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.