"I didn't know you could do that": A Pilot Assessment of EHR Optimization Training.

BACKGROUND: Informatics tools within electronic health records (EHRs)-for example, data rosters and clinical reminders-can help disseminate care guidelines into clinical practice. Such tools' adoption varies widely, however, possibly because many primary care providers receive minimal training in even basic EHR functions. OBJECTIVES: This mixed-methods evaluation of a pilot training program sought to identify factors to consider when providing EHR use optimization training in community health centers (CHCs) as a step toward supporting CHC providers' adoption of EHR tools. METHODS: In spring 2018, we offered 10 CHCs a 2-day, 16-hour training in EHR use optimization, provided by clinician trainers, and customized to each CHC's needs. We surveyed trainees pre- and immediately post-training and again 3 months later. We conducted post-training interviews with selected clinic staff, and conducted a focus group with the trainers, to assess satisfaction with the training, and perceptions of how it impacted subsequent EHR use. RESULTS: Six CHCs accepted and received the training; 122 clinic staff members registered to attend, and most who completed the post-training survey reported high satisfaction. Three months post-training, 80% of survey respondents said the training had changed their daily EHR use somewhat or significantly. CONCLUSION: Factors to consider when planning EHR use optimization training in CHCs include: CHCs may face barriers to taking part in such training; it may be necessary to customize training to a given clinic's needs and to different trainees' clinic roles; identifying trainees' skill level a priori would help but is challenging; in-person training may be preferable; and inclusion of a practice coach may be helpful. Additional research is needed to identify how to provide such training most effectively.

Does increased implementation support improve community clinics' guideline-concordant care? Results of a mixed methods, pragmatic comparative effectiveness trial.

BACKGROUND: Disseminating care guidelines into clinical practice remains challenging, partly due to inadequate evidence on how best to help clinics incorporate new guidelines into routine care. This is particularly true in safety net community health centers (CHCs). METHODS: This pragmatic comparative effectiveness trial used a parallel mixed methods design. Twenty-nine CHC clinics were randomized to receive increasingly intensive implementation support (implementation toolkit (arm 1); toolkit + in-person training + training webinars (arm 2); toolkit + training + webinars + offered practice facilitation (arm 3)) targeting uptake of electronic health record (EHR) tools focused on guideline-concordant cardioprotective prescribing for patients with diabetes. Outcomes were compared across study arms, to test whether increased support yielded additive improvements, and with 137 non-study CHCs that share the same EHR as the study clinics. Quantitative data from the CHCs' EHR were used to compare the magnitude of change in guideline-concordant ACE/ARB and statin prescribing, using adjusted Poisson regressions. Qualitative data collected using diverse methods (e.g., interviews, observations) identified factors influencing the quantitative outcomes. RESULTS: Outcomes at CHCs receiving higher-intensity support did not improve in an additive pattern. ACE/ARB prescribing did not improve in any CHC group. Statin prescribing improved overall and was significantly greater only in the arm 1 and arm 2 CHCs compared with the non-study CHCs. Factors influencing the finding of no additive impact included: aspects of the EHR tools that reduced their utility, barriers to providing the intended implementation support, and study design elements, e.g., inability to adapt the provided support. Factors influencing overall improvements in statin outcomes likely included a secular trend in awareness of statin prescribing guidelines, selection bias where motivated clinics volunteered for the study, and study participation focusing clinic staff on the targeted outcomes. CONCLUSIONS: Efforts to implement care guidelines should: ensure adaptability when providing implementation support and conduct formative evaluations to determine the optimal form of such support for a given clinic; consider how study data collection influences adoption; and consider barriers to clinics' ability to use/accept implementation support as planned. More research is needed on supporting change implementation in under-resourced settings like CHCs. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02325531. Registered 15 December 2014.

Adoption of Social Determinants of Health EHR Tools by Community Health Centers.

PURPOSE: This pilot study assessed the feasibility of implementing electronic health record (EHR) tools for collecting, reviewing, and acting on patient-reported social determinants of health (SDH) data in community health centers (CHCs). We believe it is the first such US study. METHODS: We implemented a suite of SDH data tools in 3 Pacific Northwest CHCs in June 2016, and used mixed methods to assess their adoption through July 2017. We modified the tools at clinic request; for example, we added questions that ask if the patient wanted assistance with SDH needs. RESULTS: Social determinants of health data were collected on 1,130 patients during the study period; 97% to 99% of screened patients (n = 1,098) had ≥1 SDH need documented in the EHR, of whom 211 (19%) had an EHR-documented SDH referral. Only 15% to 21% of patients with a documented SDH need indicated wanting help. Examples of lessons learned on adoption of EHR SDH tools indicate that clinics should: consider how to best integrate tools into existing workflow processes; ensure that staff tasked with SDH efforts receive adequate tool training and access; and consider that timing of data entry impacts how and when SDH data can be used. CONCLUSIONS: Our results indicate that adoption of systematic EHR-based SDH documentation may be feasible, but substantial barriers to adoption exist. Lessons from this study may inform primary care providers seeking to implement SDH-related efforts, and related health policies. Far more research is needed to address implementation barriers related to SDH documentation in EHRs.

Developing Electronic Health Record (EHR) Strategies Related to Health Center Patients' Social Determinants of Health.

BACKGROUND: "Social determinants of heath" (SDHs) are nonclinical factors that profoundly affect health. Helping community health centers (CHCs) document patients' SDH data in electronic health records (EHRs) could yield substantial health benefits, but little has been reported about CHCs' development of EHR-based tools for SDH data collection and presentation. METHODS: We worked with 27 diverse CHC stakeholders to develop strategies for optimizing SDH data collection and presentation in their EHR, and approaches for integrating SDH data collection and the use of those data (eg, through referrals to community resources) into CHC workflows. RESULTS: We iteratively developed a set of EHR-based SDH data collection, summary, and referral tools for CHCs. We describe considerations that arose while developing the tools and present some preliminary lessons learned. CONCLUSION: Standardizing SDH data collection and presentation in EHRs could lead to improved patient and population health outcomes in CHCs and other care settings. We know of no previous reports of processes used to develop similar tools. This article provides an example of 1 such process. Lessons from our process may be useful to health care organizations interested in using EHRs to collect and act on SDH data. Research is needed to empirically test the generalizability of these lessons.

The High Prevalence of Diabetes in a Large Cohort of Patients Drawn From Safety Net Clinics.

INTRODUCTION: Underserved populations have been overlooked or underrepresented in research based on data from diabetes registries. We estimated diabetes prevalence using a cohort developed from the electronic health records of 3 networks of safety net clinics that provide care to underserved populations. METHODS: ADVANCE (Accelerating Data Value Across a National Community Health Center Network) is a partnership of the OCHIN Community Health Information Network (OCHIN), the Health Choice Network (HCN), and the Fenway Health Institute (FHI), representing 97 federally qualified health centers (FQHCs) and 744 clinic sites in 22 US states. Among 952,316 adults with a body mass index (BMI) measurement and at least 2 outpatient visits in 2012 to 2014, we calculated diabetes prevalence using outpatient diagnoses, diagnostic laboratory results, or dispenses of anti-hyperglycemic agents no more than 730 days apart. We calculated prevalence by age, sex, race, Hispanic ethnicity, and BMI class. RESULTS: The crude prevalence of diabetes was 14.4%. Men had a higher prevalence than women (16.5% vs 13.2%); diabetes prevalence increased across age categories. White patients had the lowest prevalence (11.4%) and Hawaiian/Pacific Islanders, the highest prevalence (21.9%), with prevalence ranging from 15.2% to 16.5% for other race/ethnicities. The association between BMI class and diabetes prevalence was similar across all racial/ethnic groups. CONCLUSION: The ADVANCE diabetes cohort offers an opportunity to conduct epidemiologic and comparative effectiveness research on underserved and underrepresented individuals, who have a higher prevalence of diabetes than the general US population.