Patient-Reported Social Risks and Clinician Decision Making: Results of a Clinician Survey in Primary Care Community Health Centers.

PURPOSE: To assess the extent that patients' social determinants of health (SDOH) influence safety-net primary care clinicians' decisions at the point of care; examine how that information comes to the clinician's attention; and analyze clinician, patient, and encounter characteristics associated with the use of SDOH data in clinical decision making. METHODS: Thirty-eight clinicians working in 21 clinics were prompted to complete 2 short card surveys embedded in the electronic health record (EHR) daily for 3 weeks. Survey data were matched with clinician-, encounter-, and patient-level variables from the EHR. Descriptive statistics and generalized estimating equation models were used to assess relationships between the variables and the clinician reported use of SDOH data to inform care. RESULTS: Social determinants of health were reported to influence care in 35% of surveyed encounters. The most common sources of information on patients' SDOH were conversations with patients (76%), prior knowledge (64%), and the EHR (46%). Social determinants of health were significantly more likely to influence care among male and non-English-speaking patients, and those with discrete SDOH screening data documented in the EHR. CONCLUSIONS: Electronic health records present an opportunity to support clinicians integrating information about patients' social and economic circumstances into care planning. Study findings suggest that SDOH information from standardized screening documented in the EHR, combined with patient-clinician conversations, may enable social risk-adjusted care. Electronic health record tools and clinic workflows could be used to support both documentation and conversations. Study results also identified factors that may cue clinicians to include SDOH information in point-of-care decision-making. Future research should explore this topic further.

Designing and Implementing an Electronic Health Record-Embedded Card Study in Primary Care: Methods and Considerations.

Card studies-short surveys about the circumstances within which patients receive care-are traditionally completed on physical cards. We report on the development of an electronic health record (EHR)-embedded card study intended to decrease logistical challenges inherent to paper-based approaches, including distributing, tracking, and transferring the physical cards, as well as data entry and respondent prompts, while simultaneously decreasing the complexity for participants and facilitating rich analyses by linking to clinical and demographic data found in the EHR. Developing the EHR-based programming and data extraction was time consuming, required specialized expertise, and necessitated iteration to rectify issues encountered during implementation. Nonetheless, future EHR-embedded card studies will be able to replicate many of the same processes as informed by these results. Once built, the EHR-embedded card study simplified survey implementation for both the research team and clinic staff, resulting in research-quality data, the ability to link survey responses to relevant EHR data, and a 79% response rate. This detailed accounting of the development and implementation process, including issues encountered and addressed, might guide others in conducting EHR-embedded card studies.

Strengthening methods for tracking adaptations and modifications to implementation strategies.

BACKGROUND: Developing effective implementation strategies requires adequate tracking and reporting on their application. Guidelines exist for defining and reporting on implementation strategy characteristics, but not for describing how strategies are adapted and modified in practice. We built on existing implementation science methods to provide novel methods for tracking strategy modifications. METHODS: These methods were developed within a stepped-wedge trial of an implementation strategy package designed to help community clinics adopt social determinants of health-related activities: in brief, an 'Implementation Support Team' supports clinics through a multi-step process. These methods involve five components: 1) describe planned strategy; 2) track its use; 3) monitor barriers; 4) describe modifications; and 5) identify / describe new strategies. We used the Expert Recommendations for Implementing Change taxonomy to categorize strategies, Proctor et al.'s reporting framework to describe them, the Consolidated Framework for Implementation Research to code barriers / contextual factors necessitating modifications, and elements of the Framework for Reporting Adaptations and Modifications-Enhanced to describe strategy modifications. RESULTS: We present three examples of the use of these methods: 1) modifications made to a facilitation-focused strategy (clinics reported that certain meetings were too frequent, so their frequency was reduced in subsequent wedges); 2) a clinic-level strategy addition which involved connecting one study clinic seeking help with community health worker-related workflows to another that already had such a workflow in place; 3) a study-level strategy addition which involved providing assistance in overcoming previously encountered (rather than de novo) challenges. CONCLUSIONS: These methods for tracking modifications made to implementation strategies build on existing methods, frameworks, and guidelines; however, as none of these were a perfect fit, we made additions to several frameworks as indicated, and used certain frameworks' components selectively. While these methods are time-intensive, and more work is needed to streamline them, they are among the first such methods presented to implementation science. As such, they may be used in research on assessing effective strategy modifications and for replication and scale-up of effective strategies. We present these methods to guide others seeking to document implementation strategies and modifications to their studies. TRIAL REGISTRATION: clinicaltrials.gov ID: NCT03607617 (first posted 31/07/2018).

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.

Cardiovascular care guideline implementation in community health centers in Oregon: a mixed-methods analysis of real-world barriers and challenges.

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.

Multiple perspectives on clinical decision support: a qualitative study of fifteen clinical and vendor organizations.

BACKGROUND: Computerized clinical decision support (CDS) can help hospitals to improve healthcare. However, CDS can be problematic. The purpose of this study was to discover how the views of clinical stakeholders, CDS content vendors, and EHR vendors are alike or different with respect to challenges in the development, management, and use of CDS. METHODS: We conducted ethnographic fieldwork using a Rapid Assessment Process within ten clinical and five health information technology (HIT) vendor organizations. Using an inductive analytical approach, we generated themes from the clinical, content vendor, and electronic health record vendor perspectives and compared them. RESULTS: The groups share views on the importance of appropriate manpower, careful knowledge management, CDS that fits user workflow, the need for communication among the groups, and for mutual strategizing about the future of CDS. However, views of usability, training, metrics, interoperability, product use, and legal issues differed. Recommendations for improvement include increased collaboration to address legal, manpower, and CDS sharing issues. CONCLUSIONS: The three groups share thinking about many aspects of CDS, but views differ in a number of important respects as well. Until these three groups can reach a mutual understanding of the views of the other stakeholders, and work together, CDS will not reach its potential.

Ethnographic process evaluation in primary care: explaining the complexity of implementation.

BACKGROUND: The recent growth of implementation research in care delivery systems has led to a renewed interest in methodological approaches that deliver not only intervention outcome data but also deep understanding of the complex dynamics underlying the implementation process. We suggest that an ethnographic approach to process evaluation, when informed by and integrated with quantitative data, can provide this nuanced insight into intervention outcomes. The specific methods used in such ethnographic process evaluations are rarely presented in detail; our objective is to stimulate a conversation around the successes and challenges of specific data collection methods in health care settings. We use the example of a translational clinical trial among 11 community clinics in Portland, OR that are implementing an evidence-based, health-information technology (HIT)-based intervention focused on patients with diabetes. DISCUSSION: Our ethnographic process evaluation employed weekly diaries by clinic-based study employees, observation, informal and formal interviews, document review, surveys, and group discussions to identify barriers and facilitators to implementation success, provide insight into the quantitative study outcomes, and uncover lessons potentially transferable to other implementation projects. These methods captured the depth and breadth of factors contributing to intervention uptake, while minimizing disruption to clinic work and supporting mid-stream shifts in implementation strategies. A major challenge is the amount of dedicated researcher time required. The deep understanding of the 'how' and 'why' behind intervention outcomes that can be gained through an ethnographic approach improves the credibility and transferability of study findings. We encourage others to share their own experiences with ethnography in implementation evaluation and health services research, and to consider adapting the methods and tools described here for their own research.

Finding meaning: a realist-informed perspective on social risk screening and relationships as mechanisms of change.

Background: Social risk screening rates in many US primary care settings remain low. This realist-informed evaluation explored the mechanisms through which a tailored coaching and technical training intervention impacted social risk screening uptake in 26 community clinics across the United States. Methods: Evaluation data sources included the documented content of interactions between the clinics and implementation support team and electronic health record (EHR) data. Following the realist approach, analysis was composed of iterative cycles of developing, testing and refining program theories about how the intervention did-or didn't-work, for whom, under what circumstances. Normalization Process Theory was applied to the realist program theories to enhance the explanatory power and transferability of the results. Results: Analysis identified three overarching realist program theories. First, clinic staff perceptions about the role of standardized social risk screening in person-centered care-considered "good" care and highly valued-strongly impacted receptivity to the intervention. Second, the physicality of the intervention materials facilitated collaboration and impacted clinic leaders' perception of the legitimacy of the social risk screening implementation work. Third, positive relationships between the implementation support team members, between the support team and clinic champions, and between clinic champions and staff motivated and inspired clinic staff to engage with the intervention and to tailor workflows to their settings' needs. Study clinics did not always exhibit the social risk screening patterns anticipated by the program theories due to discrepant definitions of success between clinic staff (improved ability to provide contextualized, person-centered care) and the trial (increased rates of EHR-documented social risk screening). Aligning the realist program theories with Normalization Process Theory constructs clarified that the intervention as implemented emphasized preparation over operationalization and appraisal, providing insight into why the intervention did not successfully embed sustained systematic social risk screening in participating clinics. Conclusion: The realist program theories highlighted the effectiveness and importance of intervention components and implementation strategies that support trusting relationships as mechanisms of change. This may be particularly important in social determinants of health work, which requires commitment and humility from health care providers and vulnerability on the part of patients.

Partner-developed electronic health record tools to facilitate social risk-informed care planning.

OBJECTIVE: Increased social risk data collection in health care settings presents new opportunities to apply this information to improve patient outcomes. Clinical decision support (CDS) tools can support these applications. We conducted a participatory engagement process to develop electronic health record (EHR)-based CDS tools to facilitate social risk-informed care plan adjustments in community health centers (CHCs). MATERIALS AND METHODS: We identified potential care plan adaptations through systematic reviews of hypertension and diabetes clinical guidelines. The results were used to inform an engagement process in which CHC staff and patients provided feedback on potential adjustments identified in the guideline reviews and on tool form and functions that could help CHC teams implement these suggested adjustments for patients with social risks. RESULTS: Partners universally prioritized tools for social risk screening and documentation. Additional high-priority content included adjusting medication costs and changing follow-up plans based on reported social risks. Most content recommendations reflected partners' interests in encouraging provider-patient dialogue about care plan adaptations specific to patients' social needs. Partners recommended CDS tool functions such as alerts and shortcuts to facilitate and efficiently document social risk-informed care plan adjustments. DISCUSSION AND CONCLUSION: CDS tools were designed to support CHC providers and staff to more consistently tailor care based on information about patients' social context and thereby enhance patients' ability to adhere to care plans. While such adjustments occur on an ad hoc basis in many care settings, these are among the first tools designed both to systematize and document these activities.

Implementation Support for a Social Risk Screening and Referral Process in Community Health Centers.

Evidence is needed about how to effectively support health care providers in implementing screening for social risks (adverse social determinants of health) and providing related referrals meant to address identified social risks. This need is greatest in underresourced care settings. The authors tested whether an implementation support intervention (6 months of technical assistance and coaching study clinics through a five-step implementation process) improved adoption of social risk activities in community health centers (CHCs). Thirty-one CHC clinics were block-randomized to six wedges that occurred sequentially. Over the 45-month study period from March 2018 to December 2021, data were collected for 6 or more months preintervention, the 6-month intervention period, and 6 or more months postintervention. The authors calculated clinic-level monthly rates of social risk screening results that were entered at in-person encounters and rates of social risk-related referrals. Secondary analyses measured impacts on diabetes-related outcomes. Intervention impact was assessed by comparing clinic performance based on whether they had versus had not yet received the intervention in the preintervention period compared with the intervention and postintervention periods. In assessing the results, the authors note that five clinics withdrew from the study for various bandwidth-related reasons. Of the remaining 26, a total of 19 fully or partially completed all 5 implementation steps, and 7 fully or partially completed at least the first 3 steps. Social risk screening was 2.45 times (95% confidence interval [CI], 1.32-4.39) higher during the intervention period compared with the preintervention period; this impact was not sustained postintervention (rate ratio, 2.16; 95% CI, 0.64-7.27). No significant difference was seen in social risk referral rates during the intervention or postintervention periods. The intervention was associated with greater blood pressure control among patients with diabetes and lower rates of diabetes biomarker screening postintervention. All results must be interpreted considering that the Covid-19 pandemic began midway through the trial, which affected care delivery generally and patients at CHCs particularly. Finally, the study results show that adaptive implementation support was effective at temporarily increasing social risk screening. It is possible that the intervention did not adequately address barriers to sustained implementation or that 6 months was not long enough to cement this change. Underresourced clinics may struggle to participate in support activities over longer periods without adequate resources, even if lengthier support is needed. As policies start requiring documentation of social risk activities, safety-net clinics may be unable to meet these requirements without adequate financial and coaching/technical support.

Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study.

BACKGROUND: The purpose of this study was to identify recommended practices for computerized clinical decision support (CDS) development and implementation and for knowledge management (KM) processes in ambulatory clinics and community hospitals using commercial or locally developed systems in the U.S. METHODS: Guided by the Multiple Perspectives Framework, the authors conducted ethnographic field studies at two community hospitals and five ambulatory clinic organizations across the U.S. Using a Rapid Assessment Process, a multidisciplinary research team: gathered preliminary assessment data; conducted on-site interviews, observations, and field surveys; analyzed data using both template and grounded methods; and developed universal themes. A panel of experts produced recommended practices. RESULTS: The team identified ten themes related to CDS and KM. These include: 1) workflow; 2) knowledge management; 3) data as a foundation for CDS; 4) user computer interaction; 5) measurement and metrics; 6) governance; 7) translation for collaboration; 8) the meaning of CDS; 9) roles of special, essential people; and 10) communication, training, and support. Experts developed recommendations about each theme. The original Multiple Perspectives framework was modified to make explicit a new theoretical construct, that of Translational Interaction. CONCLUSIONS: These ten themes represent areas that need attention if a clinic or community hospital plans to implement and successfully utilize CDS. In addition, they have implications for workforce education, research, and national-level policy development. The Translational Interaction construct could guide future applied informatics research endeavors.

Initiating and Implementing Social Determinants of Health Data Collection in Community Health Centers.

Successfully incorporating social determinants of health (SDH) screening into clinic workflows can help care teams provide targeted care, appropriate referrals, and other interventions to address patients' social risk factors. However, integrating SDH screening into clinical routines is known to be challenging. To achieve widespread adoption of SDH screening, we need to better understand the factors that can facilitate or hinder implementation of effective, sustainable SDH processes. The authors interviewed 43 health care staff and professionals at 8 safety net community health center (CHC) organizations in 5 states across the United States; these CHCs had adopted electronic health record (EHR)-based SDH screening without any external implementation support. Interviewees included staff in administrative, quality improvement, informatics, front desk, and clinical roles (providers, nurses, behavioral health staff), and community health workers. Interviews focused on how each organization integrated EHR-based SDH screening into clinic workflows, and factors that affected adoption of this practice change. Factors that facilitated effective integration of EHR-based SDH screening were: (1) external incentives and motivators that prompted introduction of this screening (eg, grant requirements, encouragement from professional associations); (2) presence of an SDH screening advocate; and (3) maintaining flexibility with regard to workflow approaches to optimally align them with clinic needs, interests, and resources. Results suggest that it is possible to purposefully create an environment conducive to successfully implementing EHR-based SDH screening. Approaching the task of implementing SDH screening into clinic workflows as understanding the interplay of context-dependent factors, rather than following a step-by-step process, may be critical to success in primary care settings.

Using Electronic Health Record-Based Clinical Decision Support to Provide Social Risk-Informed Care in Community Health Centers: Protocol for the Design and Assessment of a Clinical Decision Support Tool.

BACKGROUND: Consistent and compelling evidence demonstrates that social and economic adversity has an impact on health outcomes. In response, many health care professional organizations recommend screening patients for experiences of social and economic adversity or social risks-for example, food, housing, and transportation insecurity-in the context of care. Guidance on how health care providers can act on documented social risk data to improve health outcomes is nascent. A strategy recommended by the National Academy of Medicine involves using social risk data to adapt care plans in ways that accommodate patients' social risks. OBJECTIVE: This study's aims are to develop electronic health record (EHR)-based clinical decision support (CDS) tools that suggest social risk-informed care plan adaptations for patients with diabetes or hypertension, assess tool adoption and its impact on selected clinical quality measures in community health centers, and examine perceptions of tool usability and impact on care quality. METHODS: A systematic scoping review and several stakeholder activities will be conducted to inform development of the CDS tools. The tools will be pilot-tested to obtain user input, and their content and form will be revised based on this input. A randomized quasi-experimental design will then be used to assess the impact of the revised tools. Eligible clinics will be randomized to a control group or potential intervention group; clinics will be recruited from the potential intervention group in random order until 6 are enrolled in the study. Intervention clinics will have access to the CDS tools in their EHR, will receive minimal implementation support, and will be followed for 18 months to evaluate tool adoption and the impact of tool use on patient blood pressure and glucose control. RESULTS: This study was funded in January 2020 by the National Institute on Minority Health and Health Disparities of the National Institutes of Health. Formative activities will take place from April 2020 to July 2021, the CDS tools will be developed between May 2021 and November 2022, the pilot study will be conducted from August 2021 to July 2022, and the main trial will occur from December 2022 to May 2024. Study data will be analyzed, and the results will be disseminated in 2024. CONCLUSIONS: Patients' social risk information must be presented to care teams in a way that facilitates social risk-informed care. To our knowledge, this study is the first to develop and test EHR-embedded CDS tools designed to support the provision of social risk-informed care. The study results will add a needed understanding of how to use social risk data to improve health outcomes and reduce disparities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31733.

"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.

Unintended consequences: a qualitative study exploring the impact of collecting implementation process data with phone interviews on implementation activities.

BACKGROUND: Qualitative data are crucial for capturing implementation processes, and thus necessary for understanding implementation trial outcomes. Typical methods for capturing such data include observations, focus groups, and interviews. Yet little consideration has been given to how such methods create interactions between researchers and study participants, which may affect participants' engagement, and thus implementation activities and study outcomes. In the context of a clinical trial, we assessed whether and how ongoing telephone check-ins to collect data about implementation activities impacted the quality of collected data, and participants' engagement in study activities. METHODS: Researchers conducted regular phone check-ins with clinic staff serving as implementers in an implementation study. Approximately 1 year into this trial, 19 of these study implementers were queried about the impact of these calls on study engagement and implementation activities. The two researchers who collected implementation process data through phone check-ins with the study implementers were also interviewed about their perceptions of the impact of the check-ins. RESULTS: Study implementers' assessment of the check-ins' impact fell into three categories: (1) the check-ins had no effect on implementation activities, (2) the check-ins served as a reminder about study participation (without relating a clear impact on implementation activities), and (3) the check-ins caused changes in implementation activities. The researchers similarly perceived that the phone check-ins served as reminders and encouraged some implementers' engagement in implementation activities; their ongoing nature also created personal connections with study implementers that may have impacted implementation activities. Among some study implementers, anticipation of the check-in calls also improved their ability to recount implementation activities and positively affected quality of the data collected. CONCLUSION: These results illustrate the potential impact of qualitative data collection on implementation activities during implementation science trials. Mitigating such effects may prove challenging, but acknowledging these consequences-or even embracing them, perhaps by designing data collection methods as implementation strategies-could enhance scientific rigor. This work is presented to stimulate debate about the complexities involved in capturing data on implementation processes using common qualitative data collection methods. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02325531 . Registered 15 December 2014.

Lessons learned about the effective operationalization of champions as an implementation strategy: results from a qualitative process evaluation of a pragmatic trial.

BACKGROUND: Though the knowledge base on implementation strategies is growing, much remains unknown about how to most effectively operationalize these strategies in diverse contexts. For example, while evidence shows that champions can effectively support implementation efforts in some circumstances, little has been reported on how to operationalize this role optimally in different settings, or on the specific pathways through which champions enact change. METHODS: This is a secondary analysis of data from a pragmatic trial comparing implementation strategies supporting the adoption of guideline-concordant cardioprotective prescribing in community health centers in the USA. Quantitative data came from the community health centers' shared electronic health record; qualitative data sources included community health center staff interviews over 3 years. Using a convergent mixed-methods design, data were collected concurrently and merged for interpretation to identify factors associated with improved outcomes. Qualitative analysis was guided by the constant comparative method. As results from the quantitative and initial qualitative analyses indicated the essential role that champions played in promoting guideline-concordant prescribing, we conducted multiple immersion-crystallization cycles to better understand this finding. RESULTS: Five community health centers demonstrated statistically significant increases in guideline-concordant cardioprotective prescribing. A combination of factors appeared key to their successful practice change: (1) A clinician champion who demonstrated a sustained commitment to implementation activities and exhibited engagement, influence, credibility, and capacity; and (2) organizational support for the intervention. In contrast, the seven community health centers that did not show improved outcomes lacked a champion with the necessary characteristics, and/or organizational support. Case studies illustrate the diverse, context-specific pathways that enabled or prevented study implementers from advancing practice change. CONCLUSION: This analysis confirms the important role of champions in implementation efforts and offers insight into the context-specific mechanisms through which champions enact practice change. The results also highlight the potential impact of misaligned implementation support and key modifiable barriers and facilitators on implementation outcomes. Here, unexamined assumptions and a lack of evidence-based guidance on how best to identify and prepare effective champions led to implementation support that failed to address important barriers to intervention success. TRIAL REGISTRATION: ClinicalTrials.gov , NCT02325531 . Registered 15 December 2014.

Educating youth about health and science using a partnership between an academic medical center and community-based science museum.

Declining student interest and scholastic abilities in the sciences are concerns for the health professions. Additionally, the National Institutes of Health is committed to promoting more research on health behaviors among US youth, where one of the most striking contemporary issues is obesity. This paper reports findings on the impact of a partnership between Oregon Health and Science University (OHSU) and the Oregon Museum of Science and Industry linked to a 17-week exhibition of BodyWorlds3 and designed to inform rural underserved youth about science and health research. Self-administered survey measures included health knowledge, attitudes, intended health behaviors, and interest in the health professions. Four hundred four surveys (88% of participants) were included in analyses. Ninety percent or more found both the BodyWorlds (n = 404) and OHSU (n = 239) exhibits interesting. Dental care habits showed the highest level of intended behavior change (Dental = 45%, Exercise = 34%, Eating = 30%). Overall, females and middle school students were more likely than male and high school students, respectively, to state an intention to change exercise, eating and dental care habits. Females and high school students were more likely to have considered a career in health or science prior to their exhibit visit and, following the exhibit, were more likely to report that this intention had been reinforced. About 6% of those who had not previously considered a career in health or science (n = 225) reported being more likely to do so after viewing the exhibits. In conclusion, high quality experiential learning best created by community-academic partnerships appears to have the ability to stimulate interest and influence intentions to change health behaviors among middle and high school students.

Educating the public about research funded by the National Institutes of Health using a partnership between an academic medical center and community-based science museum.

The NIH roadmap has among its goals, to promote studies designed to improve public understanding of biomedical and behavioral science, and to develop strategies for promoting collaborations between scientists and communities toward improving the public's health. Here, we report findings on the impact of a partnership between the Oregon Health and Science University (OHSU) and the Oregon Museum of Science and Industry (OMSI) designed to inform the public about health research being conducted in Oregon, which was linked to a 17-week traveling exhibition of BodyWorlds3. Measures included the public's understanding of health knowledge, attitudes, intended health behaviors, and visitor experience in their interactions with OHSU experts/volunteers, which were collected using exit surveys administered verbally. Nine hundred fifty-three surveys were included in analyses. Among those who felt that health behavior change was relevant to them, 67.4% of smokers (n = 133) intended to change their smoking behavior, 58.6% (of 677) intended to change their eating habits, 60.3% (of 667) intended to change their exercise routine, and 47% (of 448) intended to change their dental care habits. Forty-six percent of these visited the OHSU research exhibits (n = 437), and responded to how the exhibit changed their understanding about and openness to participate in health research. Greater than 85% had a much improved understanding of NIH research at OHSU and >58% reported they would be willing to participate in future research studies at OHSU. In conclusion, research partnerships between academic institutions and community-based museums appear to be viable ways to inform the public about research, stimulate their interest as future participants, and possibly influence their intention to improve health behaviors.

Variation in Electronic Health Record Documentation of Social Determinants of Health Across a National Network of Community Health Centers.

INTRODUCTION: This paper describes the adoption of an electronic health record-based social determinants of health screening tool in a national network of more than 100 community health centers. METHODS: In 2016, a screening tool with questions on 7 social determinants of health domains was developed and deployed in the electronic health record, with technical instructions on how to use the tool and suggested clinical workflows. To understand adoption patterns, the study team extracted electronic health record data for any patient with a community health center visit between June 2016 and May 2018. Patients were considered "screened" if a response to at least 1 social determinants of health domain was documented in the electronic health record tool. RESULTS: A total of 31,549 patients (2% of those with a visit in the study period) had a documented social determinants of health screening. The number of screenings increased over time, time; 71 community health centers (67%) conducted at least one screening, but almost 50% took place in only 4 community health centers. Over half (55%) of screenings only included responses for only 1 domain. Screening was most likely to occur during an office visit with an established patient and documented in the electronic health record by a medical assistant. CONCLUSIONS: Screening documentation patterns varied widely across the network of community health centers. Despite the growing national emphasis on the importance of screening for social determinants of health, these findings suggest that simply activating electronic health record tools for social determinants of health screening does not lead to widespread adoption. Potential barriers to screening adoption and implementation should be explored further. SUPPLEMENT INFORMATION: This article is part of a supplement entitled Identifying and Intervening on Social Needs in Clinical Settings: Evidence and Evidence Gaps, which is sponsored by the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services, Kaiser Permanente, and the Robert Wood Johnson Foundation.

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.