+1 Recommend
1 collections

      To submit your manuscript, please click here

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Using Electronic Health Record–Based Clinical Decision Support to Provide Social Risk–Informed Care in Community Health Centers: Protocol for the Design and Assessment of a Clinical Decision Support Tool


      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



          Consistent and compelling evidence demonstrates that social and economic adversity has an impact on health outcomes. In response, many health care professional organizations recommend screening patients for experiences of social and economic adversity or social risks—for example, food, housing, and transportation insecurity—in the context of care. Guidance on how health care providers can act on documented social risk data to improve health outcomes is nascent. A strategy recommended by the National Academy of Medicine involves using social risk data to adapt care plans in ways that accommodate patients’ social risks.


          This study’s aims are to develop electronic health record (EHR)–based clinical decision support (CDS) tools that suggest social risk–informed care plan adaptations for patients with diabetes or hypertension, assess tool adoption and its impact on selected clinical quality measures in community health centers, and examine perceptions of tool usability and impact on care quality.


          A systematic scoping review and several stakeholder activities will be conducted to inform development of the CDS tools. The tools will be pilot-tested to obtain user input, and their content and form will be revised based on this input. A randomized quasi-experimental design will then be used to assess the impact of the revised tools. Eligible clinics will be randomized to a control group or potential intervention group; clinics will be recruited from the potential intervention group in random order until 6 are enrolled in the study. Intervention clinics will have access to the CDS tools in their EHR, will receive minimal implementation support, and will be followed for 18 months to evaluate tool adoption and the impact of tool use on patient blood pressure and glucose control.


          This study was funded in January 2020 by the National Institute on Minority Health and Health Disparities of the National Institutes of Health. Formative activities will take place from April 2020 to July 2021, the CDS tools will be developed between May 2021 and November 2022, the pilot study will be conducted from August 2021 to July 2022, and the main trial will occur from December 2022 to May 2024. Study data will be analyzed, and the results will be disseminated in 2024.


          Patients’ social risk information must be presented to care teams in a way that facilitates social risk–informed care. To our knowledge, this study is the first to develop and test EHR-embedded CDS tools designed to support the provision of social risk–informed care. The study results will add a needed understanding of how to use social risk data to improve health outcomes and reduce disparities.

          International Registered Report Identifier (IRRID)


          Related collections

          Most cited references90

          • Record: found
          • Abstract: found
          • Article: not found

          Achieving integration in mixed methods designs-principles and practices.

          Mixed methods research offers powerful tools for investigating complex processes and systems in health and health care. This article describes integration principles and practices at three levels in mixed methods research and provides illustrative examples. Integration at the study design level occurs through three basic mixed method designs-exploratory sequential, explanatory sequential, and convergent-and through four advanced frameworks-multistage, intervention, case study, and participatory. Integration at the methods level occurs through four approaches. In connecting, one database links to the other through sampling. With building, one database informs the data collection approach of the other. When merging, the two databases are brought together for analysis. With embedding, data collection and analysis link at multiple points. Integration at the interpretation and reporting level occurs through narrative, data transformation, and joint display. The fit of integration describes the extent the qualitative and quantitative findings cohere. Understanding these principles and practices of integration can help health services researchers leverage the strengths of mixed methods. © Health Research and Educational Trust.
            • Record: found
            • Abstract: found
            • Article: not found

            Qualitative data analysis for health services research: developing taxonomy, themes, and theory.

            To provide practical strategies for conducting and evaluating analyses of qualitative data applicable for health services researchers. DATA SOURCES AND DESIGN: We draw on extant qualitative methodological literature to describe practical approaches to qualitative data analysis. Approaches to data analysis vary by discipline and analytic tradition; however, we focus on qualitative data analysis that has as a goal the generation of taxonomy, themes, and theory germane to health services research. We describe an approach to qualitative data analysis that applies the principles of inductive reasoning while also employing predetermined code types to guide data analysis and interpretation. These code types (conceptual, relationship, perspective, participant characteristics, and setting codes) define a structure that is appropriate for generation of taxonomy, themes, and theory. Conceptual codes and subcodes facilitate the development of taxonomies. Relationship and perspective codes facilitate the development of themes and theory. Intersectional analyses with data coded for participant characteristics and setting codes can facilitate comparative analyses. Qualitative inquiry can improve the description and explanation of complex, real-world phenomena pertinent to health services research. Greater understanding of the processes of qualitative data analysis can be helpful for health services researchers as they use these methods themselves or collaborate with qualitative researchers from a wide range of disciplines.
              • Record: found
              • Abstract: found
              • Article: not found

              Effect of clinical decision-support systems: a systematic review.

              Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Agency for Healthcare Research and Quality.

                Author and article information

                JMIR Res Protoc
                JMIR Res Protoc
                JMIR Research Protocols
                JMIR Publications (Toronto, Canada )
                October 2021
                8 October 2021
                : 10
                : 10
                : e31733
                [1 ] Kaiser Permanente Center for Health Research Portland, OR United States
                [2 ] OCHIN, Inc. Portland, OR United States
                [3 ] University of California San Francisco San Francisco, CA United States
                Author notes
                Corresponding Author: Rachel Gold rachel.gold@ 123456kpchr.org
                Author information
                ©Rachel Gold, Christina Sheppler, Danielle Hessler, Arwen Bunce, Erika Cottrell, Nadia Yosuf, Maura Pisciotta, Rose Gunn, Michael Leo, Laura Gottlieb. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 08.10.2021.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.

                : 22 July 2021
                : 26 July 2021
                : 28 July 2021
                : 29 July 2021
                Custom metadata
                This paper was peer reviewed by the Center for Scientific Review Special Emphasis Panel - Leveraging Health Information Technologies (Health IT) to Address Minority Health and Health Disparities (National Institutes of Health, USA).

                social determinants of health,decision support systems, clinical,electronic health records,community health centers,health status disparities


                Comment on this article