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Objective: To describe telemedicine use patterns and understand clinic's approaches to shifting care delivery during the COVID-19 pandemic. Methods: We used electronic health record data from 203 community health centers across 13 states between 01/01/2019 and 6/31/2021 to describe trends in telemedicine visit rates over time. Qualitative data were collected from 13 of those community health centers to understand factors influencing adoption and implementation of telemedicine. Results: Most clinics in our sample were in urban areas (n = 176) and served a majority of uninsured and publicly insured patients (12.8% and 44.4%, respectively) across racial and ethnic minority groups (16.6% Black and 29.3% Hispanic). During our analysis period there was a 791% increase in telemedicine visits from before the pandemic (.06% pre- vs 47.5% during). A latent class growth analysis was used to examine differences in patterns of adoption of telemedicine across the 203 CHCs. The model resulted in 6 clusters representing various levels of telemedicine adoption. A mixed methods approach streamlined these clusters into 4 final groups. Clinics that reported rapid adoption of telemedicine attributed this change to leadership prioritization of telemedicine, robust quality improvement processes (eg, using PDSA processes), and emphasis on training and technology support. Conclusions: In response to the COVID-19 pandemic, telemedicine adoption rates varied across clinics. Our study highlight that organizational factors contributed to the clinic's ability to rapidly uptake and use telemedicine services throughout the pandemic. These approaches could inform future non-pandemic practice change and care delivery. [ABSTRACT FROM AUTHOR]

Previous reviews of strategies to increase cervical cancer screening are more than 10 years old, the U.S. continues to fall short of the Healthy People 2030 cervical cancer screening goal, and guidelines were revised in 2018, therefore an updated review of the existing literature is needed. We conducted a scoping review using electronic databases PubMed, Scopus, and Ovid Medline that included publication dates between 2012 and 2021 to answer the question, "Which strategies implemented in U.S. primary care settings have been most successful in increasing rates of cervical cancer screening since the 2012 US Preventative Services Task Force cervical cancer screening guidelines were published?" We mapped findings to pre-specified implementation strategy categories. After initially identifying 399 articles, we excluded 350 due to duplicates or not meeting review criteria, leaving 49 articles for full review. We excluded 37 of these during full-text review and identified 2 additional articles from the manual search of reference lists for a total of 14 studies for abstraction. Eleven articles reported on strategies resulting in increased cervical cancer screening, and 3 did not. Clinic workflow re-design strategies showed the greatest promise in improving cervical cancer screening rates, education strategies for patients had mixed results, and quality management strategies were not effective. These findings suggest clinical workflow re-structures and patient education strategies can increase cervical cancer screening in primary care settings. Results are particularly important in settings that care for underserved populations, as these settings may need additional implementation strategies to decrease cervical cancer screening disparities. [ABSTRACT FROM AUTHOR]

Purpose: Health insurance coverage affects a patient's ability to access optimal care, the percentage of insured patients on a clinic's panel has an impact on the clinic's ability to provide needed health care services, and there are racial and ethnic disparities in coverage in the United States. Thus, we aimed to assess changes in insurance coverage at community health center (CHC) visits after the Patient Protection and Affordable Care Act (ACA) Medicaid expansion by race and ethnicity.Methods: We undertook a retrospective, observational study of visit payment type for CHC patients aged 19 to 64 years. We used electronic health record data from 10 states that expanded Medicaid and 6 states that did not, 359 CHCs, and 870,319 patients with more than 4 million visits. Our analyses included difference-in-difference (DD) and difference-in-difference-in-difference (DDD) estimates via generalized estimating equation models. The primary outcome was health insurance type at each visit (Medicaid-insured, uninsured, or privately insured).Results: After the ACA was implemented, uninsured visit rates decreased for all racial and ethnic groups. Hispanic patients experienced the greatest increases in Medicaid-insured visit rates after ACA implementation in expansion states (rate ratio [RR] = 1.77; 95% CI, 1.56-2.02) and the largest gains in privately insured visit rates in nonexpansion states (RR = 3.63; 95% CI, 2.73-4.83). In expansion states, non-Hispanic white patients had twice the magnitude of decrease in uninsured visits compared with Hispanic patients (DD = 2.03; 95% CI, 1.53-2.70), and this relative change was more than 2 times greater in expansion states compared with nonexpansion states (DDD = 2.06; 95% CI, 1.52-2.78).Conclusion: The lower rates of uninsured visits for all racial and ethnic groups after ACA implementation suggest progress in expanding coverage to CHC patients; this progress, however, was not uniform when comparing expansion with nonexpansion states and among all racial and ethnic minority subgroups. These findings suggest the need for continued and more equitable insurance expansion efforts to eliminate health insurance disparities. [ABSTRACT FROM AUTHOR]

Background: It is hypothesized that Affordable Care Act (ACA) Medicaid expansions could substantially improve access to health insurance and healthcare services for patients at risk for diabetes mellitus (DM), with pre-DM, or already diagnosed with DM. The ACA called for every state to expand Medicaid coverage by 2014. In a 2012 legal challenge, the US Supreme Court ruled that states were not required to implement Medicaid expansions. This 'natural experiment' presents a unique opportunity to learn whether and to what extent Medicaid expansion can affect healthcare access and services for patients with DM risk, pre-DM, or DM.Methods/design: Data from electronic health records (EHRs) from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) clinical data research network, which has data from >700 community health centers (CHCs), was included in the study. EHR data will be linked to Oregon Medicaid claims data. Data collection will include information on changes in health insurance, service receipt, and health outcomes, spanning 9 years (pre- and post-expansion), comparing states that expanded Medicaid, and those that did not. Patients included in this study will be diagnosed with DM, be at risk for DM, or have pre-DM, between the ages of 19 and 64, with ≥1 ambulatory visit. Sample size is estimated to be roughly 275,000 patients. Biostatistical analyses will include the difference-in-differences (DID) methodology and a generalized linear mixed model. Econometric analyses will include a DID two-part method to calculate the difference in Medicaid expenditures in Oregon among newly insured CHC patients.Discussion: Findings will have national relevance on DM health services and outcomes and will be shared through national conferences and publications. The findings will provide information needed to impact the policy as it is related to access to health insurance and receipt of healthcare among a vulnerable population.Trial Registration: This project is registered with ClinicalTrials.gov ( NCT02685384 ). Registered 18 May 2016. [ABSTRACT FROM AUTHOR]

Introduction: Little is known about what clinic-level factors differentiate community health centers that achieve high performance on cancer-preventive care metrics. This study aims to describe the longitudinal trends in the delivery of 3 cancer-preventive care metrics (cervical and colorectal cancer screenings and tobacco-cessation intervention) and define and compare community health centers with high cancer-preventive care performance with those with low cancer-preventive care performance.Methods: This observational study used 2012-2019 community health center data (N=933) from the Uniform Data System. High/low performance was based on Healthy People 2020 targets and sample distribution. For each cancer-preventive care metric, the percentage of community health centers that met high (≥70.5% at cervical or colorectal cancer screening or >80% tobacco-cessation intervention) and low thresholds at 1, 2, and all the 3 screenings was estimated. Multivariable generalized estimating equations logistic regression modeling was used to assess the community health center‒level factors associated with screening performance.Results: The community health centers' performance for tobacco-cessation intervention remained at ≥80%, with a small increase over time. Performance for cervical cancer screening remained unchanged with about 50% of patients screened. Colorectal cancer screening performance increased from around 30% in 2012 to 44% in 2019. Very few community health centers reached high performance (3%) in all the 3 indicators, and 13% of community health centers were high in any 2 of the outcomes in 2019. Higher patient volume, a greater proportion of Hispanic patients, fewer uninsured patients, and community health centers located in the Northeast region were associated with high performance in 2019.Conclusions: Very few community health centers meet all Healthy People 2020 goals in cancer screenings and may struggle to achieve the 2030 goals. Very few indicators differentiated high performers from low performers. [ABSTRACT FROM AUTHOR]