Erratum: Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2024.2.].

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

Cultural context index: A geospatial measure of social determinants of health in the United States.

Minority populations will continue to grow in the United States. Such pluralism necessitates iterative, geospatial measurements of cultural contexts. Our objective in this study was to create a measure of social determinants of health in geographic areas with varying ethnic, linguistic, and religious diversity in the United States. We extracted geographic information systems data based on community characteristics that have known associations with population health disparities from 2015 to 2019. We used principal component analysis to construct a Cultural Context Index (CCI). We created the CCI for 73,682 census tracts across 50 states and five inhabited territories. We identified hot and cold spots that are the highest and lowest CCI quintile, respectively. Hot spots census tracts were mostly located in metropolitan areas (84.8%), in the Southern census region (41.5%), and also had larger Black and Hispanic populations. The census tracts with the greatest need for culturally competent health care also had the sickest populations. Census tracts with a CCI rank of 5 ('greatest need') had higher prevalences of self-reported poor physical health (17.2%) and poor mental health (17.4%), compared to either the general population (13.9% and 14.5%) or to CCI rank of 1 ('lowest need') (11.9% and 10.8%). The CCI can pinpoint census tracts with a need for culturally competent health care and inform supply-side policy planning as healthcare and social service providers will inevitably come in contact with consumers from different backgrounds.

Medication Reconciliation during Transitions of Care Across Institutions: A Quantitative Analysis of Challenges and Opportunities.

OBJECTIVE: Medication discrepancies between clinical systems may pose a patient safety hazard. In this paper, we identify challenges and quantify medication discrepancies across transitions of care. METHODS: We used structured clinical data and free-text hospital discharge summaries to compare active medications' lists at four time points: preadmission (outpatient), at-admission (inpatient), at-discharge (inpatient), and postdischarge (outpatient). Medication lists were normalized to RxNorm. RxNorm identifiers were further processed using the RxNav API to identify the ingredient. The specific drugs and ingredients from inpatient and outpatient medication lists were compared. RESULTS: Using RxNorm drugs, the median percentage intersection when comparing active medication lists within the same electronic health record system ranged between 94.1 and 100% indicating substantial overlap. Similarly, when using RxNorm ingredients the median percentage intersection was 94.1 to 100%. In contrast, the median percentage intersection when comparing active medication lists across EHR systems was significantly lower (RxNorm drugs: 6.1-7.1%; RxNorm ingredients: 29.4-35.0%) indicating that the active medication lists were significantly less similar (p < 0.05).Medication lists in the same EHR system are more similar to each other (fewer discrepancies) than medication lists in different EHR systems when comparing specific RxNorm drug and the more general RxNorm ingredients at transitions of care. Transitions of care that require interoperability between two EHR systems are associated with more discrepancies than transitions where medication changes are expected (e.g., at-admission vs. at-discharge). Challenges included lack of access to structured, standardized medication data across systems, and difficulty distinguishing medications from orderable supplies such as lancets and diabetic test strips. CONCLUSION: Despite the challenges to medication normalization, there are opportunities to identify and assist with medication reconciliation across transitions of care between institutions.

Provider-Based HPV Vaccine Promotion Interventions: A Meta-analysis.

CONTEXT: Provider-based human papillomavirus (HPV) vaccine promotion interventions have been widely implemented; however, the effectiveness of these approaches is unclear. OBJECTIVES: This systematic review assessed the effects and costs of provider-based interventions designed to increase HPV vaccination coverage. A meta-analysis was conducted to estimate pooled effect sizes of the interventions. DATA SOURCES: We searched PubMed, Medline, Embase, and the Cochrane Library. STUDY SELECTION: We identified articles on provider-based HPV vaccine promotion interventions published in English between 2007 and 2021. DATA EXTRACTION: Information about the interventions' effects and costs was extracted from the studies. A meta-analysis was performed to estimate the pooled intervention effects, including changes in the HPV vaccine initiation rate, HPV vaccine completion rate, and the percentage of patients receiving the next needed dose. RESULTS: We identified 32 studies of provider-based interventions to improve the HPV vaccination rate. The reported intervention strategies included provider training, provider reminders, and provider assessment and feedback. In the meta-analysis, we found significant improvements in the HPV vaccine initiation rate (3.7%) and the percentage of patients receiving the next needed dose (9.4%). LIMITATIONS: Because of the limited number of studies, we were unable to compare the same outcomes between different provider-based approaches. We found the high heterogeneity across studies. The random effects method was applied to adjust for study heterogeneity, the heterogeneity remined high for certain outcomes. CONCLUSIONS: Provider-based interventions were effective in improving HPV vaccination rates. Sustainability and continuous implementation are the central factors that contributed to intervention success.

Examining neighborhood-level hot and cold spots of food insecurity in relation to social vulnerability in Houston, Texas.

Food insecurity is prevalent and associated with poor health outcomes, but little is known about its geographical nature. The aim of this study is to utilize geospatial modeling of individual-level food insecurity screening data ascertained in health care settings to test for neighborhood hot and cold spots of food insecurity in a large metropolitan area, and then compare these hot spot neighborhoods to cold spot neighborhoods in terms of the CDC's Social Vulnerability Index. In this cross-sectional secondary data analysis, we geocoded the home addresses of 6,749 unique participants screened for food insecurity at health care locations participating in CMS's Accountable Health Communities (AHC) Model, as implemented in Houston, TX. Next, we created census-tract level incidence profiles of positive food insecurity screens per 1,000 people. We used Anselin's Local Moran's I statistic to test for statistically significant census tract-level hot/cold spots of food insecurity. Finally, we utilized a Mann-Whitney-U test to compare hot spot tracts to cold spot tracts in relation to the CDC's Social Vulnerability Index. We found that hot spot tracts had higher overall social vulnerability index scores (P <0.001), higher subdomain scores, and higher percentages of individual variables like poverty (P <0.001), unemployment (P <0.001), limited English proficiency (P <0.001), and more. The combination of robust food insecurity screening data, geospatial modeling, and the CDC's Social Vulnerability Index offers a solid method to understand neighborhood food insecurity.

Implementation of an Evidence-Based Intervention with Safety Net Clinics to Improve Mammography Appointment Adherence Among Underserved Women.

The Peace of Mind Program is an evidence-based intervention to improve mammography appointment adherence in underserved women. The aim of this study was to assess effectiveness of the intervention and implementation of the intervention in safety net clinics. The intervention was implemented through a non-randomized stepped wedge cluster hybrid study design with 19 Federally Qualified Health Centers and charity care clinics within the Greater Houston area. A multivariable generalized estimating equation logistic regression was conducted to examine mammography appointment adherence. A survey assessing Consolidated Framework for Implementation Research constructs was also conducted with clinic staff prior to adoption and eight weeks post implementation. One-sided t-tests were conducted to analyze mean score changes between the surveys. A total of 4402 women (baseline period = 2078; intervention period = 2324) were included in the final regression analysis. Women in the intervention period were more likely to attend or reschedule their mammography appointment (OR = 1.30; p < 0.01) than those in the baseline period receiving usual care. Women who completed the intervention were more likely to attend or reschedule their mammography appointment than those who did not complete the intervention (OR = 1.62; p < 0.01). The mammography appointment no-show rates for those in the baseline period, in the intervention period, and who completed the intervention were, respectively, 22%, 19%, and 15%. A total of 15 clinics prior to adoption and eight clinics completed the survey at 8 weeks post implementation A statistically significant mean score decrease was observed in Inner Setting and in two Inner Setting CFIR constructs, Culture-Effort, and Implementation Climate. While the intervention improved mammography appointment adherence, there are opportunities to further integrate Consolidated Framework for Implementation Research constructs. Trial registration: Clinical trials registration number: NCT02296177.

Development, implementation, and evaluation of Teach Back curriculum for community health workers.

Teach Back is a commonly used communication method to improve patient understanding and retention of health information. The method has been shown to be effective in improving patient and healthcare system outcomes, including patient health literacy and hospital readmissions. Community health workers (CHWs) are frontline healthcare workers who can help address patient health and social needs associated with hospital readmissions. However, a gap exists in Teach Back curricula and training methods reflecting the scope of work for CHWs. The objective of this training was to provide CHWs with didactic information and skill building practice curriculum focused on the integration of Teach Back into clinical patient interactions, care coordination, and follow-up support. A multidisciplinary team of academic and clinical partners at a large academic health university developed, implemented, and evaluated a 3-week pilot Teach Back training with CHWs through a quality improvement approach. The CHWs reported overall satisfaction with the training and instructors. The academic clinical partnership allowed the training to be tailored to the daily clinical workflow as reflected in the CHWs agreement that the training was relevant and practical. With the repeated exposure to Teach Back each week, the CHWs also reported an increase in confidence and conviction in using Teach Back. Additional implementation and evaluation of the training curriculum for CHWs is needed to gain further insights into Teach Back and training best practices and translation into practice.

Development of Training Curriculum to Improve Patient Communication Skills and Social Support Among Community Health Workers.

BACKGROUND: Effective provider communication skills are important for patient decision-making and understanding, particularly for those with low health literacy. A gap exists in training methods and curriculum for community health workers (CHWs). Brief description of activity: Through a clinical and academic partnership, pilot training curriculum focused on patient communication skills was developed to align with CHW scope of work. IMPLEMENTATION: The curriculum was implemented in three 2-hour training sessions over WebEx with seven state-certified CHWs. The goal was for CHWs to understand the key elements and application of active listening, Teach Back, and action planning in a clinical setting. The sessions included didactic and skills practice modules for each skill. RESULTS: A survey was distributed to CHWs to evaluate knowledge, skills, and attitudes and reactions to training methods, instructors, and relevance using the Kirkpatrick's evaluation model (Reaction and Learning). Although CHWs agreed that they had actively participated in the training and that the instructors were well-prepared, there was less agreement that the course was relevant. CHWs reported an increase in understanding of active listening and action planning, capability of using Teach Back and providing social support, and ability to teach, whereas a decrease was reported in the capability to use action planning. When probed about training relevance, CHWs felt action listening and Teach Back were relevant, but that action planning was not relevant to their responsibilities. This gap in responsibilities was also acknowledged by the clinical leadership. LESSONS LEARNED: The training allowed the CHWs to build on subsequent skills from previous sessions and to discuss struggles. A need for tools for integrating the skills in the clinical workflow were requested by CHWs and clinical leadership. These tools offer the opportunity to tailor future trainings on communication skills or patient scenarios. Future trainings should include CHWs to provide insight into scope of work. [HLRP: Health Literacy Research and Practice. 2022;6(2):e142-e150.] Plain Language Summary: It is important for community health workers to communicate with patients so that patients can understand information and make their own decisions. There is not enough known about the best way to train CHWs in patient communication. This training was created to help CHWs use three patient communication skills in their clinic.

Predicting health-related social needs in Medicaid and Medicare populations using machine learning.

Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66-0.70) was achieved by the "any HRSNs" outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.

Association of Social Needs and Healthcare Utilization Among Medicare and Medicaid Beneficiaries in the Accountable Health Communities Model.

BACKGROUND: Integration of health-related social needs (HRSNs) data into clinical care is recognized as a driver for improving healthcare. However, few published studies on HRSNs and their impact are available. CMS sought to fill this gap through the Accountable Health Communities (AHC) Model, a national RCT of HRSN screening, referral, and navigation. Data from the AHC Model could significantly advance the field of HRSN screening and intervention in the USA. OBJECTIVE: To present data from the Greater Houston AHC (GH-AHC) Model site on HRSN frequency and the association between HRSNs, sociodemographic factors, and self-reported ED utilization using a cross-sectional design. Analyses included descriptive statistics and multinomial logistic regression. PARTICIPANTS (OR PATIENTS OR SUBJECTS): All community-dwelling Medicare, Medicaid, or dually covered beneficiaries at participating GH-AHC clinical delivery sites were eligible. MAIN MEASURES: Self-reported ED utilization in the previous 12 months served as the outcome; demographic characteristics including race, ethnicity, age, sex, income, education level, number of people living in the household, and insurance type were treated as covariates. HRSNs included food insecurity, housing instability, transportation, difficulty paying utility bills, and interpersonal safety. Clinical delivery site type was used as the clustering variable. KEY RESULTS: Food insecurity was the most common HRSN identified (38.7%) followed by housing instability (29.0%), transportation (28.0%), and difficulty paying utility bills (26.7%). Interpersonal safety was excluded due to low prevalence. More than half of the beneficiaries (56.9%) reported at least one of the four HRSNs. After controlling for covariates, having multiple co-occurring HRSNs was strongly associated with increased risk of two or more ED visits (OR 1.8-9.47 for two to four needs, respectively; p < 0.001). Beneficiaries with four needs were at almost 10 times higher risk of frequent ED utilization (p < 0.001). CONCLUSIONS: To our knowledge, this is only the second published study to report screening data from the AHC Model. Future research focused on the impact of multiple co-occurring needs on health outcomes is warranted.

Barriers and facilitators to implementation of the Accountable Health Communities (AHC) Model: Findings from a between-site qualitative assessment of implementation strategies.

Introduction: A multitude of HRSN interventions are undergoing testing in the U.S., with the CMS Accountable Health Communities (AHC) Model as the largest. HRSN interventions typically include screening for social needs, referral to community resources, and patient navigation to ensure needs are met. There is currently a paucity of evidence on implementation of HRSN interventions. The Consolidated Framework for Implementation Research (CFIR) is a determinant framework widely used to plan and assess implementation. To the authors knowledge, there are no published studies assessing CFIR constructs for HRSN intervention implementation in the U.S. In the Assessment step of the Strengthening Peer AHC Navigation (SPAN) model, a between-site qualitative assessment methodology was used to examine implementation within and between AHC bridge organizations (BOs) within six ERIC implementation strategies identified by the authors based on AHC Model requirements. Objective: Our aim was to identify and present between-site barriers and facilitators to AHC Model implementation strategies. Design: A multi-site qualitative analysis methodology was used. CFIR determinants were linked to six Expert Recommendations for Implementing Change (ERIC) strategies: staff training, identify and prepare champions, facilitation, community resource engagement (alignment through advisory boards and working groups), data systems, and quality monitoring and assurance. Interviews were analyzed using thematic content analysis in NVivo 12 (QSR International). Setting: Five health-related bridge organizations participating in the AHC Model. Results: Fifty-eight interviews were completed with 34 staff and 24 patients or patient proxies. Facilitators were identified across five of the six ERIC strategies. Barriers were identified across all six. While organizations found the AHC Model compatible and facilitators to implementation included previous experience, meeting patient needs and resources, and leadership engagement and support, a number of barriers presented challenges to implementation. Issues with adequate staff training, staff skills to resolve HRSN, including patient communication and boundary spanning, setting staff goals, beneficiary caseloads and measurement of progress, data infrastructure (including EHR), available resources to implement and differences in perceptions between clinical delivery site (CDS), and CSP of how to measure and resolve HRSN. Conclusions and relevance: The conduct of a pre-implementation readiness assessment benefited from identifying CFIR determinants linked to various ERIC implementation strategies.

Stakeholder Engagement in Adoption, Implementation, and Sustainment of an Evidence-Based Intervention to Increase Mammography Adherence Among Low-Income Women.

Multi-level organizational stakeholder engagement plays an important role across the research process in a clinical setting. Stakeholders provide organizational specific adaptions in evidence-based interventions to ensure effective adoption, implementation, and sustainability. Stakeholder engagement strategies involve building mutual trust, providing clear communication, and seeking feedback. Using constructs from the Consolidated Framework for Implementation Research and The International Association for Public Participation spectrum, a conceptual framework was created to guide stakeholder engagement in an evidence-based intervention to increase mammography appointment adherence in underserved and low-income women. A document review was used to explore the alignment of the conceptual framework with intervention activities and stakeholder engagement strategies. The results indicate an alignment with the conceptual framework constructs and a real-world application of stakeholder engagement in a mammography evidence-based intervention. The conceptual framework and stakeholder engagement strategies can be applied across a range of community-based cancer programs and interventions, organizations, and clinical settings.

Adoption of an Evidence-Based Intervention for Mammography Screening Adherence in Safety Net Clinics.

Through an academic-community partnership, an evidence-based intervention to reduce mammography appointment no-show rates in underserved women was expanded to safety net clinics. The partnership implemented four strategies to improve the adoption and scale-up of evidence-based interventions with Federally Qualified Health Centers and charity care clinics: (1) an outreach email blast targeting the community partner member clinics to increase program awareness, (2) an adoption video encouraging enrollment in the program, (3) an outreach webinar educating the community partner member clinics about the program, encouraging enrollment and outlining adoption steps, and (4) an adoption survey adapted from Consolidated Framework for Implementation Research constructs from the Cancer Prevention and Control Research Network for cancer control interventions with Federally Qualified Health Centers. The development of academic-community partnerships can lead to successful adoption of evidence-based interventions particularly in safety net clinics.

The beneficial effect of providing kidney transplantation information on transplantation status differs between for-profit and nonprofit dialysis centers.

Informing end-stage kidney disease patients about kidney transplantation options increases the likelihood of kidney transplant waiting list (WL) enrollment and live donor kidney transplant (LDKT) receipt. Patients in for-profit dialysis centers have lower rates of WL enrollment and LDKT receipt. This study examined if the ownership status of dialysis centers modified the association between informing patients about transplantation options and patients' transplantation status. Multilevel analysis using mixed-effect multinomial logistic regression was performed using the United States Renal Data System (USRDS) data (January 2005 to December 2017). The study showed that informing patients improved the odds of WL enrollment and LDKT receipt. However, the effect of informing patients on transplantation status was less pronounced at for-profit as compared with nonprofit centers (Nonprofit: WL enrollment OR: 2.23 [95% CI: 2.07-2.40], and LDKT receipt OR: 3.35 [95% CI: 2.65-4.25]. For-profit: WL enrollment OR: 1.73 [95% CI: 1.66-1.79], and LDKT receipt OR: 2.35 [95% CI: 2.08-2.66]), although the odds of informing patients was higher for for-profit centers, and type of patients informed were similar across both types of centers. Information provided by for-profit centers was potentially less effective than those provided by nonprofit centers. Standardized guidelines for transplantation information provision are needed in order to ensure similar informational quality across centers.

Understanding Pediatric Surgery Cancellation: Geospatial Analysis.

BACKGROUND: Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients' and families' behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood. OBJECTIVE: This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children's Hospital Medical Center (CCHMC) and of Texas Children's Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods. METHODS: The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients' health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients' socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set. RESULTS: Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate. CONCLUSIONS: Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children's surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account.

The association of social determinants of health with health outcomes.

OBJECTIVES: This study explored the contributions of social determinants of health (SDOH) to measures of population health-specifically cost, hospitalization rates, rate of emergency department utilization, and health status-in Texas. STUDY DESIGN: The study associated common SDOH metrics from public data sources (county specific) with health plan enrollment data (including demographics, counties, and zip codes) and medical and pharmaceutical annual claims data. METHODS: Following correlation analyses to reduce variables, the contribution of each SDOH individually and by category to the health outcomes was evaluated. Separate matrices for age populations (under age 19, general population [all ages], and ≥ 65 years) were created with assigned weights of influence for categories and the factors within each category. RESULTS: The contributions of the categories varied by population, confirming that different SDOH influence populations to varying degrees. This was reflected in each model. The largest contributor to cost for the general population and for the group 65 years and older was factors grouped as health outcomes (such as perceived health), at 43.5% contribution and 37.7% contribution, respectively. Yet for the population younger than 19 years, the largest contributor to cost was socioeconomic factors (such as unemployment rate), at 40.2%. The other performance measures also varied by population and the mix and weight of determinants. CONCLUSIONS: This study and the developed population-based matrices can provide a valuable framework for reporting the impact of SDOH on health care quality. The variation suggests the need for further research on how age groups react to the social environment.

Implementing an inpatient integrative medicine consult service for children with pain: A qualitative analysis.

OBJECTIVES: Complementary and integrative medicine (CIM) therapies show clinical benefits with minimal side effects, yet challenges to effective integration in hospital settings remain. The current study aimed to better understand the process of integration of CIM therapies at a large urban pediatric hospital from the perspectives of providers, parents, and administrators. DESIGN: The study employed an applied medical ethnography. SETTING: The ethnography was conducted before, during, and after an Integrative Medicine Pain Consult Service (IM Pilot) was implemented at a large urban pediatric hospital during the spring of 2017. MAIN OUTCOME MEASURES: Fieldwork interviews, participant observations, and document review captured aspects of the integration of CIM over a 6-month study period. Ethnographic analysis included thematic content analysis. Participants included providers (n = 10), administrators (n = 5), and parents of patients (n = 11). RESULTS: Emergent themes from analysis of the interviews and field notes were organized according to the socio-ecological model. Themes included facilitating factors for CIM pain management at the intrapersonal and community levels (Alignment with Parental Perceptions of Child Needs and Provider Desire to Offer Care, Alignment of CIM with Spiritual Beliefs and Community Norms) and barriers at the interpersonal, organizational, and political levels (Inter-professional Challenges, Lack of Logistics in Place for Referrals and Triaging Patients with Pain, Lack of Remuneration/Insurance Reimbursement for Care). CONCLUSIONS: To address barriers, future efforts to implement integrative pain management programs in pediatric hospital settings may consider testing implementation strategies, including engaging program champions and family advocates, providing education on CIM professions and therapies to hospital staff, and billing for provider time rather than individual CIM therapies.

Developing and Evaluating a Quality Improvement Intervention to Facilitate Patient Navigation in the Accountable Health Communities Model.

Introduction: The Accountable Health Communities (AHC) Model was designed to address the health-related social needs of Centers for Medicare & Medicaid Services beneficiaries. Bridge organizations across the AHC Model have identified lack of technical assistance and peer planning as potential barriers to Model success, particularly around patient navigation. The technical assistance and peer planning literature lacks an organizing, conceptual framework, but implementation science frameworks could serve as useful guides. The Strengthening Peer AHC Navigation (SPAN) research protocol seeks to fill this gap and will apply three implementation science frameworks, Consolidated Framework for Implementation Research, Intervention Mapping, and the Expert Recommendations for Implementing Change compilation, to develop a multi-level quality improvement intervention and evaluate the impact of peer planning on Model outcomes. The aims of the SPAN study are to implement and evaluate a novel multi-level quality improvement intervention to improve AHC implementation and navigation milestones through structured peer planning and to provide successful technical assistance for the AHC Model. Methods and Analysis: The quality improvement intervention is outlined in four Tasks: (1) Assessment - to conduct an assessment of each bridge organization's current implementation, needs, and readiness in AHC Model navigation activities; (2) Planning - to engage in a peer planning approach to build capacity for AHC Model navigation activities; (3) Implementation with technical assistance - Co-creation of a quality improvement protocol for AHC Model navigation activities; and (4) Evaluation - measure the impact of the peer planning and technical assistance approach. Alongside the development and implementation of the quality improvement intervention, this protocol describes a mixed method, convergent parallel study design which will be used to evaluate whether the quality improvement intervention will lead to better outcomes. Tasks will be replicated with five bridge organizations participating in the AHC Model. Discussion: This research protocol provides a framework that can be used to conduct structured peer planning with technical assistance for social needs programs. This study will provide data on both implementation and outcomes which eventually may impact healthcare cost and utilization.