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Context: Atherosclerotic cardiovascular disease (ASCVD) disproportionally affects racial and ethnic minority populations. HMG-CoA reductase inhibitors (statins) have long been first-line pharmacotherapy for ASCVD prevention. Measures recommended to guide statin indication changed in 2013 guidelines aimed at improving ASCVD prevention and reducing ASCVD disparities. It is unknown whether risk screening for initiating statins for primary prevention of ASCVD differs across race and ethnicity. Objective: Examine racial/ethnic/language differences in electronic health record (EHR) prevalence of ASCVD screening measures used for statin consideration. Specifically, compare Asian, Latino, non-Hispanic Black, and non-Hispanic White adults further distinguished by language preference. Design: Retrospective cohort study. Setting: ADVANCE (Advancing Data Value Across a National Community Health Center) network-a PCORNet data network-635 community health centers (CHCs) in 24 US states with a shared EHR platform. Population: Adults 50+ years with =1 primary care encounter in the network 2009-2013, and/or 2014-2018. Outcome measures: Prevalence of period-specific guideline concordant screening measures for statin consideration in 2009-2013: low-density lipoprotein (LDL) and 2014-2018: pooled cohort equation (PCE) components of age, sex, race, systolic blood pressure, total cholesterol, high-density lipoprotein, smoking status. For patients seen in both periods, we assessed change in period-specific measure prevalence. Outcomes were analyzed using GEE logistic regression to measure the association between race/ethnicity/language and measurement prevalence. Results: Adjusting for sociodemographic and clinical factors, compared to English-preferring non-Hispanic White patients, all other groups were more likely to have LDL documented (2009-2013, N=195,061) and all PCE components documented (2014-2018, N=344,504). Among those seen in both periods (N=128,621), all groups had lower odds of having PCE components vs. LDL documented in the measures' respective period; English-preferring non-Hispanic Black adults experienced a greater decline in measure documentation across the two periods compared to English-preferring non-Hispanic White adults (odds ratio 0.81; 95% confidence interval 0.72-0.91). Conclusions: CHCs may mitigate racial/ethnic/language disparities in the collection of screening measures for ASCVD prevention, which did not improve after the 2013 guideline change. [ABSTRACT FROM AUTHOR]

Electronic health record (EHR) tools such as documentation shortcuts and ordering templates are designed to enhance both the efficiency and quality of care delivery. Use of such tools vary widely in primary care. Understanding patterns of use as a means to identify priority areas for improvement are lacking. Objective: This study is designed to identify and understand patterns of EHR efficiency use among primary care clinicians as a means to inform training support needs. Study Design: Retrospective observational study. Setting or Dataset: 340 Community Health Centers (CHCs) across 48 US states from the OCHIN Network. Population studied: The OCHIN's network and 2033 primary care clinicians engaged in patient care at least 2 days a week during DEC 2019-FEB 2020. Methods: Six EHR efficiency indicators were drawn from the Epic EHR Signal efficiency indicators which are automatically generated monthly for clinicians. Indicators include: use of quick actions, preference lists, level of service and diagnosis speed buttons and chart search functions and notes written with smart tools. A weighted score of these indicators represented overall EHR proficiency with a possible range of 0-10 with higher scores suggesting higher efficiency. Latent profile analysis (LPA) was used to identify clusters of clinicians defined by distinct patterns of efficiency indicators. Results: The median EHR proficiency score was 4 and ranged from 0 to 9.5. The 9 cluster LPA solution the best overall fit. Clusters represented different patterns with notable strengths and deficiencies in EHR use efficiency. Cluster 2 was large (n=518) and characterized by being low on all indicators except for chart search. In contrast, cluster 9 was very small (n=12) and characterized by high scores for 4/6 indicators and low on quick actions. Conclusions: Substantial variation in EHR use efficiency among primary care clinicians was observed and can be represented by nine different patterns of indicators. These patterns show strengths and deficiencies in EHR use efficiency and could guide a tailored training approach to improve EHR efficiency. [ABSTRACT FROM AUTHOR]