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Culture Of Health

Integrating Data On Social Determinants Of Health Into Electronic Health Records

  1. Michael N. Cantor ([email protected]) is clinical director, New York University Langone Health DataCore; an associate professor of population health and medicine; and director of clinical research informatics, all at the New York University School of Medicine, in New York City.
  2. Lorna Thorpe is a professor, director of the Division of Epidemiology, and vice chair of strategy and planning in the Department of Population Health at the New York University School of Medicine.
PUBLISHED:Free Accesshttps://doi.org/10.1377/hlthaff.2017.1252


As population health becomes more of a focus of health care, providers are realizing that data outside of traditional clinical findings can provide a broader perspective on potential drivers of a patient’s health status and can identify approaches to improving the effectiveness of care. However, many challenges remain before data related to the social determinants of health, such as environmental conditions and education levels, are as readily accessible and actionable as medical data are. Key challenges are a lack of consensus on standards for capturing or representing social determinants of health in electronic health records and insufficient evidence that once information on them has been collected, social determinants can be effectively addressed through referrals or other action tools. To address these challenges and effectively use social determinants in health care settings, we recommend creating national standards for representing data related to social determinants of health in electronic health records, incentivizing the collection of the data through financial or quality measures, and expanding the body of research that measures the impact of acting on the information collected.


Social determinants of health are defined as “the complex, integrated, and overlapping social structures and economic systems that are responsible for most health inequities…[including] the social environment, physical environment, health services, and structural and societal factors.”1 Social factors account for 25–60 percent of deaths in the United States in any given year according to results from various meta-analyses,2,3 depending on the scope of the definition they used. At a broad level, social determinants can be divided into two categories: individual-level determinants specific to a patient, such as education level, employment status, or housing situation; and community-level determinants, which measure environmental, neighborhood, or socioeconomic characteristics (such as air pollution levels, housing quality, and the unemployment rate) that affect a broad population. Different studies have shown individual- and community-level social determinants of health to affect multiple health-related outcomes across a variety of populations and age groups.48 Moreover, in the era of accountable care organizations (ACOs) and value-based payment for care, addressing these determinants has become a priority for many health care systems.

Concurrent with a sharper focus on the relevance of social determinants of health to health care, the benefit of using electronic health records (EHRs) for managing the health of populations, not just individual patients, has attracted attention. Many health care systems have begun to explore ways to integrate data related to social determinants with patients’ clinical records.9,10 Initiatives such as the Medicaid and CHIP Payment and Access Commission’s Delivery System Reform Incentive Payment Program, which aims to fundamentally redesign state Medicaid programs, are providing large financial incentives to bring social determinants to the attention of a much broader group of health care providers—beyond the community health centers and safety-net providers where much of the work about the determinants has traditionally been centered. Responding to this type of demand, EHR vendors have begun to develop new tools for capturing and addressing the determinants and using them for population health management. Examples include as Cerner’s HealtheIntent11 and Epic’s Healthy Planet.12 Though designed to address an overlapping set of issues related to social determinants, these and other EHR tools are not following any overarching strategy or standards to ensure that data captured from disparate systems can be easily exchanged between or used by other providers, patients, or social service organizations. Multiple challenges therefore remain before data related to the determinants are as readily accessible as laboratory results or vital signs and are integrated in a meaningful way. In this article we identify specific technical and implementation challenges that must be addressed if the social determinants are to be reliably integrated into EHRs on a wide scale, and we offer a number of potential policy solutions.

Technical Issues: Developing Standards

In 2014 the Institute of Medicine published two reports that made recommendations on which social and behavioral-related measures to use for data collection in EHRs.13,14 However, little consensus has since emerged on the measures that can or should be captured in EHR systems. Data on individual-level determinants are currently collected using a variety of instruments, including the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE),15 the Accountable Health Communities Screening (AHCS) tool,16 and a myriad of locally designed tools from a variety of organizations,17 some of which are tailored for use with specific populations (exhibit 1). In addition to individual-level determinants, many institutions are also interested in capturing data on community-level factors because of their usefulness in predicting health risks, 18 such as the likelihood of surviving an out-of-hospital cardiac arrest.19

Exhibit 1 Categories of data related to individual-level social determinants of health across three common sources

Generally collected in electronic health records
Race/ethnicityIOM reports; PRAPARE
Tobacco useIOM reports
Alcohol useIOM reports
Primary languagePRAPARE
Veteran statusPRAPARE
Health insurancePRAPARE
DepressionIOM reports
AddressIOM reports; PRAPARE
Safety issues
Intimate partner violenceIOM reports; PRAPARE; AHCS
Financial issues
Financial strain (including food insecurity)IOM reports; PRAPARE; AHCS
Transportation needsPRAPARE; AHCS
Housing insecurityPRAPARE; AHCS
Housing qualityAHCS
Employment statusPRAPARE
Utility needsAHCS
Behavioral health
StressIOM reports; PRAPARE
Social isolationIOM reports; PRAPARE
Physical activityIOM reports
Other demographic characteristics
Education levelIOM reports; PRAPARE
Migrant worker statusPRAPARE
History of incarcerationPRAPARE
Refugee statusPRAPARE
Family sizePRAPARE

SOURCE Authors’ analysis of data from the sources described below. NOTES “IOM reports” are the Institute of Medicine reports in notes 13 and 14 in text. “PRAPARE” is Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (see note 15 in text). “AHCS” is Accountable Health Communities Screening Tool (see note 16 in text).

Despite this level of interest, no uniform, accepted data model exists for representing these determinants in EHRs. Data standardization is important for implementing appropriate clinical decision support interventions to address social determinants of health, both within an EHR system and across different systems. It is also important for valid data aggregation across practices, EHR systems, and communities. Different biomedical standards exist for lab values, diagnoses, or procedures. Clinicians’ use of these standards is relatively straightforward, based on operational and regulatory requirements (for example, International Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10], codes for diagnoses and Current Procedural Terminology [CPT] codes for procedures and billing). The social determinants of health can be represented in demographic data elements (such as housing status), diagnoses (homelessness), or procedures (referral to supportive housing). However, no single current biomedical standard captures the breadth of information necessary for documenting the determinants in a manner appropriate for clinical care, quality improvement, and research.

The result of this situation is that existing clinical standards lack explicit translation algorithms to comprehensively integrate results of a screening tool for social determinants of health into an EHR as a relevant clinical finding or problem.20 Currently, the sets of diagnosis codes used to represent the results of screening for the determinants are developed case by case, so some institutions may use different codes or different levels of specificity for the same problems. Current codes are also imprecise. For example, the ICD-10 Z59.0 code (problems related to housing and economic circumstances) contains several diagnoses related to social determinants of health, such as Z59.4 (lack of adequate food and safe drinking water). To permit more precise documentation and appropriate tailored referrals, this code should most likely be divided into two different codes, one for food insecurity and the other for lack of safe water. CPT codes are also imprecise, with codes 96150–1 (health and behavior assessment) and 96152–55 (health and behavior intervention) being the codes that come closest to addressing social determinants. While the existence of a CPT code alone does not guarantee adequate reimbursement to incentivize its use in a clinical encounter, it does establish a starting point for policies that would do so.

The Office of the National Coordinator for Health Information Technology (ONC), recognizing that standardization is an essential first step in creating interoperable systems,21 recently drafted 2017 guidelines for the collection of eight different individual-level social determinant domains,22 all of which are mapped to codes in an established standard, the Logical Observation Identifiers Names and Codes (LOINC). The Social Interventions Research and Evaluation Network (SIREN)23 at the University of California San Francisco is also leading efforts to develop a proposed standard for collecting data on social determinants of health. Specifically, SIREN convened a multistakeholder meeting on November 9, 2017, to develop a road map for developing standards pertinent to including social determinants of health, with input from standards development organizations, government agencies, consumer organizations, health care providers, payers, EHR vendors, and the nonprofit sector, among others. The road map suggests that the ONC will need to play a major role in supporting the evolution of standards as the roles of certain determinants are better defined and given more or less emphasis. We anticipate that the standards will be a framework specifying the use of elements from several existing biomedical vocabularies. That framework will serve as a common foundation on which to build data collection and analysis efforts. The establishment of this framework will give institutions the flexibility to customize existing instruments or develop their own if needed, with the knowledge that the domains addressed and the data collected by these instruments will all be mapped consistently to well-specified concepts in the EHR’s database. For example, a checklist in PRAPARE asks one question about food insecurity, but the AHCS separates food insecurity into two questions. Though the questions and answers are phrased differently, both references to food insecurity can be mapped to “screen positive for food insecurity” or a related concept in the EHR database.

Implementation Issues: Workflows And Measuring Impact

Information on community-level determinants such as poverty, unemployment, and even air pollution rates is generally available as structured data from the Census Bureau or other agencies. Such information has the potential for seamless linkage to EHR data without any disruption of clinical workflow (assuming that patients’ addresses are accurate and geocoded). HealthLandscape9 and other vendors of geospatial analysis, as well as the Factors Affecting Communities and Enabling Targeted Services (FACETS) database,24 a compendium of census tract–level data in New York City, have taken the approach of linking community data to EHR data. This approach allows providers to view community data “on demand,” when a patient’s address is sent from the EHR to the database through a web service. Providers’ need to know about community-level determinants is generally less urgent at the time of a particular clinical encounter, although their presence may trigger a prompt to screen at-risk patients for social needs. Community-level determinants also may be useful at the system level for enhancing the performance of predictive models or targeting specific interventions.18,25 They are also of interest to researchers who seek to better understand the roles that community context plays in determining health outcomes, and to policy makers who want to design interventions to address these determinants.

In contrast, collecting data on individual-level determinants is more challenging, because it generally relies on clinics to gather information through screening checklists or surveys when patients come seeking treatment. Additionally, individual-level determinants can change rapidly, particularly if patients are successfully referred for services. For example, a patient with acute food insecurity at their first visit may no longer have similar needs at a later date after receiving assistance enrolling in the Supplemental Nutrition Assistance Program and referrals to a food pantry. Individual-level determinants are generally a higher priority for clinicians because they are immediately actionable (for example, referrals to community services).

With standardization, both community- and individual-level determinants can be incorporated into clinical decision support tools to help providers prioritize the highest-impact issues at a particular visit.

Investigators at OCHIN, a nonprofit community-based organization that manages EHRs for over 440 community health centers in nineteen states, have described their experience capturing and acting on social determinants of health using a standardized approach.10 They stress the importance of balancing local data collection needs with the use of national standards and validated tools, the latter being especially important if data related to social determinants become a requirement for EHR certification or reporting. Other important issues include “closing the loop” on community resource referrals, balancing the display of data related to the determinants with that of traditional clinical data, and integrating the collection of data related to the determinants into diverse workflows. OCHIN’s investigators noted that “little…guidance currently exists” on how to “facilitate systematic [social determinants of health] screening in primary care settings using EHR-based tools,”10(p444) reflecting the current early state of integrating the determinants into EHRs.

As is the case with any other change in how a clinic is run, input from stakeholders—clinicians, administrators, and patients—can help determine locally relevant areas of focus and best approaches for data collection (for example, paper versus electronic). Broad stakeholder input is a hallmark of successful programs for collecting data related to social determinants of health, such as those developed at OCHIN.10 Changes to workflows may include, for example, using private interview spaces instead of having an assistant ask questions related to social determinants in the middle of a crowded waiting area. The content of a screening tool for the determinants may also vary based on patient populations. Initiatives to collect data related to the determinants can also involve questionnaires administered by nurses, medical assistants, or other staff or patient self-reported data, which would allow more time during an office visit for the provider to focus on the results of the collected data or their associated analytics.

The body of evidence related to impacts of screening for social determinants of health and successes in connecting patients to needed resources is small, and results are mixed—with favorable outcomes noted most frequently in pediatric populations. Across several studies among children, screening for and acting on the determinants have shown improved overall health,26 better access to community resources,27 and improved obesity outcomes.28 For example, at the Center for Health and Community at the University of California San Francisco, a randomized controlled trial of screening for social needs and providing written information about community resources versus active, in-person help with referrals to services showed both a reduction in social needs and improved overall health at four months in the trial’s active, in-person arm.26 Interventions to address the social needs of adult emergency department (ED) patients have had mixed results in reducing subsequent ED use,29 and similar efforts among patients with metabolic syndrome have had only modest effects on outcomes related to chronic disease management.30

Policy Implications: Developing Infrastructure And Incentives

Addressing determinants in the clinical setting requires both additional infrastructure and a stronger evidence base.

Perhaps the most pressing question an institution must ask before integrating data related to social determinants of health into its EHR is what actions need to be taken to address the determinants once data are collected. Addressing the determinants in the clinical setting ultimately requires both additional infrastructure and a stronger evidence base. Infrastructure can support tasks such as referring patients to community services, tracking the results of these referrals, maintaining updated lists of community service providers, and updating patients’ quality ratings of service providers. The breadth of these tasks has led several vendors (for example, Healthify31 and NowPow32) to offer integrated online platforms for connecting patients to services, including a curated set of community service providers, integration with EHR systems, and communication platforms that enable community services and medical providers to “close the loop” on referrals. Ultimately, however, additional clinic-level investments in staff and technology are needed to fully realize the benefits of these tools.

Regarding evidence, studies have confirmed that data related to social determinants of health can improve predictive models and give a more complete understanding of a patient’s life circumstances. Yet stronger evidence is needed to demonstrate that referrals to community services will lead to better clinical outcomes as they help address patients’ social needs. Currently, evaluation criteria for referral programs based on social determinants focus mostly on process measures, such as the number of patients referred to specific services or whether regulatory requirements for referral are met. Yet most rewards in the era of accountable care are based on improving clinical outcomes and reducing expenditures. Efforts to improve social situations without direct evidence of their link to clinical improvements might not pass muster with payers. Establishing universal reimbursement standards for CPT codes (or CPT modifiers) related to screening for or acting on social determinants or health, or incorporating social determinants of health into risk-adjustment models,33 can provide both incentives and resources to integrate the determinants into clinical care while also refining the critically needed evidence base so that the effectiveness of interventions can be better understood. Developing this evidence base requires a research agenda that focuses on the evaluation of both implementation34 and outcomes.35


Widespread integration of data related to social determinants of health into electronic health records offers tremendous potential for improved care and health, including a better understanding of the influence of neighborhood characteristics on health, improved connections between providers of medical care and community services, and a chance to treat the “whole patient.” Expanding or adapting existing standards to capture data accurately and facilitating the adoption of these standards by EHR vendors are essential first steps on the pathway toward systematic collection of data on the determinants. Other priorities include transforming clinical staff members’ tasks so that they can successfully link patients to community services and conducting rigorous research to evaluate the health and social impacts of referral efforts. Only when these steps are taken will we be able to achieve the goal of improving outcomes for both patients and populations.


Michael Cantor is supported in part by a grant from the National Center for Advancing Translational Sciences, National Institutes of Health, to New York University (Grant No. UL1 TR001445). The authors thank Elaine Meyer for her thoughtful review of the manuscript.


  • 1 Centers for Disease Control and Prevention. NCHHSTP Social Determinants of Health: definitions [Internet]. Atlanta (GA): CDC; [last updated 2014 Mar 21; cited 2018 Mar 2]. Available from: https://www.cdc.gov/nchhstp/socialdeterminants/definitions.html Google Scholar
  • 2 Hieman HJ , Artiga S . Beyond health care: the role of social determinants in promoting health and health equity [Internet]. Menlo Park (CA): Henry J. Kaiser Family Foundation; 2015 Nov 4 [cited 2018 Feb 6]. Available from: https://www.kff.org/disparities-policy/issue-brief/beyond-health-care-the-role-of-social-determinants-in-promoting-health-and-health-equity/ Google Scholar
  • 3 McGovern L , Miller G , Hughes-Cromwick P . Health Policy Brief: the relative contribution of multiple determinants to health [serial on the Internet]. 2014 Aug 21 [cited 2018 Feb 6]. Available from: https://www.healthaffairs.org/do/10.1377/hpb20140821.404487/full/ Google Scholar
  • 4 Bieler G , Paroz S , Faouzi M , Trueb L , Vaucher P , Althaus F et al. Social and medical vulnerability factors of emergency department frequent users in a universal health insurance system. Acad Emerg Med. 2012;19(1):63– 8. Crossref, MedlineGoogle Scholar
  • 5 Chetty R , Stepner M , Abraham S , Lin S , Scuderi B , Turner N et al. The association between income and life expectancy in the United States, 2001–2014. JAMA. 2016;315(16):1750– 66. Crossref, MedlineGoogle Scholar
  • 6 Kind AJ , Jencks S , Brock J , Yu M , Bartels C , Ehlenbach W et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann Intern Med. 2014;161(11):765– 74. Crossref, MedlineGoogle Scholar
  • 7 Sills MR , Hall M , Colvin JD , Macy ML , Cutler GJ , Bettenhausen JL et al. Association of social determinants with children’s hospitals’ preventable readmissions performance. JAMA Pediatr. 2016;170(4):350– 8. Crossref, MedlineGoogle Scholar
  • 8 Walker RJ , Gebregziabher M , Martin-Harris B , Egede LE . Relationship between social determinants of health and processes and outcomes in adults with type 2 diabetes: validation of a conceptual framework. BMC Endocr Disord. 2014;14:82. Crossref, MedlineGoogle Scholar
  • 9 Bazemore AW , Cottrell EK , Gold R , Hughes LS , Phillips RL , Angier H et al. “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc. 2016;23(2):407– 12. Crossref, MedlineGoogle Scholar
  • 10 Gold R , Cottrell E , Bunce A , Middendorf M , Hollombe C , Cowburn S et al. Developing electronic health record (EHR) strategies related to health center patients’ social determinants of health. J Am Board Fam Med. 2017;30(4):428– 47. Crossref, MedlineGoogle Scholar
  • 11 Cerner. Population Health Management [home page on the Internet]. North Kansas City (MO): Cerner; c 2018 [cited 2018 Feb 21]. Available from: https://www.cerner.com/solutions/population-health-management Google Scholar
  • 12 Epic Software (click “Population Health”) [home page on the Internet]. Verona (WI): Epic; c 2016 [cited 2018 Feb 21]. Available from: http://www.epic.com/software#PopulationHealth Google Scholar
  • 13 Institute of Medicine. Capturing social and behavioral domains and measures in electronic health records: phase 1. Washington (DC): National Academies Press; 2014. Google Scholar
  • 14 Institute of Medicine. Capturing social and behavioral domains and measures in electronic health records: phase 2. Washington (DC): National Academies Press; 2014. Google Scholar
  • 15 National Association of Community Health Centers. What is PRAPARE? [Internet]. Bethesda (MD): NACHC; c 2018 [cited 2018 Feb 5]. Available from: http://www.nachc.org/research-and-data/prapare/ Google Scholar
  • 16 Billioux A , Verlander K , Anthony S , Alley D . Standardized screening for health-related social needs in clinical settings: the Accountable Health Communities screening tool [Internet]. Washington (DC): National Academy of Medicine; 2017 May 30 [cited 2018 Feb 5]. (Discussion Paper). Available from: https://nam.edu/wp-content/uploads/2017/05/Standardized-Screening-for-Health-Related-Social-Needs-in-Clinical-Settings.pdf Google Scholar
  • 17 Social Interventions Research and Evaluation Network. Metrics, measures, and instruments [Internet]. San Francisco (CA): SIREN; c 2017 [cited 2018 Feb 6]. Available from: https://sirenetwork.ucsf.edu/tools-resources/metrics-measures-instruments Google Scholar
  • 18 Dalton JE , Perzynski AT , Zidar DA , Rothberg MB , Coulton CJ , Milinovich AT et al. Accuracy of cardiovascular risk prediction varies by neighborhood socioeconomic position: a retrospective cohort study. Ann Intern Med. 2017;167(7):456– 64. Crossref, MedlineGoogle Scholar
  • 19 Starks MA , Schmicker RH , Peterson ED , May S , Buick JE , Kudenchuk PJ et al. Association of neighborhood demographics with out-of-hospital cardiac arrest treatment and outcomes: where you live may matter. JAMA Cardiol. 2017;2(10):1110– 8. Crossref, MedlineGoogle Scholar
  • 20 Gottlieb L , Tobey R , Cantor J , Hessler D , Adler NE . Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood). 2016;35(11):2116– 23. Go to the articleGoogle Scholar
  • 21 HealthIT.gov. Draft interoperability road map [Internet]. Washington (DC): Department of Health and Human Services; [last updated 2015 Nov 10; cited 2018 Mar 2]. Available from: https://www.healthit.gov/policy-researchers-implementers/draft-interoperability-roadmap Google Scholar
  • 22 Health IT.gov. 2018 interoperability standards advisory; I-S: social, psychological, and behavioral data [Internet]. Washington (DC): Department of Health and Human Services; 2018 [cited 2018 Mar 2]. Available from: https://www.healthit.gov/isa/sites/default/files/2018%20ISA%20Reference%20Edition.pdf Google Scholar
  • 23 Social Interventions Research and Evaluation Network. Our mission [Internet]. San Francisco (CA): SIREN; c 2017 [cited 2018 Feb 6]. Available from: https://sirenetwork.ucsf.edu/ Google Scholar
  • 24 Cantor MN , Chandras R , Pulgarin C . FACETS: using open data to measure community social determinants of health. J Am Med Inform Assoc. 2017 Oct 27. [Epub ahead of print]. Google Scholar
  • 25 Jamei M , Nisnevich A , Wetchler E , Sudat S , Liu E . Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PLoS One. 2017;12(7):e0181173. Crossref, MedlineGoogle Scholar
  • 26 Gottlieb LM , Hessler D , Long D , Laves E , Burns AR , Amaya A et al. Effects of social needs screening and in-person service navigation on child health: a randomized clinical trial. JAMA Pediatr. 2016;170(11):e162521. Crossref, MedlineGoogle Scholar
  • 27 Garg A , Toy S , Tripodis Y , Silverstein M , Freeman E . Addressing social determinants of health at well child care visits: a cluster RCT. Pediatrics. 2015;135(2):e296– 304. Crossref, MedlineGoogle Scholar
  • 28 Taveras EM , Marshall R , Sharifi M , Avalon E , Fiechtner L , Horan C et al. Comparative effectiveness of clinical-community childhood obesity interventions: a randomized clinical trial. JAMA Pediatr. 2017;171(8):e171325. Crossref, MedlineGoogle Scholar
  • 29 Losonczy LI , Hsieh D , Wang M , Hahn C , Trivedi T , Rodriguez M et al. The Highland Health Advocates: a preliminary evaluation of a novel programme addressing the social needs of emergency department patients. Emerg Med J. 2017;34(9):599– 605. Crossref, MedlineGoogle Scholar
  • 30 Berkowitz SA , Hulberg AC , Standish S , Reznor G , Atlas SJ . Addressing unmet basic resource needs as part of chronic cardiometabolic disease management. JAMA Intern Med. 2017;177(2):244– 52. Crossref, MedlineGoogle Scholar
  • 31 Healthify [home page on the Internet]. New York (NY): Healthify; c 2018 [cited 2018 Mar 2]. Available from: https://www.healthify.us/ Google Scholar
  • 32 NowPow [home page on the Internet]. Chicago (IL): NowPow; [cited 2018 Mar 2]. Available from: http://www.nowpow.com Google Scholar
  • 33 Ash AS , Mick EO , Ellis RP , Kiefe CI , Allison JJ , Clark MA . Social determinants of health in managed care payment formulas. JAMA Intern Med. 2017;177(10):1424– 30. Crossref, MedlineGoogle Scholar
  • 34 Rasanathan K , Diaz T . Research on health equity in the SDG era: the urgent need for greater focus on implementation. Int J Equity Health. 2016;15(1):202. Crossref, MedlineGoogle Scholar
  • 35 Gottlieb L , Ackerman S , Wing H , Adler N . Evaluation activities and influences at the intersection of medical and social services. J Health Care Poor Underserved. 2017;28(3):931– 51. Crossref, MedlineGoogle Scholar
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