Title: Improving Statin Quality Metric Performance and Identifying Predictors of Compliance in a
Community Health Center
Authors: Joshua Tseng, MD MBA, Catherine McDonald, MD, Rodrigo Alban, MD
Location: CSC Health, Los Angeles, CA; Cedars-Sinai Medical Center, Los Angeles, CA
Background: Cardiovascular disease is the leading cause of death in the US, and treatment of elevated
blood cholesterol with a statin reduces the risk of major cardiovascular events by 20%. However, studies
show that over 25% of guideline-eligible patients are not on statins. Risk factors for noncompliance
include minority status, low income, underinsurance, and female gender.
Objective: In this study, we comprehensively evaluate patterns of statin utilization, implement strategies
to improve performance, and identify factors associated with compliance, including language
congruence between healthcare providers and patients.
Methods: Statin therapy utilization at a network of federally qualified health centers in a major
metropolitan city was assessed. Institutional database was queried for all patients in calendar years
2022-2023 who were guideline-eligible for statins based on MIPS Clinical Quality Measure: Quality ID
#438: Statin Therapy for the Prevention and Treatment of Cardiovascular Disease. Data on patient and
clinician demographics, medical history via ICD codes, LDL levels, and prescriptions were obtained. The
compliance rate was calculated using definitions established by MIPS. Chart reviews of non-compliant
patients were conducted to identify reasons for non-compliance, and a series of interventions was
implemented in Q3 2023. Compliance rates at the end of 2022 and the end of 2023 were compared. A
multivariable regression model was used to identify independent predictors of compliance.
Results: A total of 2,265 guideline-eligible patients were identified. 1,410 (62.2%) patients were
guideline-compliant, while 855 (37.8%) patients were non-compliant. Of the non-compliant cohort, 412
patients had incorrect LDL values captured due to erroneous data mapping, and 283 patients were
incorrectly diagnosed with ICD-10 code E78.00, “Pure hypercholesterolemia”. The remaining 160
patients met guidelines for statin therapy. A series of interventions were implemented, including
improved data mapping, group clinician educational seminars, 1:1 coaching, and a tech-based
intervention that automatically flagged patients eligible for statins and possible indications for exclusion.
At the end of 2023, the true compliance rate increased from 62.2% to 88.0%.
When comparing compliant to non-compliant patients, compliant patients were more likely to be Asian
(83.1% vs. 68.0%, p<0.01) and less likely to be White (9.1% vs. 22.0%, p<0.01). Compliant patients were
also older (65 vs. 59 years, p<0.01) and had more comorbidities in terms of Charlson-Deyo Score (1.34
vs. 0.00, p<0.01). Compliant patients were more likely to receive care from a language congruent
clinician (89.1% vs. 80.1%, p<0.01). On multivariable regression, predictors of higher odds of compliance
include older age (1.037, 95%CI 1.024-1.050), higher Charlson-Deyo Scores (1.663, 95%CI 1.447-1.912),
and language congruence (2.718, 95%CI 1.817-4.066), while White race was associated with lower odds
of compliance compared to Asians (0.361, 95%CI 0.244-0.534).
Conclusions: A comprehensive review of statin metric compliance, coupled with clinician education and
a technology-based clinical decision support tool, resulted in a significant increase from 62.2% to 88.0%
over a year. Finally, in this study, the strongest predictor of statin compliance was language congruence
between patients and providers.
Clinical Implications: Quality metrics may be adversely affected by non-clinical factors such as inaccurate
data mapping and medical coding. Real-time clinical decision support tools are associated with
improvement in best practice guidelines. Lingual congruency between clinicians and patients is
associated with improved outcomes.