Assess Social determinantS using EHRs in Safety net clinicS: Diabetes Outcomes

 描述（由申请人提供）：尽管有大量证据表明，个人和社区层面的“健康社会决定因素”（SDH）对糖尿病（DM）相关治疗和健康结局产生负面影响，但大多数电子健康记录（EHR）中的SDH数据采集不足。认识到这一点，医学研究所（IOM）委员会最近推荐了一组候选SDH数据域，以纳入所有EHR。我们的团队以前表明，SDH数据可以在EHR中记录;关键的下一步将是学习如何以临床有意义的方式在EHR中集成和呈现SDH数据。获得关于SDH的可操作信息可以使初级保健团队更全面地了解影响患者护理和结果的因素，并为临床决策，小组管理和转诊外部服务提供信息。在卫生保健安全网中，对SDH信息的需求最大，其弱势患者比美国普通人群有更多的SDH相关风险，更高的DM发病率和更差的DM结局。因此，我们建议通过在20个社区卫生服务中心进行的一项群集随机化务实试验，评估如何最有效地将IOM推荐的领域纳入EHR，以优化其在糖尿病预防和护理方面的临床效用。使用混合方法，我们将确定修改现有SDH数据文档工具的具体方法，以更有效地：将SDH数据集成到DM预防/护理中;优化工作流程;并支持临床决策。我们将根据这些发现，通过以用户为中心的设计过程，增强和调整SDH数据工具，以优化护理团队对工具的使用和接受。我们预计将创建增强的SDH数据工具，这些工具将使用经过验证的临床决策支持（CDS）方法，提示特定的临床行动或转诊，增强面板管理，并提高转诊跟踪能力。我们将通过双臂、随机、交错设计实施适应性工具。我们将研究将SDH数据整合到工作流程中、数据工具的利用以及潜在的不良后果。我们将评估所实施的增强工具对DM相关临床决策以及对DM预防、护理和健康结果的影响（例如，糖尿病发病率;血压、糖化血红蛋白、低密度脂蛋白）。这项工作利用了与OCHIN，Inc.建立的合作伙伴关系，全国领先的电子健康记录工具开发商，为社区卫生服务中心和其他社区合作伙伴。这些合作伙伴关系将研究结果定位为立即通知SDH数据记录和使用，并产生超越DM的持久护理改善。我们的团队包括不同的专家，IOM SDH委员会成员和社区临床医生。我们及时的结果将为将SDH数据整合到不同护理系统的临床工作流程中的实践和政策提供信息。

 DESCRIPTION (provided by applicant): Despite substantial evidence that individual- and neighborhood-level `social determinants of health' (SDH) negatively impact diabetes (DM)-related treatment and health outcomes, SDH data are insufficiently captured in most electronic health records (EHRs). Recognizing this, an Institute of Medicine (IOM) committee recently recommended a set of candidate SDH data domains for inclusion in all EHRs. Our team previously showed that SDH data can feasibly be documented in EHRs; the crucial next step will be to learn how to integrate and present SDH data in EHRs in clinically meaningful ways. Access to actionable information on SDH could give primary care teams a more complete understanding of the factors impacting patients' care and outcomes, and inform clinical decision-making, panel management, and referrals to external services. The need for information on SDH is greatest in the health care safety net, whose vulnerable patients have more SDH-related risks, higher DM incidence, and worse DM outcomes than the general US population. Thus, we propose to assess how the IOM-recommended domains can most effectively be incorporated and presented in the EHR to optimize their clinical utility for DM prevention and care, through a cluster-randomized pragmatic trial in 20 CHCs. Using mixed methods, we will identify specific ways to modify existing SDH data documentation tools to more effectively: integrate SDH data into DM prevention/care; optimize workflows; and support clinical decision-making. We will enhance and adapt the SDH data tools based on these findings, through a user- centered design process, to optimize care teams' use of and acceptance of the tools. We anticipate creating enhanced SDH data tools that prompt specific clinical actions or referrals, augment panel management, and improve referral tracking capabilities, using proven Clinical Decision Support (CDS) approaches. We will implement the adapted tools via a two-arm, randomized, staggered design. We will study integration of the SDH data into workflows, utilization of the data tools, and potential adverse outcomes. We will evaluate the impact of the implemented enhanced tools on DM-related clinical decision-making, and on DM prevention, care, and health outcomes (e.g., rates of incident DM; BP, A1c, LDL), in a segmented time-series analysis. This work leverages established partnerships with OCHIN, Inc., the nation's leading developer of EHR tools for CHCs, and other community partners. These partnerships position study results to immediately inform SDH data documentation and use, and to yield lasting care improvements extending beyond DM. Our team includes diverse experts, IOM SDH committee members, and community clinicians. Our timely results will inform practice and policy related to integrating SDH data into clinical workflows in diverse care systems.