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Abstract: To examine which parental health care and health factors are most strongly associated with a child's receipt of recommended care we must be able to link children to their parents in electronic health record data. Yet, there is not an easy way to link these data.To identify a national cohort of children that link to at least one parent in the same electronic health record dataset and describe their demographics.Methodology to link parents and children in electronic health records and descriptive sociodemographic data.Children with at least one encounter with a primary care clinician between Januray 1, 2007 and December 12, 2018 to a community health center in the OCHIN national network. We identified parents of these children who also had at least one encounter to a community health center in the network using emergency contact and guarantor record fields.A total of 227,552 children had parents with a linkable patient record. After exclusions, our final cohort included 213,513 distinct children with either one or two parent-links. 82% of children linked to a mother only, 14% linked to a father only, and 4% linked to both a mother and a father. Most families consisted of only one linked child (61%).We were able to link 33% of children to a parent in electronic health record data from a large network of community health centers across the United States. Further analyses utilizing these linkages will allow examination of the multi-level factors that impact a child's receipt of recommended health care., Competing Interests: The authors report no conflicts of interest., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: Many cancer survivors receive primary care in community health centers (CHCs). Cancer history is an important factor to consider in the provision of primary care, yet little is known about the completeness or accuracy of cancer history data contained in CHC electronic health records (EHRs)., Methods: We probabilistically linked EHR data from more than1.5 million adult CHC patients to state cancer registries in California, Oregon, and Washington and estimated measures of agreement (eg, kappa, sensitivity, specificity). We compared demographic and clinical characteristics of cancer patients as estimated by each data source, evaluating distributional differences with absolute standardized mean differences., Results: A total 74 707 cancer patients were identified between the 2 sources (EHR only, n = 22 730; registry only, n = 23 616; both, n = 28 361). Nearly one-half of cancer patients identified in registries were missing cancer documentation in the EHR. Overall agreement of cancer ascertainment in the EHR vs cancer registries (gold standard) was moderate (kappa = 0.535). Cancer site-specific agreement ranged from substantial (eg, prostate and female breast; kappa > 0.60) to fair (melanoma and cervix; kappa < 0.40). Comparing population characteristics of cancer patients as ascertained from each data source, groups were similar for sex, age, and federal poverty level, but EHR-recorded cases showed greater medical complexity than those ascertained from cancer registries., Conclusions: Agreement between EHR and cancer registry data was moderate and varied by cancer site. These findings suggest the need for strategies to improve capture of cancer history information in CHC EHRs to ensure adequate delivery of care and optimal health outcomes for cancer survivors., (© The Author(s) 2020. Published by Oxford University Press.)

Background: Electronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown., Objective: The objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care., Research Design: A retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care., Subjects: A total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source., Measures: Percentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source., Results: Attrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients., Conclusion: This study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

Background: In addition to delivering vital health care to millions of patients in the United States, community health centers (CHCs) provide needed health insurance outreach and enrollment support to their communities. We developed a health insurance enrollment tracking tool integrated within the electronic health record (EHR) and conducted a hybrid implementation-effectiveness trial in a CHC-based research network to assess tool adoption using two implementation strategies., Methods: CHCs were recruited from the OCHIN practice-based research network. Seven health center systems (23 CHC clinic sites) were recruited and randomized to receive basic educational materials alone (Arm 1), or these materials plus facilitation (Arm 2) during the 18-month study period, September 2016-April 2018. Facilitation consisted of monthly contacts with clinic staff and utilized audit and feedback and guided improvement cycles. We measured total and monthly tool utilization from the EHR. We conducted structured interviews of CHC staff to assess factors associated with tool utilization. Qualitative data were analyzed using an immersion-crystallization approach with barriers and facilitators identified using the Consolidated Framework for Implementation Research., Results: The majority of CHCs in both study arms adopted the enrollment tool. The rate of tool utilization was, on average, higher in Arm 2 compared to Arm 1 (20.0% versus 4.7%, p < 0.01). However, by the end of the study period, the rate of tool utilization was similar in both arms; and observed between-arm differences in tool utilization were largely driven by a single, large health center in Arm 2. Perceived relative advantage of the tool was the key factor identified by clinic staff as driving tool utilization. Implementation climate and leadership engagement were also associated with tool utilization., Conclusions: Using basic education materials and low-intensity facilitation, CHCs quickly adopted an EHR-based tool to support critical outreach and enrollment activities aimed at improving access to health insurance in their communities. Though facilitation carried some benefit, a CHC's perceived relative advantage of the tool was the primary driver of decisions to implement the tool., Trial Registration: ClinicalTrials.gov: NCT02355262, Posted February 4, 2015.

Background: Integrating behavioral health into primary care can improve care quality; however, most electronic health records are not designed to meet the needs of integrated teams. We worked with practices and behavioral health (BH) clinicians to design a suite of electronic health record tools to address these needs ("BH e-Suite"). The purpose of this article is to examine whether implementation of the BH e-Suite changes process of care, intermediate clinical outcomes, and patient experiences, and whether its use is acceptable to practice members and BH clinicians., Methods: We conducted a convergent mixed-methods proof-of-concept study, implementing the BH e-Suite across 6 Oregon federally qualified community health centers ("intervention clinics"). We matched intervention clinics to 6 control clinics, based on location and patient panel characteristics, to assess whether process of care (Patient Health Questionnaire-9 [PHQ-9] and Generalized Anxiety Disorder-7 screening) and intermediate outcomes (PHQ-9, Generalized Anxiety Disorder-7 scores) changed postimplementation. Prepost patient surveys were used to assess changes in patient experience. To elucidate factors influencing implementation, we merged quantitative findings with structured observations, surveys, and interviews with practice members., Results: Implementation improved process of care (PHQ-9 screening). During the course of the study, change in intermediate outcomes was not observed. Degree of BH e-Suite implementation varied: 2 clinics fully implemented, 2 partially implemented, and 2 practices did not implement at all. Initial practice conditions (eg, low resistance to change, higher capacity), process characteristics (eg, thoughtful planning), and individual characteristics (eg, high self-efficacy) were related to degree of implementation., Conclusions: Health information technology tools designed for behavioral health integration must fit the needs of clinics for the successful uptake and improvement in patient experiences. Research is needed to further assess the effectiveness of this tool in improving patient outcomes and to optimize broader dissemination of this tool among primary care clinics integrating behavioral health., Competing Interests: Conflict of interest: none declared., (© Copyright 2018 by the American Board of Family Medicine.)

Background: Children with health insurance have increased access to healthcare and receive higher quality care. However, despite recent initiatives expanding children's coverage, many remain uninsured. New technologies present opportunities for helping clinics provide enrollment support for patients. We developed and tested electronic health record (EHR)-based tools to help clinics provide children's insurance assistance., Methods: We used mixed methods to understand tool adoption, and to assess impact of tool use on insurance coverage, healthcare utilization, and receipt of recommended care. We conducted intent-to-treat (ITT) analyses comparing pediatric patients in 4 intervention clinics (n = 15,024) to those at 4 matched control clinics (n = 12,227). We conducted effect-of-treatment-on-the-treated (ETOT) analyses comparing intervention clinic patients with tool use (n = 2240) to intervention clinic patients without tool use (n = 12,784)., Results: Tools were used for only 15% of eligible patients. Qualitative data indicated that tool adoption was limited by: (1) concurrent initiatives that duplicated the work associated with the tools, and (2) inability to obtain accurate insurance coverage data and end dates. The ITT analyses showed that intervention clinic patients had higher odds of gaining insurance coverage (adjusted odds ratio [aOR] = 1.32, 95% confidence interval [95%CI] 1.14-1.51) and lower odds of losing coverage (aOR = 0.77, 95%CI 0.68-0.88), compared to control clinic patients. Similarly, ETOT findings showed that intervention clinic patients with tool use had higher odds of gaining insurance (aOR = 1.83, 95%CI 1.64-2.04) and lower odds of losing coverage (aOR = 0.70, 95%CI 0.53-0.91), compared to patients without tool use. The ETOT analyses also showed higher rates of receipt of return visits, well-child visits, and several immunizations among patients for whom the tools were used., Conclusions: This pragmatic trial, the first to evaluate EHR-based insurance assistance tools, suggests that it is feasible to create and implement tools that help clinics provide insurance enrollment support to pediatric patients. While ITT findings were limited by low rates of tool use, ITT and ETOT findings suggest tool use was associated with better odds of gaining and keeping coverage. Further, ETOT findings suggest that use of such tools may positively impact healthcare utilization and quality of pediatric care., Trial Registration: ClinicalTrials.gov, NCT02298361 ; retrospectively registered on November 5, 2014.

Introduction: Brief smoking-cessation interventions in primary care settings are effective, but delivery of these services remains low. The Centers for Medicare and Medicaid Services' Meaningful Use (MU) of Electronic Health Record (EHR) Incentive Program could increase rates of smoking assessment and cessation assistance among vulnerable populations. This study examined whether smoking status assessment, cessation assistance, and odds of being a current smoker changed after Stage 1 MU implementation., Methods: EHR data were extracted from 26 community health centers with an EHR in place by June 15, 2009. AORs were computed for each binary outcome (smoking status assessment, counseling given, smoking-cessation medications ordered/discussed, current smoking status), comparing 2010 (pre-MU), 2012 (MU preparation), and 2014 (MU fully implemented) for pregnant and non-pregnant patients., Results: Non-pregnant patients had decreased odds of current smoking over time; odds for all other outcomes increased except for medication orders from 2010 to 2012. Among pregnant patients, odds of assessment and counseling increased across all years. Odds of discussing or ordering of cessation medications increased from 2010 compared with the other 2 study years; however, medication orders alone did not change over time, and current smoking only decreased from 2010 to 2012. Compared with non-pregnant patients, a lower percentage of pregnant patients were provided counseling., Conclusions: Findings suggest that incentives for MU of EHRs increase the odds of smoking assessment and cessation assistance, which could lead to decreased smoking rates among vulnerable populations. Continued efforts for provision of cessation assistance among pregnant patients is warranted., (Copyright © 2017 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

Background: Changes in health insurance policies have increased coverage opportunities, but enrollees are required to annually reapply for benefits which, if not managed appropriately, can lead to insurance gaps. Electronic health records (EHRs) can automate processes for assisting patients with health insurance enrollment and re-enrollment., Objective: We describe community health centers' (CHC) workflow, documentation, and tracking needs for assisting families with insurance application processes, and the health information technology (IT) tool components that were developed to meet those needs., Method: We conducted a qualitative study using semi-structured interviews and observation of clinic operations and insurance application assistance processes. Data were analyzed using a grounded theory approach. We diagramed workflows and shared information with a team of developers who built the EHR-based tools., Results: Four steps to the insurance assistance workflow were common among CHCs: 1) Identifying patients for public health insurance application assistance; 2) Completing and submitting the public health insurance application when clinic staff met with patients to collect requisite information and helped them apply for benefits; 3) Tracking public health insurance approval to monitor for decisions; and 4) assisting with annual health insurance reapplication. We developed EHR-based tools to support clinical staff with each of these steps., Conclusion: CHCs are uniquely positioned to help patients and families with public health insurance applications. CHCs have invested in staff to assist patients with insurance applications and help prevent coverage gaps. To best assist patients and to foster efficiency, EHR based insurance tools need comprehensive, timely, and accurate health insurance information.

Objective: To demonstrate use of the electronic health record (EHR) for health insurance surveillance and identify factors associated with lack of coverage., Materials and Methods: Using EHR data, we conducted a retrospective, longitudinal cohort study of adult patients (n = 279 654) within a national network of community health centers during a 2-year period (2012-2013)., Results: Factors associated with higher odds of being uninsured (vs Medicaid-insured) included: male gender, age >25 years, Hispanic ethnicity, income above the federal poverty level, and rural residence (P < .01 for all). Among patients with no insurance at their initial visit (n = 114 000), 50% remained uninsured for every subsequent visit., Discussion: During the 2 years prior to 2014, many patients utilizing community health centers were unable to maintain stable health insurance coverage., Conclusion: As patients gain access to health insurance under the Affordable Care Act, the EHR provides a novel approach to help track coverage and support vulnerable patients in gaining and maintaining coverage., (© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

In 2014 both the Institute of Medicine and the National Quality Forum recommended the inclusion of social determinants of health data in electronic health records (EHRs). Both entities primarily focus on collecting socioeconomic and health behavior data directly from individual patients. The burden of reliably, accurately, and consistently collecting such information is substantial, and it may take several years before a primary care team has actionable data available in its EHR. A more reliable and less burdensome approach to integrating clinical and social determinant data exists and is technologically feasible now. Community vital signs-aggregated community-level information about the neighborhoods in which our patients live, learn, work, and play-convey contextual social deprivation and associated chronic disease risks based on where patients live. Given widespread access to "big data" and geospatial technologies, community vital signs can be created by linking aggregated population health data with patient addresses in EHRs. These linked data, once imported into EHRs, are a readily available resource to help primary care practices understand the context in which their patients reside and achieve important health goals at the patient, population, and policy levels., (© Copyright 2016 by the American Board of Family Medicine.)

Objective: The future of the Children's Health Insurance Program (CHIP) is uncertain after 2017. Survey-based research shows positive associations between CHIP expansions and children's healthcare utilization. To build on this prior work, we used electronic health record (EHR) data to assess temporal patterns of healthcare utilization after Oregon's 2009-2010 CHIP expansion. We hypothesized increased post-expansion utilization among children who gained public insurance., Methods: Using EHR data from 154 Oregon community health centers, we conducted a retrospective cohort study of pediatric patients (2-18 years old) who gained public insurance coverage during the Oregon expansion (n = 3054), compared to those who were continuously publicly insured (n = 10,946) or continuously uninsured (n = 10,307) during the 2-year study period. We compared pre-post rates of primary care visits, well-child visits, and dental visits within- and between-groups. We also conducted longitudinal analysis of monthly visit rates, comparing the three insurance groups., Results: After Oregon's 2009-2010 CHIP expansions, newly insured patients' utilization rates were more than double their pre-expansion rates [adjusted rate ratios (95 % confidence intervals); increases ranged from 2.10 (1.94-2.26) for primary care visits to 2.77 (2.56-2.99) for dental visits]. Utilization among the newly insured spiked shortly after coverage began, then leveled off, but remained higher than the uninsured group., Conclusions: This study used EHR data to confirm that CHIP expansions are associated with increased utilization of essential pediatric primary and preventive care. These findings are timely to pending policy decisions that could impact children's access to public health insurance in the United States.

Social determinants of health significantly impact morbidity and mortality; however, physicians lack ready access to this information in patient care and population management. Just as traditional vital signs give providers a biometric assessment of any patient, "community vital signs" (Community VS) can provide an aggregated overview of the social and environmental factors impacting patient health. Knowing Community VS could inform clinical recommendations for individual patients, facilitate referrals to community services, and expand understanding of factors impacting treatment adherence and health outcomes. This information could also help care teams target disease prevention initiatives and other health improvement efforts for clinic panels and populations. Given the proliferation of big data, geospatial technologies, and democratization of data, the time has come to integrate Community VS into the electronic health record (EHR). Here, the authors describe (i) historical precedent for this concept, (ii) opportunities to expand upon these historical foundations, and (iii) a novel approach to EHR integration., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Context: Guideline-discordant imaging to evaluate incident low back pain is common., Objective: We compared rates of guideline-discordant imaging in patients with low back pain in two care delivery systems with differing abilities to track care through an electronic health record (EHR), and in their patients' insurance status, to measure the association between these factors and rates of ordered low back imaging., Design: We used data from two Kaiser Permanente (KP) Regions and from OCHIN, a community health center network. We extracted data on imaging performed after index visits for low back pain from June 1, 2011, to May 31, 2012, in these systems. Adjusted logistic regression measured associations between system-level factors and imaging rates., Main Outcome Measures: Imaging rates for incident low back pain using 2 national quality metrics: Clinical Quality Measure 0052, a measure for assessing Meaningful Use of EHRs, and the Healthcare Effectiveness Data and Information Set measure "Use of Imaging Studies for Low Back Pain.", Results: Among 19,503 KP patients and 2694 OCHIN patients with incident low back pain, ordered imaging was higher among men and whites but did not differ across health care systems. OCHIN's publicly insured patients had higher rates of imaging compared with those with private or no insurance., Conclusion: Rates of ordered imaging to evaluate incident low back pain among uninsured OCHIN patients were lower than in KP overall; among insured OCHIN patients, rates were higher than in KP overall. Research is needed to establish causality and develop interventions.

Background and Objectives: Health information technology (health IT) and health technology, more broadly, offer tremendous promise for connecting, synthesizing, and sharing information critical to improving health care delivery, reducing health system costs, and achieving personal and community health. While efforts to spur adoption of electronic health records (EHRs) among US practices and hospitals have been highly successful, aspirations for effective data exchanges and translation of data into measureable improvements in health outcomes remain largely unrealized. There are shining examples of health enhancement through new technologies, and the discipline of family medicine is well poised to take advantage of these innovations to improve patient and population health. The Future of Family Medicine led to important family medicine health IT initiatives over the past decade. For example, the American Academy of Family Physicians (AAFP) Center for Health Information Technology and the Robert Graham Center provided important leadership for informing health IT policy and standard-setting, such as the Centers for Medicare and Medicaid Services EHR incentives programs (often referred to as "meaningful use."). As we move forward, there is a need for a new and more comprehensive family medicine strategy for technology. To inform the Family Medicine for America's Health (FMAHealth) initiative, this paper explores strategies and tactics that family medicine could pursue to improve the utility of technology for primary care and to help primary care become a leader in rapid development, testing, and implementation of new technologies. These strategies were also designed with a broader stakeholder audience in mind, intending to reach beyond the work being done by FMAHealth. Specific suggestions include: a shared primary care health IT center, meaningful primary care quality measures and capacity to assess/report them, increased primary care technology research, a national family medicine registry, enhancement of family physicians' technology leadership, and championing patient-centered technology functionality.

Objective: To validate electronic health record (EHR) insurance information for low-income pediatric patients at Oregon community health centers (CHCs), compared to reimbursement data and Medicaid coverage data., Materials and Methods: Subjects Children visiting any of 96 CHCs (N = 69 189) from 2011 to 2012. Analysis The authors measured correspondence (whether or not the visit was covered by Medicaid) between EHR coverage data and (i) reimbursement data and (ii) coverage data from Medicaid., Results: Compared to reimbursement data and Medicaid coverage data, EHR coverage data had high agreement (87% and 95%, respectively), sensitivity (0.97 and 0.96), positive predictive value (0.88 and 0.98), but lower kappa statistics (0.32 and 0.49), specificity (0.27 and 0.60), and negative predictive value (0.66 and 0.45). These varied among clinics., Discussion/conclusions: EHR coverage data for children had a high overall correspondence with Medicaid data and reimbursement data, suggesting that in some systems EHR data could be utilized to promote insurance stability in their patients. Future work should attempt to replicate these analyses in other settings., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Evidence-based preventive services for early detection of cancer and other health conditions offer profound health benefits, yet Americans receive only half of indicated services. Policy initiatives promote the adoption of information technologies to engage patients in care. We developed a theory-driven interactive preventive health record (IPHR) to engage patients in health promotion. The model defines five levels of functionality: (1) collecting patient information, (2) integrating with electronic health records (EHRs), (3) translating information into lay language, (4) providing individualized, guideline-based clinical recommendations, and (5) facilitating patient action. It is hypothesized that personal health records (PHRs) with these higher levels of functionality will inform and activate patients in ways that simpler PHRs cannot. However, realizing this vision requires both technological advances and effective implementation based upon clinician and practice engagement., Methods/design: We are starting a two-phase, mixed-method trial to evaluate whether the IPHR is scalable across a large number of practices and how its uptake differs for minority and disadvantaged patients. In phase 1, 40 practices from three practice-based research networks will be randomized to add IPHR functionality to their PHR versus continue to use their existing PHR. Throughout the study, we will engage intervention practices to locally tailor IPHR content and learn how to integrate new functions into their practice workflow. In phase 2, the IPHR to all nonintervention practices to observe whether the IPHR can be implemented more broadly (Scalability). Phase 1 will feature an implementation assessment in intervention practices, based on the RE-AIM model, to measure Reach (creation of IPHR accounts by patients), Adoption (practice decision to use the IPHR), Implementation (consistency, fidelity, barriers, and facilitators of use), and Maintenance (sustained use). The incremental effect of the IPHR on receipt of cancer screening tests and shared decision-making compared to traditional PHRs will assess Effectiveness. In phase 2, we will assess similar outcomes as phase 1 except for effectiveness., Discussion: This study will yield information about the effectiveness of new health information technologies designed to actively engage patients in their care as well as information about how to effectively implement and disseminate PHRs by engaging clinicians., Trial Registration: ClinicalTrials.gov: NCT02138448.

The objective of this study was to develop methodologies for creating child-parent 'links' in two healthcare-related data sources. We linked children and parents who were patients in a network of Oregon clinics with a shared electronic health record (EHR), using data that reported the child's emergency contact information or the 'guarantor' for the child's visits. We also linked children and parents enrolled in the Oregon Health Plan (OHP; Oregon's public health insurance programs), using administrative data; here, we defined a 'child' as aged <19 years and identified potential 'parents' from among adults sharing the same OHP household identification (ID) number. In both data sources, parents had to be 12-55 years older than the child. We used OHP individual client ID and EHR patient ID numbers to assess the quality of our linkages through cross-validation. Of the 249,079 children in the EHR dataset, we identified 62,967 who had a 'linkable' parent with patient information in the EHR. In the OHP data, 889,452 household IDs were assigned to at least one child; 525,578 with a household ID had a 'linkable' parent (272,578 households). Cross-validation of linkages revealed 99.8 % of EHR links validated in OHP data and 97.7 % of OHP links validated in EHR data. The ability to link children and their parents in healthcare-related datasets will be useful to inform efforts to improve children's health. Thus, we developed strategies for linking children with their parents in an EHR and a public health insurance administrative dataset.

Objective: This study compared the preventive service utilization of uninsured patients receiving care at Oregon community health centers (CHCs) in 2008 through 2011 with that of continuously insured patients at the same CHCs in the same period, using electronic health record (EHR) data., Methods: We performed a retrospective cohort analysis, using logistic mixed effects regression modeling to calculate odds ratios and rates of preventive service utilization for patients without insurance, or with continuous insurance., Results: CHCs provided many preventive services to uninsured patients. Uninsured patients were less likely than continuously insured patients to receive 5 of 11 preventive services, ranging from OR 0.52 (95% CI: 0.35-0.77) for mammogram orders to 0.75 (95% CI: 0.66-0.86) for lipid panels. This disparity persisted even in patients who visited the clinic regularly., Conclusion: Lack of insurance is a barrier to preventive service utilization, even in patients who can access care at a CHC. Policymakers in the United States should continue to address this significant prevention disparity., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The ADVANCE (Accelerating Data Value Across a National Community Health Center Network) clinical data research network (CDRN) is led by the OCHIN Community Health Information Network in partnership with Health Choice Network and Fenway Health. The ADVANCE CDRN will 'horizontally' integrate outpatient electronic health record data for over one million federally qualified health center patients, and 'vertically' integrate hospital, health plan, and community data for these patients, often under-represented in research studies. Patient investigators, community investigators, and academic investigators with diverse expertise will work together to meet project goals related to data integration, patient engagement and recruitment, and the development of streamlined regulatory policies. By enhancing the data and research infrastructure of participating organizations, the ADVANCE CDRN will serve as a 'community laboratory' for including disadvantaged and vulnerable patients in patient-centered outcomes research that is aligned with the priorities of patients, clinics, and communities in our network., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

To compare the agreement of electronic health record (EHR) data versus Medicaid claims data in documenting adult preventive care. Insurance claims are commonly used to measure care quality. EHR data could serve this purpose, but little information exists about how this source compares in service documentation. For 13 101 Medicaid-insured adult patients attending 43 Oregon community health centers, we compared documentation of 11 preventive services, based on EHR versus Medicaid claims data. Documentation was comparable for most services. Agreement was highest for influenza vaccination (κ =  0.77; 95% CI 0.75 to 0.79), cholesterol screening (κ = 0.80; 95% CI 0.79 to 0.81), and cervical cancer screening (κ = 0.71; 95% CI 0.70 to 0.73), and lowest on services commonly referred out of primary care clinics and those that usually do not generate claims. EHRs show promise for use in quality reporting. Strategies to maximize data capture in EHRs are needed to optimize the use of EHR data for service documentation., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Objective: To evaluate selected Children's Health Insurance Program Reauthorization Act claims-based quality measures using claims data alone, electronic health record (EHR) data alone, and both data sources combined., Methods: Our population included pediatric patients from 46 clinics in the OCHIN network of community health centers, who were continuously enrolled in Oregon's public health insurance program during 2010. Within this population, we calculated selected pediatric care quality measures according to the Children's Health Insurance Program Reauthorization Act technical specifications within administrative claims. We then calculated these measures in the same cohort, by using EHR data, by using the technical specifications plus clinical data previously shown to enhance capture of a given measure. We used the k statistic to determine agreement in measurement when using claims versus EHR data. Finally, we measured quality of care delivered to the study population, when using a combined dataset of linked, patient-level administrative claims and EHR data., Results: When using administrative claims data, 1.0% of children (aged 3-17) had a BMI percentile recorded, compared with 71.9% based on the EHR data (k agreement [k] # 0.01), and 72.0% in the combined dataset. Among children turning 2 in 2010, 20.2% received all recommended immunizations according to the administrative claims data, 17.2% according to the EHR data (k = 0.82), and 21.4% according to the combined dataset., Conclusions: Children's care quality measures may not be accurate when assessed using only administrative claims. Adding EHR data to administrative claims data may yield more complete measurement.

Background: Colorectal-cancer is a leading cause of cancer death in the United States, and Latinos have particularly low rates of screening. Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) is a partnership among two research institutions and a network of safety net clinics to promote colorectal cancer screening among populations served by these clinics. This paper reports on results of a pilot study conducted in a safety net organization that serves primarily Latinos., Methods: The study assessed two clinic-based approaches to raise rates of colorectal-cancer screening among selected age-eligible patients not up-to-date with colorectal-cancer screening guidelines. One clinic each was assigned to: (1) an automated data-driven Electronic Health Record (EHR)-embedded program for mailing Fecal Immunochemical Test (FIT) kits (Auto Intervention); or (2) a higher-intensity program consisting of a mailed FIT kit plus linguistically and culturally tailored interventions delivered at the clinic level (Auto Plus Intervention). A third clinic within the safety-net organization was selected to serve as a passive control (Usual Care). Two simple measurements of feasibility were: 1) ability to use real-time EHR data to identify patients eligible for each intervention step, and 2) ability to offer affordable testing and follow-up care for uninsured patients., Results: The study was successful at both measurements of feasibility. A total of 112 patients in the Auto clinic and 101 in the Auto Plus clinic met study inclusion criteria and were mailed an introductory letter. Reach was high for the mailed component (92.5% of kits were successfully mailed), and moderate for the telephone component (53% of calls were successful completed). After exclusions for invalid address and other factors, 206 (109 in the Auto clinic and 97 in the Auto Plus clinic) were mailed a FIT kit. At 6 months, fecal test completion rates were higher in the Auto (39.3%) and Auto Plus (36.6%) clinics compared to the usual-care clinic (1.1%)., Conclusions: Findings showed that the trial interventions delivered in a safety-net setting were both feasible and raised rates of colorectal-cancer screening, compared to usual care. Findings from this pilot will inform a larger pragmatic study involving multiple clinics., Trial Registration: ClinicalTrial.gov: NCT01742065.

Objective: Health insurance options are changing. Electronic health record (EHR) databases present new opportunities for providers to track the insurance coverage status of their patients. This study demonstrates the use of EHR data for this purpose., Methods: Using EHR data from the OCHIN Network of community health centers, we conducted a retrospective cohort study of data from children presenting to a community health center in 2010-2011 (N = 185,959). We described coverage patterns for children, used generalized estimating equation logistic regression to compare uninsured children with those with insurance, and assessed insurance status at subsequent visits., Results: At their first visit during the study period, 21% of children had no insurance. Among children uninsured at a first visit, 30% were uninsured at all subsequent visits. In multivariable analyses (including gender, age, race, ethnicity, language, income, location, and type of clinic), we observed significant differences in the characteristics of children who were uninsured as compared with those with insurance coverage. For example, compared with white, non-Hispanic children, nonwhite and/or Hispanic children had lower odds of being uninsured than having Medicaid/Medicare (adjusted odds ratio, 0.73; 95% confidence interval: 0.71-0.75) but had higher odds of being uninsured than having commercial insurance (adjusted odds ratio, 1.50; 95% confidence interval: 1.44-1.56)., Conclusions: Nearly one-third of children uninsured at their first visit remained uninsured at all subsequent visits, which suggests a need for clinics to conduct insurance surveillance and develop mechanisms to assist patients with obtaining coverage. EHRs can facilitate insurance surveillance and inform interventions aimed at helping patients obtain and retain coverage.

This case study describes how we are translating a diabetes care quality improvement initiative from an insured (HMO) setting into federally qualified health centers (FQHCs). We outline the innovative collaborative processes whereby researchers and FQHC providers adapted this initiative, which includes health information technology tools, to meet the FQHCs' needs.

The Children's Health Insurance Program Reauthorization Act of 2009 (CHIPRA) includes provisions for identifying standardized pediatric care quality measures. These 24 "CHIPRA measures" were designed to be evaluated by using claims data from health insurance plan populations. Such data have limited ability to evaluate population health, especially among uninsured people. The rapid expansion of data from electronic health records (EHRs) may help address this limitation by augmenting claims data in care quality assessments. We outline how to operationalize many of the CHIPRA measures for application in EHR data through a case study of a network of >40 outpatient community health centers in 2009-2010 with a single EHR. We assess the differences seen when applying the original claims-based versus adapted EHR-based specifications, using 2 CHIPRA measures (Chlamydia screening among sexually active female patients; BMI percentile documentation) as examples. Sixteen of the original CHIPRA measures could feasibly be evaluated in this dataset. Three main adaptations were necessary (specifying a visit-based population denominator, calculating some pregnancy-related factors by using EHR data, substituting for medication dispense data). Although it is feasible to adapt many of the CHIPRA measures for use in outpatient EHR data, information is gained and lost depending on how numerators and denominators are specified. We suggest first steps toward application of the CHIPRA measures in uninsured populations, and in EHR data. The results highlight the importance of considering the limitations of the original CHIPRA measures in care quality evaluations.

In 2001, community health center (CHC) leaders in Oregon established an organization to facilitate the integration of health information technology, including a shared electronic health record (EHR), into safety net clinics. The Oregon Community Health Information Network (shortened to OCHIN as other states joined) became a CHC information technology hub, supporting a network-wide EHR with one master patient index, now linked across >40 safety net organizations serving >900,000 patients with nearly 800,000 distinct CHC visits. Recognizing the potential of OCHIN's multiclinic network and comprehensive EHR database for conducting safety net-based research, OCHIN leaders and local researchers formed the Safety Net West practice-based research network (PBRN). The Safety Net West "community- based laboratory," based at OCHIN, is positioned to become an important resource for many studies including: evaluation of the real-time impact of health care reform on uninsured populations; development of new models of primary care delivery; dissemination and translation of interventions from other EHR-based systems (e.g., Kaiser Permanente) into the community health setting; and analyses of factors influencing disparities in health and health care access. We describe the founding of Safety Net West, its infrastructure development, current projects, and the future goals of this community-based PBRN with a common EHR.

Purpose: Electronic Health Record (EHR) databases in community health centers (CHCs) present new opportunities for quality improvement, comparative effectiveness, and health policy research. We aimed (1) to create individual-level linkages between EHR data from a network of CHCs and Medicaid claims from 2005 through 2007; (2) to examine congruence between these data sources; and (3) to identify sociodemographic characteristics associated with documentation of services in one data set vs the other., Methods: We studied receipt of preventive services among established diabetic patients in 50 Oregon CHCs who had ever been enrolled in Medicaid (N = 2,103). We determined which services were documented in EHR data vs in Medicaid claims data, and we described the sociodemographic characteristics associated with these documentation patterns., Results: In 2007, the following services were documented in Medicaid claims but not the EHR: 11.6% of total cholesterol screenings received, 7.0% of total influenza vaccinations, 10.5% of nephropathy screenings, and 8.8% of tests for glycated hemoglobin (HbA(1c)). In contrast, the following services were documented in the EHR but not in Medicaid claims: 49.3% of cholesterol screenings, 50.4% of influenza vaccinations, 50.1% of nephropathy screenings, and 48.4% of HbA(1c) tests. Patients who were older, male, Spanish-speaking, above the federal poverty level, or who had discontinuous insurance were more likely to have services documented in the EHR but not in the Medicaid claims data., Conclusions: Networked EHRs provide new opportunities for obtaining more comprehensive data regarding health services received, especially among populations who are discontinuously insured. Relying solely on Medicaid claims data is likely to substantially underestimate the quality of care.

Several recent national initiatives have called for increased efforts to integrate social determinants of health (SDH) into health care settings using health information technology (HIT). However, there is limited evidence to guide the implementation of these recommendations in practice. Research is needed to understand what SDH information is most important to collect, how SDH information can be used to inform clinical care and referrals, and ultimately, whether and how integrating SDH screening and action into primary care affects individual and population health. We recently proposed a conceptual framework to illustrate how HIT can be used to bring SDH information into primary care. In this paper, we describe how we are putting this conceptual model into practice within the OCHIN network of community health centers by highlighting examples of ongoing research, identifying knowledge gaps, and outlining a roadmap of future research to move the field forward.

Aims: Children of parents with substance use and/or other mental health (SU/MH) diagnoses are at increased risk for health problems. It is unknown whether these children benefit from receiving primary care at the same clinic as their parents. Thus, among children of parents with >1 SU/MH diagnosis, we examined the association of parent-child clinic concordance with rates of well-child checks (WCCs) and childhood vaccinations. Design: Retrospective cohort study using electronic health record (EHR) data from the OCHIN network of community health organizations (CHOs), 2010-2018. Setting: 280 CHOs across 17 states. Participants/Cases: 41,413 parents with >1 SU/MH diagnosis, linked to 65,417 children aged 0 to 17 years, each with >1 visit to an OCHIN clinic during the study period. Measurements: Dependent variables: rates of WCCs during (1) the first 15 months of life, and (2) ages 3 to 17 years; vaccine completeness (3) by the age of 2, and (4) before the age of 18. Estimates were attained using generalized estimating equations Poisson or logistic regression. Findings: Among children utilizing the same clinic as their parent versus children using a different clinic (reference group), we observed greater WCC rates in the first 15 months of life [adjusted rate ratio (aRR) = 1.06; 95% confidence interval (CI) = 1.02-1.10]; no difference in WCC rates in ages 3 to 17; higher odds for vaccine completion before age 2 [adjusted odds ratio (aOR) = 1.12; 95% CI = 1.03-1.21]; and lower odds for vaccine completion before age 18 (aOR = 0.88; 95% CI = 0.81-0.95). Conclusion: Among children whose parents have at least one SU/MH diagnosis, parent-child clinic concordance was associated with greater rates of WCCs and higher odds of completed vaccinations for children in the youngest age groups, but not the older children. This suggests the need for greater emphasis on family-oriented healthcare for young children of parents with SU/MH diagnoses; this may be less important for older children. [ABSTRACT FROM AUTHOR]

As recent extreme weather events demonstrate, climate change presents unprecedented and increasing health risks, disproportionately so for disadvantaged communities in the U.S. already experiencing health disparities. As patients in these frontline communities live through extreme weather events, socioeconomic and health stressors are compounded; thus, their healthcare teams will need tools to provide precision ecologic medicine approaches to their care. Many primary care teams are taking actionable steps to bring community-level socioeconomic data ("community vital signs") into electronic medical records, to facilitate tailoring care based on a given patient's circumstances. This work can be extended to include environmental risk data, thus equipping healthcare teams with an awareness of clinical and community vital signs and making them better positioned to mitigate climate impacts on health. For example, if healthcare teams can easily identify patients who have multiple chronic conditions and live in an urban heat island, they can proactively arrange to "prescribe" an air conditioner, heat pump, and/or air purifier. Or, when a severe storm/heat event/poor air quality event is predicted, they can take preemptive steps to get help to patients at high medical and socioeconomic risk, rather than waiting for them to arrive in the emergency department. Advances in health information technologies now make it technically feasible to integrate a wealth of publicly-available community-level data into EMRs. Efforts to bring this contextual data into clinical settings must be accelerated to equip healthcare teams to provide precision ecologic medicine interventions to their patients. [ABSTRACT FROM AUTHOR]

Background: In 2019–2020, with National Cancer Institute funding, seven implementation laboratory (I-Lab) partnerships between scientists and stakeholders in 'real-world' settings working to implement evidence-based interventions were developed within the Implementation Science Centers in Cancer Control (ISC3) consortium. This paper describes and compares approaches to the initial development of seven I-Labs in order to gain an understanding of the development of research partnerships representing various implementation science designs. Methods: In April-June 2021, members of the ISC3 Implementation Laboratories workgroup interviewed research teams involved in I-Lab development in each center. This cross-sectional study used semi-structured interviews and case-study-based methods to collect and analyze data about I-Lab designs and activities. Interview notes were analyzed to identify a set of comparable domains across sites. These domains served as the framework for seven case descriptions summarizing design decisions and partnership elements across sites. Results: Domains identified from interviews as comparable across sites included engagement of community and clinical I-Lab members in research activities, data sources, engagement methods, dissemination strategies, and health equity. The I-Labs use a variety of research partnership designs to support engagement including participatory research, community-engaged research, and learning health systems of embedded research. Regarding data, I-Labs in which members use common electronic health records (EHRs) leverage these both as a data source and a digital implementation strategy. I-Labs without a shared EHR among partners also leverage other sources for research or surveillance, most commonly qualitative data, surveys, and public health data systems. All seven I-Labs use advisory boards or partnership meetings to engage with members; six use stakeholder interviews and regular communications. Most (70%) tools or methods used to engage I-Lab members such as advisory groups, coalitions, or regular communications, were pre-existing. Think tanks, which two I-Labs developed, represented novel engagement approaches. To disseminate research results, all centers developed web-based products, and most (n = 6) use publications, learning collaboratives, and community forums. Important variations emerged in approaches to health equity, ranging from partnering with members serving historically marginalized populations to the development of novel methods. Conclusions: The development of the ISC3 implementation laboratories, which represented a variety of research partnership designs, offers the opportunity to advance understanding of how researchers developed and built partnerships to effectively engage stakeholders throughout the cancer control research lifecycle. In future years, we will be able to share lessons learned for the development and sustainment of implementation laboratories. [ABSTRACT FROM AUTHOR]

Parent and child vaccination behavior is related for human papillomavirus (HPV) and flu vaccine. Thus, it is likely that parental vaccination status is also associated with their children's adherence to guideline-concordant childhood vaccination schedules. We hypothesized that parent influenza (flu) vaccination would be associated with their child's vaccination status at age two. We used electronic health record data to identify children and linked parents seen in a community health center (CHC) within the OCHIN network (292 CHCs in 16 states). We randomly selected a child aged <2 years with ≥1 ambulatory visit between 2009-2018. Employing a retrospective, cohort study design, we used general estimating equations logistic regression to estimate the odds of a child being up-to-date on vaccinations based on their linked parents' flu vaccination status. We adjusted for relevant parent and child covariates and stratified by mother only, father only, and two-parent samples. The study included 40,007 family-units: mother only = 35,444, father only = 2,784, and two parents = 1,779. A higher percentage of children were fully vaccinated if their parent or parents received a flu vaccine. Children in the two-parent sample whose parents both received a flu vaccine had more than twice the odds of being fully vaccinated, and two and a half times the odds of being fully vaccinated except flu vaccine compared to children with two parents who did not receive a flu vaccine (covariate-adjusted odds ratio [aOR] = 2.39, 95% CI = 1.67, 3.43 and aOR = 2.54, 95% CI = 1.54, 4.19, respectively). Parent flu vaccination is associated with routine child vaccination. Future research is needed to understand if this relationship persists over time and in different settings. [ABSTRACT FROM AUTHOR]

Background: Many cancer survivors receive primary care in community health centers (CHCs). Cancer history is an important factor to consider in the provision of primary care, yet little is known about the completeness or accuracy of cancer history data contained in CHC electronic health records (EHRs).Methods: We probabilistically linked EHR data from more than1.5 million adult CHC patients to state cancer registries in California, Oregon, and Washington and estimated measures of agreement (eg, kappa, sensitivity, specificity). We compared demographic and clinical characteristics of cancer patients as estimated by each data source, evaluating distributional differences with absolute standardized mean differences.Results: A total 74 707 cancer patients were identified between the 2 sources (EHR only, n = 22 730; registry only, n = 23 616; both, n = 28 361). Nearly one-half of cancer patients identified in registries were missing cancer documentation in the EHR. Overall agreement of cancer ascertainment in the EHR vs cancer registries (gold standard) was moderate (kappa = 0.535). Cancer site-specific agreement ranged from substantial (eg, prostate and female breast; kappa > 0.60) to fair (melanoma and cervix; kappa < 0.40). Comparing population characteristics of cancer patients as ascertained from each data source, groups were similar for sex, age, and federal poverty level, but EHR-recorded cases showed greater medical complexity than those ascertained from cancer registries.Conclusions: Agreement between EHR and cancer registry data was moderate and varied by cancer site. These findings suggest the need for strategies to improve capture of cancer history information in CHC EHRs to ensure adequate delivery of care and optimal health outcomes for cancer survivors. [ABSTRACT FROM AUTHOR]

Objective: We sought to understand how Affordable Care Act (ACA) Medicaid expansion insurance coverage gains are associated with changes in diabetes-related biomarkers.Research Design and Methods: This was a retrospective observational cohort study using electronic health record data from 178 community health centers (CHCs) in the ADVANCE (Accelerating Data Value Across a National Community Health Center Network) network. We assessed changes in diabetes-related biomarkers among adult patients with diabetes in 10 Medicaid expansion states (n = 25,279), comparing newly insured with continuously insured, discontinuously insured, and continuously uninsured patients pre- to post-ACA expansion. Primary outcomes included changes from 24 months pre- to 24 months post-ACA in glycosylated hemoglobin (HbA1c), systolic (SBP) and diastolic (DBP) blood pressure, and LDL cholesterol levels.Results: Newly insured patients exhibited a reduction in adjusted mean HbA1c levels (8.24% [67 mmol/mol] to 8.17% [66 mmol/mol]), which was significantly different from continuously uninsured patients, whose HbA1c levels increased (8.12% [65 mmol/mol] to 8.29% [67 mmol/mol]; difference-in-differences [DID] -0.24%; P < 0.001). Newly insured patients showed greater reductions than continuously uninsured patients in adjusted mean SBP (DID -1.8 mmHg; P < 0.001), DBP (DID -1.0 mmHg; P < 0.001), and LDL (DID -3.3 mg/dL; P < 0.001). Among patients with elevated HbA1c in the 3 months prior to expansion, newly insured patients were more likely than continuously uninsured patients to have a controlled HbA1c measurement by 24 months post-ACA (hazard ratio 1.25; 95% CI 1.02-1.54].Conclusions: Post-ACA, newly insured patients had greater improvements in diabetes-related biomarkers than continuously uninsured, discontinuously insured, or continuously insured patients. Findings suggest that health insurance gain via ACA facilitates access to appropriate diabetes care, leading to improvements in diabetes-related biomarkers. [ABSTRACT FROM AUTHOR]

Background: Understanding the impact of health insurance is critical, particularly in the era of Affordable Care Act Medicaid expansion. The electronic health record (EHR) provides new opportunities to quantify health outcomes.Objective: To assess changes in biomarkers of chronic disease among community health center (CHC) patients who gained Medicaid coverage with the Oregon Medicaid expansion (2008-2011).Design: Prospective cohort. Patients were followed for 24 months, and rate of mean biomarker change was calculated. Time to a controlled follow-up measurement was compared using Cox regression models.Setting/patients: Using EHR data from OCHIN (a non-profit network of CHCs) linked to state Medicaid data, we identified three cohorts of patients with uncontrolled chronic conditions (diabetes, hypertension, and hyperlipidemia). Within these cohorts, we included patients who gained Medicaid coverage along with a propensity score-matched comparison group who remained uninsured (diabetes n = 608; hypertension n = 1244; hyperlipidemia n = 546).Main Measures: Hemoglobin A1c (HbA1c) for the diabetes cohort, systolic and diastolic blood pressure (SBP and DBP, respectively) for the hypertension cohort, and low-density lipoprotein (LDL) for the hyperlipidemia cohort.Key Results: All cohorts improved over time. Compared to matched uninsured patients, adults in the diabetes and hypertension cohorts who gained Medicaid coverage were significantly more likely to have a follow-up controlled measurement (hazard ratio [HR] =1.26, p = 0.020; HR = 1.35, p < 0.001, respectively). No significant difference was observed in the hyperlipidemia cohort (HR = 1.09, p = 0.392).Conclusions: OCHIN patients with uncontrolled chronic conditions experienced objective health improvements over time. In two of three chronic disease cohorts, those who gained Medicaid coverage were more likely to achieve a controlled measurement than those who remained uninsured. These findings demonstrate the effective care provided by CHCs and the importance of health insurance coverage within a usual source of care setting.Clinical Trials Registration: NCT02355132 [ https://clinicaltrials.gov/ct2/show/NCT02355132 ]. [ABSTRACT FROM AUTHOR]

Background: Primary care providers must understand the use patterns, clinical complexity, and primary care needs of cancer survivors to provide quality health care services. However, to the authors' knowledge, little is known regarding the prevalence and health care needs of this growing population, particularly in safety net settings.Methods: The authors identified adults with a history of cancer documented in primary care electronic health records within a network of community health centers (CHCs) in 19 states. The authors estimated cancer history prevalence among >1.2 million patients and compared sex-specific site distributions with national estimates. Each survivor was matched to 3 patients without cancer from the same set of clinics. The demographic characteristics, primary care use, and comorbidity burden then were compared between the 2 groups, assessing differences with absolute standardized mean differences (ASMDs). ASMD values >0.1 denote meaningful differences between groups. Generalized estimating equations yielded adjusted odds ratios (aORs) for select indicators.Results: A total of 40,266 cancer survivors were identified (prevalence of 3.0% of adult CHC patients). Compared with matched cancer-free patients, a higher percentage of survivors had ≥6 primary care visits across 3 years (62% vs 48%) and were insured (83% vs 74%) (ASMD, >0.1 for both). Cancer survivors had excess medical complexity, including a higher prevalence of depression, asthma/chronic obstructive pulmonary disease, and liver disease (ASMD, >0.1 for all). Survivors had higher odds of any opioid prescription (aOR, 1.23; 95% CI, 1.19-1.27) and chronic opioid therapy (aOR, 1.27; 95% CI, 1.23-1.32) compared with matched controls (P < .001 for all).Conclusions: Identifying cancer survivors and understanding their patterns of utilization and physical and mental comorbidities present an opportunity to tailor primary health care services to this population. [ABSTRACT FROM AUTHOR]

Community-level factors have significant impacts on health. There is renewed enthusiasm for integrating these data with electronic health record (EHR) data for use in primary care to improve health equity in the USA. Thus, it is valuable to reflect on what has been published to date. Specifically, we comment on: (1) recommendations about combining community-level factors in EHRs for use in primary care; (2) examples of how these data have been combined and used; and (3) the impact of using combined data on healthcare, patient health and health equity. We found publications discussing the potential of combined data to inform clinical care, target interventions, track population health and spark community partnerships with the goal of reducing health disparities and improving health equity. Although there is great enthusiasm and potential for using these data to inform primary care, there is little evidence of improved healthcare, patient health or health equity. [ABSTRACT FROM AUTHOR]

Background: Spreading effective, guideline-based cardioprotective care quality improvement strategies between healthcare settings could yield great benefits, particularly in under-resourced contexts. Understanding the diverse factors facilitating or impeding such guideline implementation could improve cardiovascular care quality and outcomes for vulnerable patients.Methods: We sought to identify multi-level factors affecting uptake of cardioprotective care guidelines in community health centers (CHCs), within a successful trial of cross-setting implementation of an effective intervention. Quantitative analyses used multivariable logistic regression to examine in-person patient encounters at 10 CHCs from June 2011-May 2014. At these encounters, a point-of-care alert flagged adults with diabetes who were clinically indicated for, but not currently prescribed, cardioprotective medications. The main outcome measure was the rate of relevant prescriptions issued within two days of encounters. Qualitative analyses focused on CHC providers and staff, and, guided by the constant comparative method, were used to enhance understanding of the factors that influenced this prescribing.Results: Recommended prescribing occurred at 13-16% of encounters with patients who were indicated for such prescribing. The odds of this prescribing were higher when the patient was male, had HbA1c ≥7, was previously prescribed a similar medication, gave diabetes as the chief complaint, saw a mid-level practitioner, or saw their primary care provider. The odds were lower when the patient was insured, had ≥1 clinic visits in the past year, had kidney disease, or was prescribed certain other medications. Additional factors were associated with prescribing of each medication class. Qualitative results both supported and challenged the quantitative findings, illustrating important tensions involved in guideline-based prescribing. Clinic staff stressed the importance of the provider-patient relationship in guiding prescribing decisions in the face of competing priorities and care needs, and the impact of rapidly changing guidelines.Conclusions: Diverse factors associated with guideline-concordant prescribing illuminate the complexity of delivering evidence-based care in CHCs. We present possible strategies for addressing barriers to guideline-based prescribing.Clinical Trials Registration: This trial was registered retrospectively. Currently Controlled Trials NCT02299791 . Retrospectively registered 10 November 2014. [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]

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]

Maintaining continuous health insurance coverage is important. With recent expansions in access to coverage in the United States after "Obamacare," primary care teams have a new role in helping to track and improve coverage rates and to provide outreach to patients. We describe efforts to longitudinally track health insurance rates using data from the electronic health record (EHR) of a primary care network and to use these data to support practice-based insurance outreach and assistance. Although we highlight a few examples from one network, we believe there is great potential for doing this type of work in a broad range of family medicine and community health clinics that provide continuity of care. By partnering with researchers through practice-based research networks and other similar collaboratives, primary care practices can greatly expand the use of EHR data and EHR-based tools targeting improvements in health insurance and quality health care. [ABSTRACT FROM AUTHOR]

Background: Little research has directly compared the effectiveness of implementation strategies in any setting, and we know of no prior trials directly comparing how effectively different combinations of strategies support implementation in community health centers. This paper outlines the protocol of the Study of Practices Enabling Implementation and Adaptation in the Safety Net (SPREAD-NET), a trial designed to compare the effectiveness of several common strategies for supporting implementation of an intervention and explore contextual factors that impact the strategies' effectiveness in the community health center setting.Methods/design: This cluster-randomized trial compares how three increasingly hands-on implementation strategies support adoption of an evidence-based diabetes quality improvement intervention in 29 community health centers, managed by 12 healthcare organizations. The strategies are as follows: (arm 1) a toolkit, presented in paper and electronic form, which includes a training webinar; (arm 2) toolkit plus in-person training with a focus on practice change and change management strategies; and (arm 3) toolkit, in-person training, plus practice facilitation with on-site visits. We use a mixed methods approach to data collection and analysis: (i) baseline surveys on study clinic characteristics, to explore how these characteristics impact the clinics' ability to implement the tools and the effectiveness of each implementation strategy; (ii) quantitative data on change in rates of guideline-concordant prescribing; and (iii) qualitative data on the "how" and "why" underlying the quantitative results. The outcomes of interest are clinic-level results, categorized using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework, within an interrupted time-series design with segmented regression models. This pragmatic trial will compare how well each implementation strategy works in "real-world" practices.Discussion: Having a better understanding of how different strategies support implementation efforts could positively impact the field of implementation science, by comparing practical, generalizable methods for implementing clinical innovations in community health centers. Bridging this gap in the literature is a critical step towards the national long-term goal of effectively disseminating and implementing effective interventions into community health centers.Trial Registration: ClinicalTrials.gov, NCT02325531. [ABSTRACT FROM AUTHOR]

Introduction: The Affordable Care Act increases health insurance options, yet many Americans may struggle to consistently maintain coverage. While health care providers have traditionally not been involved in providing insurance enrollment support to their patients, the ability for them to do so now exists. We propose that providers could capitalize on the expansion of electronic health records (EHRs) and the advances in health information exchanges (HIEs) to improve their patients' insurance coverage rates and continuity. Evidence for Argument: We describe a project in which we are building strategies for linking, and thus improving synergy between, payer and EHR data. Through this effort, care teams will have access to new automated tools and increased EHR functionality designed to help them assist their patients in obtaining and maintaining health insurance coverage. Suggestion for the Future: The convergence of increasing EHR adoption, improving HIE functionality, and expanding insurance coverage options, creates new opportunities for clinics to help their patients obtain public health insurance. Harnessing this nascent ability to exchange information between payers and providers may improve synergies between HIE and EHRs, and thus support clinic-based efforts to keep patients continuously insured. [ABSTRACT FROM AUTHOR]

The recent confluence of: (1) changing state and national insurance-related policies, and (2) the rapid growth in electronic health record (EHR) use, yields an unprecedented opportunity for patient-centered medical homes (PCMHs) and other primary care practices or care settings to use health information technology (HIT) and health information exchange (HIE) in novel ways to impact patient health. We propose that HIT is an untapped resource for supporting clinic-based efforts to help eligible patients obtain and maintain insurance coverage. This commentary presents a conceptual model and guiding principles for this idea. Additionally, it describes insurance support tools that could be used to conduct 'inreach' and 'outreach' with patients around health insurance, similar to how HIT is used to manage chronic disease and panels of patients, and to improve population health outcomes. [ABSTRACT FROM AUTHOR]

To estimate how the Affordable Care Act's Medicaid expansions will affect demand for services, we measured ambulatory care utilization among adult patients who gained insurance during Oregon's 2008 Medicaid expansion. Using electronic health record data from 67 community health centers, we assessed pre- and postcoverage utilization among patients who gained insurance, compared with patients continuously insured or uninsured. In comparisons of the pre- and postcoverage periods, mean annual encounters among persons who gained insurance increased 22% to 35%, but declined in the comparison groups. These findings suggest that providers should expect a significant increase in demand among patients who gain Medicaid coverage through the Affordable Care Act. [ABSTRACT FROM AUTHOR]

Lack of health insurance negatively impacts children's health. Despite federal initiatives to expand children's coverage and accelerate state outreach efforts, millions of US children remain uninsured or experience frequent gaps in coverage. Most current efforts to enroll and retain eligible children in public insurance programs take place outside of the health care system. This study is a partnership between patients' families, medical informaticists, federally qualified health center (FQHC) staff, and researchers to build and test information technology tools to help FQHCs reach uninsured children and those at risk for losing coverage. [ABSTRACT FROM AUTHOR]

Background: Patients with gaps in health insurance coverage often defer or forgo cancer prevention services. These delays in cancer detection and diagnoses lead to higher rates of morbidity and mortality and increased costs. Recent advances in health information technology (HIT) create new opportunities to enhance insurance support services that reduce coverage gaps through automated processes applied in healthcare settings. This study will assess the implementation of insurance support HIT tools and their effectiveness at improving patients' insurance coverage continuity and cancer screening rates.Methods/design: This study uses a hybrid cluster-randomized design-a combined effectiveness and implementation trial-in community health centers (CHCs) in the USA. Eligible CHC clinic sites will be randomly assigned to one of two groups in the trial's implementation component: tools + basic training (Arm I) and tools + enhanced training + facilitation (Arm II). A propensity score-matched control group of clinics will be selected to assess the tools' effectiveness. Quantitative analyses of the tools' impact will use electronic health record and Medicaid data to assess effectiveness. Qualitative data will be collected to evaluate the implementation process, understand how the HIT tools are being used, and identify facilitators and barriers to their implementation and use.Discussion: This study will test the effectiveness of HIT tools to enhance insurance support in CHCs and will compare strategies for facilitating their implementation in "real-world" practice settings. Findings will inform further development and, if indicated, more widespread implementation of insurance support HIT tools.Trial Registration: Clinical trial NTC02355262. [ABSTRACT FROM AUTHOR]

Introduction: Patients with multiple chronic conditions (multimorbidity) are seen commonly in primary care practices and often have suboptimal uptake of preventive care owing to competing treatment demands. The complexity of multimorbidity patterns and their impact on receiving preventive services is not fully understood. This study identifies multimorbidity combinations associated with low receipt of preventive services.Methods: This was a retrospective cohort study of U.S. community health center patients aged ≥19 years. Electronic health record data from 209 community health centers for the January 1, 2014-December 31, 2017 study period were analyzed in 2018-2019. Multimorbidity patterns included physical only, mental health only, and physical and mental health multimorbidity patterns, with no multimorbidity as a reference category. Electronic health record-based preventive ratios (number of months services were up-to-date/total months the patient was eligible for services) were calculated for the 14 preventive services. Negative binomial regression models assessed the relationship between multimorbidity physical and/or mental health patterns and the preventive ratio for each service.Results: There was a variation in receipt of preventive care between multimorbidity groups: individuals with mental health only multimorbidity were less likely to be up-to-date with cardiometabolic and cancer screenings than the no multimorbidity group or groups with physical health conditions, and the physical only multimorbidity group had low rates of depression screening.Conclusions: This study provided critical insights into receipt of preventive service among adults with multimorbidity using a more precise method for measuring up-to-date preventive care delivery. Findings would be useful to identify target populations for future intervention programs to improve preventive care. [ABSTRACT FROM AUTHOR]

Background: Lack of insurance is associated with suboptimal receipt of diabetes preventive care. One known reason for this is an access barrier to obtaining healthcare visits; however, little is known about whether insurance status is associated with differential rates of receipt of diabetes care during visits. Purpose: To examine the association between health insurance and receipt of diabetes preventive care during an office visit. Methods: This retrospective cohort study used electronic health record and Medicaid data from 38 Oregon community health centers. Logistic regression was used to test the association between insurance and receipt of four diabetes services during an office visit among patients who were continuously uninsured (n=1,117); continuously insured (n=1,466); and discontinuously insured (n=336) in 2006-2007. Generalized estimating equations were used to account for within-patient correlation. Data were analyzed in 2013. Results: Overall, continuously uninsured patients had lower odds of receiving services at visits when due, compared to those who were continuously insured (AOR=0.73, 95% CI=0.66, 0.80). Among the discontinuously insured, being uninsured at a visit was associated with lower odds of receipt of services due at that visit (AOR=0.77, 95% CI=0.64, 0.92) than being insured at a visit. Conclusions: Lack of insurance is associated with a lower probability of receiving recommended services that are due during a clinic visit. Thus, the association between being uninsured and receiving fewer preventive services may not be completely mediated by access to clinic visits. [ABSTRACT FROM AUTHOR]