3 interventions (eg,table 2, practice B).

Table 2

Examples of large changes in ABCS scores, and perceptions of changes by practice facilitators


In this evaluation of a PF-led QI intervention, we found a number of practices with sizeable changes in performance scores after 12 months. While the largest changes in scores may not reflect actual changes in care delivery, in practices where data accuracy improved, the changes represent success for the H3 programme. Those practices are now better prepared to engage in QI and pay-for-performance efforts that rely on EHR data.

Our results highlight the importance of mixed methods research, which provides a richer contextual lens to judge the success of QI interventions. A limitation of our study is reliance on ABCS measures as our quality indicators. H3 interventions may have improved care processes uncaptured by the measures. Also, our analysis relied on perceptions of only practice leaders and PFs, and our sample is small. However, our findings are consistent with the broader evaluation of EvidenceNow, and evaluations of similar efforts showing that small practices continue to struggle with EHRs.13 14 Federal investments in EHR adoption and technical assistance were made available to practices with the expectation that EHRs would generate meaningful performance data, enabling QI and leading to improved care delivery.15 However, our findings show that some small practices continue to operate with limited or incorrect performance data. Our results should lend caution to pay-for-performance programmes that rely on EHR data.


The authors wish to thank the practice leaders and practice facilitators who participated in this study.



  • Contributors MM designed study with input from all authors. MM and TB conducted interviews and analysed data. MM, TB, DTL, SDP, MG and TLW were major contributors in the writing of the manuscript. All authors read and approved the final manuscript.

  • Funding This research was supported by grant number R18HS023921 from the Agency for Healthcare Research and Quality (AHRQ).

  • Disclaimer The contents of this product are solely the responsibility of the authors and do not necessarily represent the official views of or imply endorsement by AHRQ or the U.S. Department of Health and Human Services. AHRQ had no involvement in the design of the study and collection, analysis, and interpretation of data and in writing of the manuscript.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval Northwestern University Institutional Review Board (STU00202126).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement No data are available.