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.

Documenting New Ways of Delivering Care Under Oregon's Alternative Payment and Advanced Care Model.

BACKGROUND: The fee-for-service reimbursement system that dominates health care throughout the United States links payment to a billable office visit with a physician or advanced practice provider. Under Oregon's Alternative Payment and Advanced Care Model (APCM), initiated in 2013, participating community health centers (CHCs) received per-member-per-month payments for empaneled Medicaid patients in lieu of standard fee-for-service Medicaid payments. With Medicaid revenue under APCM no longer tied solely to the volume of visits, the Oregon Health Authority needed a way to document the full range of care and services that CHCs were providing to their patients, including nontraditional patient encounters taking place outside of traditional face-to-face visits with a billable provider. Toward this end, program leadership defined 18 visit and nonvisit-based care activities-"Care Services That Engage Patients" (Care STEPs)-that APCM CHCs were asked to document in the electronic health record to demonstrate continued empanelment. OBJECTIVE: To describe trends in rates of traditional face-to-face office visits and Care STEPs documentation among CHCs involved in the first 3 phases of APCM implementation. RESEARCH DESIGN: The study population included the 9 CHCs involved in the first 3 phases of APCM implementation. Using data from the electronic health record, quarterly summary rates for office visits and Care STEPs were calculated for the first 18 quarters of implementation (March 1, 2013 to June 30, 2017). RESULTS: Among participating CHCs, the mean rate of face-to-face visits with billable providers declined from 635 ± 128 to 461 ± 109 visits/1000 patients/quarter (mean difference, -174; 95% CI, -255, -94). Care STEPs documentation increased from 831 ± 174 to 1017 ± 369 Care Steps/1000 patients/quarter, but the difference was not statistically significant. Care STEPs within the category of New Visit Types were documented most frequently. There were significant increases in documentation of Patient Care Coordination and Integration and a small, albeit significant, increase in Reducing Barriers to Health. There was a significant decline in the documentation of Care STEPs by physicians and advanced practice providers an increase in documentation by ancillary staff. CONCLUSIONS: These findings suggest that APCM is increasing CHCs' capacity to experiment with new ways of providing care beyond the traditional face-to-face office visit with a physician or advanced practice provider. However, CHCs may choose different ways to change the delivery of care and some CHCs have implemented these changes more quickly than others. Future mixed-methods research is needed to understand barriers and facilitators to changing the delivery of care after APCM implementation.

The impact of capitated payment on preventive care utilization in community health clinics.

Only half of the United States population regularly receives recommended preventive care services. Alternative payment models (e.g., a per-member-per-month capitated payment model) may encourage the delivery of preventive services when compared to a fee-for-service visitbased model; however, evaluation is lacking in the United States. This study assesses the impact of implementing Oregon's Alternative Payment Methodology (APM) on orders for preventive services within community health centers (CHCs). This retrospective cohort study uses electronic health record data from the OCHIN, Inc., 2012-2018, analyzed in 2018-2019. Twenty-seven CHCs which implemented APM in 2013-2016 were compared to six non-APM CHCs. Clinic-level quarterly rates of ordering nine preventive services in 2012-2018 were calculated. For each phase and preventive service, we used difference-in-differences analysis to assess the APM impact on ordering preventive care. We found greater increases for APM CHCs compared to non-APM CHCs for orders of mammograms (difference-in-differences estimates (DDs) across four phases:1.69-2.45). Both groups had decreases in ordering cervical cancer screenings, however, APM CHCs had smaller decreases (DDs:1.62-1.93). The APM CHCs had significantly greater decreases in influenza vaccinations (DDs:0.17-0.32). There were no consistent significant differences in pre-post changes in APM vs. non-APM CHCs for cardiometabolic risk screenings, smoking status and depression assessments. There was nonsignificant change in the proportion of nontraditional encounters in APM clinics compared to controls. Transition from fee-for-service to an APM did not negatively impact delivery of preventive care. Further studies are needed to understand how to change encounter structures to best deliver recommended preventive care.

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.

Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers.

Importance: Responding to the substantial research on the relationship between social risk factors and health, enthusiasm has grown around social risk screening in health care settings, and numerous US health systems are experimenting with social risk screening initiatives. In the absence of standard social risk screening recommendations, some health systems are exploring using publicly available community-level data to identify patients who live in the most vulnerable communities as a way to characterize patient social and economic contexts, identify patients with potential social risks, and/or to target social risk screening efforts. Objective: To explore the utility of community-level data for accurately identifying patients with social risks by comparing the social deprivation index score for the census tract where a patient lives with patient-level social risk screening data. Design, Setting, and Participants: Cross-sectional study using patient-level social risk screening data from the electronic health records of a national network of community health centers between June 24, 2016, and November 15, 2018, linked to geocoded community-level data from publicly available sources. Eligible patients were those with a recorded response to social risk screening questions about food, housing, and/or financial resource strain, and a valid address of sufficient quality for geocoding. Exposures: Social risk screening documented in the electronic health record. Main Outcomes and Measures: Community-level social risk was assessed using census tract-level social deprivation index score stratified by quartile. Patient-level social risks were identified using food insecurity, housing insecurity, and financial resource strain screening responses. Results: The final study sample included 36 578 patients from 13 US states; 22 113 (60.5%) received public insurance, 21 181 (57.9%) were female, 17 578 (48.1%) were White, and 10 918 (29.8%) were Black. Although 6516 (60.0%) of those with at least 1 social risk factor were in the most deprived quartile of census tracts, patients with social risk factors lived in all census tracts. Overall, the accuracy of the community-level data for identifying patients with and without social risks was 48.0%. Conclusions and Relevance: Although there is overlap, patient-level and community-level approaches for assessing patient social risks are not equivalent. Using community-level data to guide patient-level activities may mean that some patients who could benefit from targeted interventions or care adjustments would not be identified.

The Impact of Social and Clinical Complexity on Diabetes Control Measures.

PURPOSE: In an age of value-based payment, primary care providers are increasingly scrutinized on performance metrics that assess quality of care, including the outcomes of their patient population in key areas such as diabetes control. Although such measures often adjust for patient clinical risk factors or clinical complexity, most do not account for the social complexity of patient populations, despite research demonstrating the strong association between social factors and health. METHODS: Using patient electronic health record data from 2 large community health center networks serving safety net patients, we assessed the effect of both clinical and social risk factors on poor glucose control among diabetics. Logistic regression results were used to estimate the impact of adjusting for both clinical and social complexity on provider performance metrics. Clinical complexity was measured at the patient-level using the Charlson Comorbidity Index. Social complexity was measured at the community-level using the Social Deprivation Index. RESULTS: Clinical complexity alone was not consistently associated with poor diabetes control (ie, HbA1c > 9%) in diabetic patients with HbA1c testing during the study period. However, increasing social complexity was significantly associated with higher rates of poor diabetic control in both cohorts. After adding adjustment for social complexity down to the national median score, our models suggest that approximately 25% of providers would have 1 to 2% improvement in the assessment of their diabetes control measures, with 45% showing a 2 to 5% improvement, and 5% showing more than a 5% improvement. CONCLUSIONS: Providers caring for patients with greater social risk factors may benefit from having their performance metrics adjusted for the social complexity of their patient populations.

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.

Study protocol: a pragmatic, stepped-wedge trial of tailored support for implementing social determinants of health documentation/action in community health centers, with realist evaluation.

BACKGROUND: National leaders recommend documenting social determinants of health and actions taken to address social determinants of health in electronic health records, and a growing body of evidence suggests the health benefits of doing so. However, little evidence exists to guide implementation of social determinants of health documentation/action. METHODS: This paper describes a 5-year, mixed-methods, stepped-wedge trial with realist evaluation, designed to test the impact of providing 30 community health centers with step-by-step guidance on implementing electronic health record-based social determinants of health documentation. This guidance will entail 6 months of tailored support from an interdisciplinary team, including training and technical assistance. We will report on tailored support provided at each of five implementation steps; impact of tailored implementation support; a method for tracking such tailoring; and context-specific pathways through which these tailored strategies effect change. We will track the competencies and resources needed to support the study clinics' implementation efforts. DISCUSSION: Results will inform how to tailor implementation strategies to meet local needs in real-world practice settings. Secondary analyses will assess impacts of social determinants of health documentation and referral-making on diabetes outcomes. By learning whether and how scalable, tailored implementation strategies help community health centers adopt social determinants of health documentation and action, this study will yield timely guidance to primary care providers. We are not aware of previous studies exploring implementation strategies that support adoption of social determinants of action using electronic health and interventions, despite the pressing need for such guidance. TRIAL REGISTRATION: clinicaltrials.gov, NCT03607617 , registration date: 7/31/2018-retrospectively registered.

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.

How 6 Organizations Developed Tools and Processes for Social Determinants of Health Screening in Primary Care: An Overview.

Little is known about how health care organizations are developing tools for identifying/addressing patients' social determinants of health (SDH). We describe the processes recently used by 6 organizations to develop SDH screening tools for ambulatory care and the barriers they faced during those efforts. Common processes included reviewing literature and consulting primary care staff. The organizations prioritized avoiding redundant data collection, integrating SDH screening into existing workflows, and addressing diverse clinic needs. This article provides suggestions for others hoping to develop similar tools/strategies for identifying patients' SDH needs in ambulatory care settings, with recommendations for further research.