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PURPOSE Practice facilitation is an evidence-informed implementation strategy to support quality improvement (QI) and aid practices in aligning with best evidence. Few studies, particularly of this size and scope, identify strategies that contribute to facilitator effectiveness. METHODS We conducted a sequential mixed methods study, analyzing data from EvidenceNOW, a large-scale QI initiative. Seven regional cooperatives employed 162 facilitators to work with 1,630 small or medium-sized primary care practices. Main analyses were based on facilitators who worked with at least 4 practices. Facilitators were defined as more effective if at least 75% of their practices improved on at least 1 outcome measure-aspirin use, blood pressure control, smoking cessation counseling (ABS), or practice change capacity, measured using Change Process Capability Questionnaire-from baseline to follow-up. Facilitators were defined as less effective if less than 50% of their practices improved on these outcomes. Using an immersion crystallization and comparative approach, we analyzed observational and interview data to identify strategies associated with more effective facilitators. RESULTS Practices working with more effective facilitators had a 3.6% greater change in the mean percentage of patients meeting the composite ABS measure compared with practices working with less effective facilitators (P CONCLUSIONS Facilitation strategies that differentiate more and less effective facilitators have implications for enhancing facilitator development and training, and can assist all facilitators to more effectively support practice changes. Key words: practice facilitation; quality improvement; large-scale initiative; capacity building; implementation facilitation; organizational change; organizational innovation, INTRODUCTION Practice facilitators are trained individuals who build medical practices' capacity to make meaningful operational changes aligned with current evidence. (1,2) Facilitators provide education, motivation, accountability, and guidance on organizational [...]

PURPOSE Evidence shows the value of home blood pressure (BP) monitoring in hypertension management. Questions exist about how to effectively incorporate these readings into BP follow-up visits. We developed and implemented a tool that combines clinical and home BP readings into an electronic health record (EHR)-integrated visualization tool. We examined how this tool was used during primary care visits and its effect on physician-patient communication and decision making about hypertension management, comparing it with home BP readings on paper. METHODS We video recorded the hypertension follow-up visits of 73 patients with 15 primary care physicians between July 2018 and April 2019. During visits, physicians reviewed home BP readings with patients, either directly from paper or as entered into the EHR visualization tool. We used conversation analysis to analyze the recordings. RESULTS Home BP readings were viewed on paper for 26 patients and in the visualization tool for 47 patients. Access to home BP readings during hypertension management visits, regardless of viewing mode, positioned the physician and patient to assess BP management and make decisions about treatment modification, if needed. Length of BP discussion with the visualization tool was similar to or shorter than that with paper. Advantages of the visualization tool included ease of use, and enhanced and faster sense making and decision making. Successful use of the tool required patients' ability to obtain their BP readings and enter them into the EHR via a portal, and an examination room configuration that allowed for screen sharing. CONCLUSIONS Reviewing home BP readings using a visualization tool is feasible and enhances sense making and patient engagement in decision making. Practices and their patients need appropriate infrastructure to realize these benefits. Key words: patient-generated health data; home blood pressure monitoring; hypertension control; clinical decision support; EHR; health information technology; ergonomics; data visualization; shared decision making, INTRODUCTION Hypertension is a serious, prevalent disease affecting more than 100 million American adults. (1) High blood pressure (BP) contributes 40.6% of the attributable risk for heart and cardiovascular disease [...]

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

Failed diffusion of innovations may be linked to an inability to use and apply data, information, and knowledge to change perceptions of current practice and motivate change. Using qualitative and quantitative data from three large-scale health care delivery innovations-accountable care organizations, advanced primary care practice, and EvidenceNOW-we assessed where data-driven innovation is occurring and where challenges lie. We found that implementation of some technological components of innovation (for example, electronic health records) has occurred among health care organizations, but core functions needed to use data to drive innovation are lacking. Deficits include the inability to extract and aggregate data from the records; gaps in sharing data; and challenges in adopting advanced data functions, particularly those related to timely reporting of performance data. The unexpectedly high costs and burden incurred during implementation of the innovations have limited organizations' ability to address these and other deficits. Solutions that could help speed progress in data-driven innovation include facilitating peer-to-peer technical assistance, providing tailored feedback reports to providers from data aggregators, and using practice facilitators skilled in using data technology for quality improvement to help practices transform. Policy efforts that promote these solutions may enable more rapid uptake of and successful participation in innovative delivery system reforms.

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.

Background: Two primary care clinics implemented an opioid use disorder (OUD) treatment program that integrates behavioral health counseling with buprenorphine treatment and uses tiers. This project aimed to understand how patients moved through tiers in this program. Method: We purposively sampled 20 patients with at least 10 OUD-related treatment visits; we documented tier changes at all visits between September 1, 2016 and December 31, 2018 using a standard data collection instrument. These data were used to construct run-charts. Results: About 45% of sampled patients had at least one relapse noted and 60% of patients dropped in tier during the study. Reductions in tier often happened when the patient was navigating difficult psychosocial situations in their life, whereas increases in tier often accompanied positive life events. We also found variation in use of the tiers. Discussion: OUD treatment from two clinics by tier illustrates that recovery is an individualized process based on patient need that can fluctuate due to psychosocial triggers and significant life events. Having tiers can guide treatment and provide both clinicians and patients with information about what to expect during treatment while still allowing the flexibility to meet patients where they are. Public Significance Statement Most patients experienced fluctuations between higher and lower tiers of officebased opioid treatment with buprenorphine. Opioid use disorder treatment programs should meet patients where they are and continue to provide treatment during times of relapse to reduce drug use. Keywords: office-based opioid treatment, buprenorphine, opioid use disorder, primary care, Medications for OUD (MOUD) are effective and increasingly delivered in primary care settings: (Olfson et al., 2020) guidelines for treatment recommend frequent office visits when treatment begins (American Society of [...]

PURPOSE We undertook a study to identify conditions and operational changes linked to improvements in smoking and blood pressure (BP) outcomes in primary care. METHODS We purposively sampled and interviewed practice staff (eg, office managers, clinicians) from a subset of 104 practices participating in EvidenceNOW--a multisite cardiovascular disease prevention initiative. We calculated Clinical Quality Measure improvements, with targets of 10-point or greater absolute improvements in the proportion of patients with smoking screening and, if relevant, counseling and in the proportion of hypertensive patients with adequately controlled BP. We analyzed interview data to identify operational changes, transforming these into numeric data. We used Configurational Comparative Methods to assess the joint effects of multiple factors on outcomes. RESULTS In clinician-owned practices, implementing a workflow to routinely screen, counsel, and connect patients to smoking cessation resources, or implementing a documentation change or a referral to a resource alone led to an improvement of at least 10 points in the smoking outcome with a moderate level of facilitation support. These patterns did not manifest in health- or hospital system-owned practices or in Federally Qualified Health Centers, however. The BP outcome improved by at least 10 points among solo practices after medical assistants were trained to take an accurate BP. Among larger, clinician-owned practices, BP outcomes improved when practices implemented a second BP measurement when the first was elevated, and when staff learned where to document this information in the electronic health record. With 50 hours or more of facilitation, BP outcomes improved among larger and health- and hospital system-owned practices that implemented these operational changes. CONCLUSIONS There was no magic bullet for improving smoking or BP outcomes. Multiple combinations of operational changes led to improvements, but only in specific contexts of practice size and ownership, or dose of external facilitation. Key words: quality improvement; configurational comparative methods; mixed methods; cardiovascular prevention; smoking cessation; blood pressure management; organizational change; primary care; practice-based research, INTRODUCTION Cardiovascular disease (CVD) is the leading cause of death in the United States, with stroke or heart disease contributing to 1 out of every 3 deaths. (1) Primary risk [...]

We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting Observations of Daily Living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients' collection of ODL data: usability, illness experience, relevance of ODLs, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health.

ObjectiveExamine how the Electronic Health Record (EHR) and its related systems support or inhibit provider collaboration.BackgroundHealth care systems in the US are simultaneously implementing EHRs and transitioning to more collaborative delivery systems; this study examines the interaction between these two changes.MethodsThis qualitative study of five US EHR implementations included 49 interviews and over 60 hours of provider observation. We examined the role of the EHR in building relationships, communicating, coordinating, and collaborative decision-making.ResultsThe EHR plays four roles in collaboration: a repository, a messenger, an orchestrator, and a monitor. While EHR performance varied, common themes were decreased trust due to poor quality documentation, incomplete communication, potential for increased effectiveness through better coordination, and the emerging role of the EHR in identifying performance gaps.ConclusionBoth organizational and technical innovations are needed if the EHR is to truly support collaborative behaviors.

Key words: practice transformation; organizational change; innovation; quality improvement; professional practice; health extension; outreach; practice facilitation; primary care; burnout; health information technology; practice-based research, This supplement to the Annals of Family Medicine brings together early learning from multiple examples of health extension and practice transformation support, with the goal of informing future efforts to [...]

Posttraumatic stress disorder (PTSD) and bipolar disorder are common in primary care. Evidence supports collaborative care in primary care settings to treat depression and anxiety, and recent studies have evaluated its effectiveness in treating complex conditions such as PTSD and bipolar disorder. This study aimed to examine how primary care clinicians experience collaborative care for patients with these more complex psychiatric disorders. The authors conducted semistructured interviews with 22 primary care clinicians participating in a pragmatic trial that included telepsychiatry collaborative care (TCC) to treat patients with PTSD or bipolar disorder in rural or underserved areas. Analysis utilized a constant comparative method to identify recurring themes. Clinicians reported that TCC improved their confidence in managing medications for patients with PTSD or bipolar disorder and supported their ongoing learning and skill development. Clinicians also reported improvements in patient engagement in care. Care managers were crucial to realizing these benefits by fostering communication within the clinical team while engaging patients through regular outreach. Clinicians valued TCC because it included and supported them in improving the care of patients' mental health conditions, which opened opportunities for clinicians to enhance care and address co-occurring general medical conditions. Overall, benefits of the TCC model outweighed its minimal burdens. Clinicians found that TCC supported their care of patients with PTSD or bipolar disorder. This approach has the potential to extend the reach of specialty mental health care and to support primary care clinicians treating patients with these more complex psychiatric disorders. [ABSTRACT FROM AUTHOR]

Background: Following the ACA, millions of people gained Medicaid insurance. Most electronic health record (EHR) tools to date provide clinical-decision support and tracking of clinical biomarkers, we developed an EHR tool to support community health center (CHC) staff in assisting patients with health insurance enrollment documents and tracking insurance application steps. The objective of this study was to test the effectiveness of the health insurance support tool in (1) assisting uninsured patients gaining insurance coverage, (2) ensuring insurance continuity for patients with Medicaid insurance (preventing coverage gaps between visits); and (3) improving receipt of cancer preventive care.Methods: In this quasi-experimental study, twenty-three clinics received the intervention (EHR-based insurance support tool) and were matched to 23 comparison clinics. CHCs were recruited from the OCHIN network. EHR data were linked to Medicaid enrollment data. The primary outcomes were rates of uninsured and Medicaid visits. The secondary outcomes were receipt of recommended breast, cervical, and colorectal cancer screenings. A comparative interrupted time-series using Poisson generalized estimated equation (GEE) modeling was performed to evaluate the effectiveness of the EHR-based tool on the primary and secondary outcomes.Results: Immediately following implementation of the enrollment tool, the uninsured visit rate decreased by 21.0% (Adjusted Rate Ratio [RR] = 0.790, 95% CI = 0.621-1.005, p = .055) while Medicaid-insured visits increased by 4.5% (ARR = 1.045, 95% CI = 1.013-1.079) in the intervention group relative to comparison group. Cervical cancer preventive ratio increased 5.0% (ARR = 1.050, 95% CI = 1.009-1.093) immediately following implementation of the enrollment tool in the intervention group relative to comparison group. Among patients with a tool use, 81% were enrolled in Medicaid 12 months after tool use. For the 19% who were never enrolled in Medicaid following tool use, most were uninsured (44%) at the time of tool use.Conclusions: A health insurance support tool embedded within the EHR can effectively support clinic staff in assisting patients in maintaining their Medicaid coverage. Such tools may also have an indirect impact on evidence-based practice interventions, such as cancer screening.Trial Registration: This study was retrospectively registered on February 4th, 2015 with Clinicaltrials.gov (#NCT02355262). The registry record can be found at https://www.clinicaltrials.gov/ct2/show/NCT02355262 . [ABSTRACT FROM AUTHOR]