Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Gruß I

PROVIDER: S-EPMC7875127 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Vietnam has a smoking prevalence that is the second highest among Southeast Asian countries (SEACs). According to the World Health Organization (WHO), most reductions in mortality from tobacco use in the near future will be achieved through helping current users quit. Yet, largely due to a lack of research on strategies for implementing WHO-endorsed treatment guidelines in primary care settings, services to treat tobacco dependence are not readily available to smokers in low middle-income countries (LMICs) like Vietnam. The objective of this study is to conduct a cluster randomized controlled trial that compares the effectiveness of two system-level strategies for implementing evidence-based guidelines for the treatment of tobacco use in 26 public community health centers (CHCs) in Vietnam. METHODS/DESIGN:The current study will use a cluster-randomized design and multiple data sources (patient exit interviews, provider and village health worker (VHW) surveys, and semi-structured provider/VHW interviews) to study the process of adapting and implementing clinical practice guidelines in Vietnam and theory-driven mechanisms hypothesized to explain the comparative effectiveness of the two strategies for implementation. CHCs will be randomly assigned to either of the following: (1) training plus clinical reminder system (TC) or (2) TC?+?referral to a VHW (TCR) for three in person counseling sessions. The primary outcome is provider adherence to tobacco use treatment guidelines. The secondary outcome is 6-month biochemically verified smoking abstinence. DISCUSSION:The proposed implementation strategies draw on evidence-based approaches and a growing literature that supports the effectiveness of integrating community health workers as members of the health care team to improve access to preventive services. We hypothesize that the value of these implementation strategies is additive and that incorporating a referral resource that allows providers to delegate the task of offering counseling (TCR) will be superior to TC alone in improving delivery of cessation assistance to smokers. The findings of this research have potential to guide large-scale adoption of promising strategies for implementing and disseminating tobacco use treatment guidelines throughout the public health system in Vietnam and will serve as a model for similar action in other LMICs. TRIAL REGISTRATION:NCT01967654.

Project description:ImportanceResponding 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.ObjectiveTo 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 participantsCross-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.ExposuresSocial risk screening documented in the electronic health record.Main outcomes and measuresCommunity-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.ResultsThe 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 relevanceAlthough 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.

Project description: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.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets