Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives Journal of the American Medical Informatics Association Advance Access published July 13, 2015
“Community Vital Signs”: Incorporating geocoded social determinants into electronic records to promote patient and population health
RECEIVED 6 January 2015 REVISED 6 May 2015 ACCEPTED 26 May 2015
Andrew W Bazemore1, Erika K Cottrell2,3, Rachel Gold2,4, Lauren S Hughes5, Robert L Phillips6, Heather Angier3, Timothy E Burdick3,7, Mark A Carrozza8, Jennifer E DeVoe2,3
ABSTRACT .................................................................................................................................................... Social determinants of health significantly impact morbidity and mortality; however, physicians lack ready access to this information in patient care and population management. Just as traditional vital signs give providers a biometric assessment of any patient, “community vital signs” (Community VS) can provide an aggregated overview of the social and environmental factors impacting patient health. Knowing Community VS could inform clinical recommendations for individual patients, facilitate referrals to community services, and expand understanding of factors impacting treatment adherence and health outcomes. This information could also help care teams target disease prevention initiatives and other health improvement efforts for clinic panels and populations. Given the proliferation of big data, geospatial technologies, and democratization of data, the time has come to integrate Community VS into the electronic health record (EHR). Here, the authors describe (i) historical precedent for this concept, (ii) opportunities to expand upon these historical foundations, and (iii) a novel approach to EHR integration.
.................................................................................................................................................... Keywords: social determinants of health, electronic health records, socioeconomic factors, residence characteristics
INTRODUCTION
Pioneers in Community-oriented Care In the 1940s, Sidney and Emily Kark pioneered the collection and integration of community data into the delivery of effective primary care, calling it Community Oriented Primary Care (COPC).17,18 The Karks’ conducted demographic and epidemiologic assessments to define and characterize the South African communities they served. In a pre-EHR era, these pioneers integrated community and patient data to understand population risk, prioritize health problems, and develop interventions tailored to their particular community context.19 These principles took root in countries around the world.20 Notably, after spending time with the Karks, H. Jack Geiger brought COPC methods to the United States in the 1960s, partnering with epidemiologist John Hatch to create the nation’s first two neighborhood health centers.21 By understanding the communities in which patients live, and addressing a broad spectrum of SDH, Geiger sought to create healthcare systems that improved the health of the community, broadening the focus beyond traditional medical care. The Tufts-Delta Health Center, the first US federally qualified health center (FQHC), not only provided health services, but also surveyed and assessed local needs, conducted outreach and health education, and implemented interventions to address community issues such as housing, water supply, sanitation.19 The legacy of Geiger’s early centers is reflected in the nation’s 8000þ FQHCs, which are governed by communitybased boards and provide services according to local need.21 Advancing the idea of providing context-informed care within a practice, Farley and Froom22 organized patient charts by family and
Correspondence to Heather Angier, Department of Family Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; [email protected]; 503-349-6362 C The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. V All rights reserved. For Permissions, please email: [email protected] For numbered affiliations see end of article.
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Social determinants of health (SDH)—the milieu of social, economic, occupational, and environmental factors—influence morbidity and mortality more than traditional medical care.1–13 However, healthcare providers rarely have tools to incorporate information about patients’ SDH into healthcare decision-making. Providers begin each patient encounter with “vital signs”—biometric markers essential to clinical assessment. It is time for providers to also have meaningful information on “community vital signs” (Community VS) at the point of care to inform panel management efforts. Community VS, defined as one or aggregated measures of SDH, are constructed from community-level geocoded data from publicly available sources, such as the US Census Bureau. The Institute of Medicine’s (IOM) recent call to enhance meaningful use of electronic health records (EHRs) envisions patient report as the principal means of adding SDH data to EHRs.14,15 Though this approach is useful, as population data availability increases, so does capacity to augment EHR records with community-level data through geospatial integration. Community VS can now be imported into EHRs to enable healthcare providers to offer context-informed care—care that meaningfully accounts for neighborhood factors that affect patients’ health.16 We describe historical precedents for integrating data in clinical settings to facilitate context-informed and communityoriented care, contemporary opportunities to expand on these foundations, and our specific efforts to build and test integration of Community VS into EHRs.
Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives
neighborhood as a way to better understand the community factors that influence patient health. Curtis Hames used aerial photography and frequent assessments and observations of the community surrounding his clinic to inform individual patient care, while contributing to scientific understanding of community influences on heart disease.23,24 In the 1960s and 1970s, Larry Weed and Jan Schultz introduced the Problem-Oriented Medical Information System, one of the first point-ofcare EHRs. Dr Weed advocated for including SDH, such as patient-reported financial and housing stressors, in the problem list.25 He believed that all data, including SDH, could be acquired, stored in the EHR as structured data, and used to support clinical decision-making.
Table 1. Summary of IOM Recommended Social and Behavioral Domains for Inclusion in all EHRs Domains Individual-level (patient-reported) Race/ethnicitya Education Financial resource strain Stress
PERSPECTIVES
Building on Historical Foundations: EHRs, Geospatial Technology, and Accessible Data Present Big Opportunities The pioneers described above could only dream of today’s opportunities for collecting and organizing data. We now have the unique ability to build on this foundation by using community-level data and geospatial technologies to incorporate Community VS into EHRs. Most healthcare providers now utilize EHRs, which offer tremendous potential to aggregate, analyze, and integrate individual- and community-level data across settings and over time.16 Though EHRs provide crucial information to providers treating individual patients, they currently do not capture and integrate the wealth of publicly available population health data on SDH. Community-level SDH data has been used to develop public health and policy interventions,26,27 but providers at the point of care lack both access and education regarding the potential use of SDH in clinical decision-making and management.28 Until Community VS are encoded in the EHR using a structured terminology (e.g., Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT)); geocoded community-level SDH data will remain isolated from existing clinical workflows. National initiatives are taking first steps toward incorporating SDH data into EHRs. Most notably, the IOM’s Committee on Recommended Social and Behavioral Domains and Measures for EHRs convened to “identify [SDH] domains [to inform] recommendations for stage three Meaningful Use of EHRs.”14,15 This committee’s 2014 report, “Capturing Social and Behavioral Domains in EHRs: Phase 2,” presents a set of 11 candidate SDH data domains recommended for inclusion in all EHRs (Table 1).15 These domains (i.e., data elements) were identified based on evidence of their association with health; the potential usefulness of information about that domain in treating patients, developing interventions or policies, and conducting research; and the availability of valid measures. Most of the IOM’s candidate domains address individual-level SDH which must be collected directly from each patient then entered into the EHR by clinic staff or patients (e.g., through the patient portal); only 1 of the candidate domains would utilize geocoded communitylevel data.15 Of the 10 individual-level patient-reported domains, 4 are already recommended for collection in most EHRs as structured patient-level data. However, few healthcare settings are currently equipped with workflows, staff resources, and technological tools to systematically collect and record data about the remaining individuallevel SDH domains. With limited EHR functionality supporting collection and use of these data, and Meaningful Use Stage 3 requirements delayed until 2017, it could take years to operationalize these domains and develop care systems’ capacity to integrate the collection of individual-level SDH into the EHR. While enabling clinics’ collection of diverse individual-level patientreported data may take time, the capacity to integrate communitylevel geocoded data exists now. An increasing wealth of public data at the small area level (e.g., census tract or city block) on median income
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Depressiona Physical activity Tobacco use and exposurea Alcohol usea Social connections and social isolation Exposure to violence: intimate partner violence Community-level (geocodable) Neighborhood and community compositional characteristics (residential addressa; census tract-median income)
Source: IOM. Recommended Social and Behavioral Domains and Measures for EHRs: Phase 2. a Denotes domains which are already recommended for collection as structured data in the EHR at the patient level for various purposes, including Meaningful Use. (recommended by the IOM) and many other potentially relevant community-level factors are available now and could be used to populate discrete fields in the EHR.29 Other countries routinely use community-level data to inform and strategically resource clinics.30,31 These deprivation indices have been modeled in the United States using existing data sources and tested against disease prevalence and outcomes at the Primary Care Service Area level.32 We have an opportunity to bring these data, or derived indices, into EHRs as structured content. The SNOMED-CT terminology (a standardized, clinical terminology used by physicians and other healthcare providers for the electronic exchange of clinical health information) already includes a hierarchy for social context, including ethnicity, occupation, and economic status at the person-level. Moreover, SNOMED-CT has a hierarchy for environment and geographic locations which could be expanded to include concepts not already mapped.33 Advances in geospatial technology and access to contextual information in the form of publicly available large datasets make it possible to automate processes for embedding Community VS into every patient’s chart. With georeferenced and geocodable information readily available, and technology that enables integrating these data into the EHR, all healthcare professionals could see a patient knowing not just her blood pressure, pulse, respiratory rate, and temperature, but also whether she lives in the presence of poverty, healthy food and water sources, walkable streets and parks, and has social capital—or how these add up to predict increased risk of morbidity, early mortality, or other adverse health outcomes. Our Approach: Incorporating Community-Level Data into EHRs Through the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network (CDRN)34 (funded by the Patient-Centered Outcomes Research
Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives
Table 2: Indicators selected for ADVANCE Pilot by Community VS Type Community VS
Indicators
Data Source
Built environment
Fast food restaurants per 100 000 population; liquor stores per 100 000 population; population density
American Community Survey US Census Bureau, county business patterns US Census Bureau, ZIP code business patterns
Median housing structure age; number of person-days with maximum 8-h average ozone concentration over the National Ambient Air Quality Standard (monitored and modeled data); number of person-days with PM2.5 over the National Ambient Air Quality Standard (monitored and modeled data); percent of occupied housing units without complete plumbing facilities; percent of population potentially exposed to water exceeding a violation limit during the past year
American Community Survey
Dependency ratio (old-age); estimated percent of foreclosure starts over the past 18 months through June 2008; estimated percent of vacant addresses in June 2008 (90-day vacancy rate); Gini coefficient– inequality; overall percentile ranking for the CDC Social Vulnerability Index
Agency for Toxic Substances and Disease Registry
Neighborhood race/ethnic composition
Count and percent by race; residential segregation (dissimilarity and exposure)
American Community Survey
Neighborhood resources
Low access tract at 1 mile and at 1=2 mile for urban areas or 10 miles for rural areas; metro/non-metro classification codes; Modified Retail Food Environment Index (no. of healthy food stores divided by all food stores); percent of people in a county living more than 1 mile from a supermarket or large grocery store if in an urban area, or more than 10 miles if in a rural area; percentage of population living within 1=2 mile of a park; recreation facilities per 100 000 population; Urban Classification Code—rural, urban cluster (>10 000 population, 50 000 population)
Center for Disease Control and Prevention, Environmental Public Health Tracking Network
Neighborhood socioeconomic composition
Number with Bachelor’s Degree or higher; median household income; number and percent of persons in managerial, professional, or executive occupations; percent below 100% of Federal Poverty Level (FPL); percent below 200% of FPL; unemployment rate
American Community Survey
Social Deprivation Index
A composite measure of social deprivation validated to be more strongly associated with poor access to healthcare and poor health outcomes than a measure of poverty alone.
Robert Graham Center32
Environmental exposures
Neighborhood economic conditions
Environmental Protection Agency, Safe Drinking Water Information System
American Community Survey Department of Housing and Urban Development, Neighborhood Stabilization Program
US Census Bureau, county business patterns US Census Bureau, ZIP code business patterns USDA Food Access Research Atlas USDA, Economic Research Service
PERSPECTIVES
Institute) the Robert Graham Center (RGC), OCHIN (originally called the Oregon Community Health Information Network, renamed OCHIN as other states joined), and HealthLandscape are partnering with FQHCs to integrate geocoded information from neighborhood geospatial maps into every patient’s EHR. This work builds on similar efforts that used indices derived from US census data linked to a given patient’s home address, such as the Neighborhood SocioEconomic Status index,35–40 the Neighborhood Deprivation Index,41–44 and the Social Deprivation Index.32 HealthLandscape45 and the RGC46 have systematically acquired social, behavioral, economic, and health data from multiple national, state, and local sources for almost a decade. HealthLandscape’s comprehensive data library includes nearly 10 000 national, regional, county, and small area measures ranging from health economics, healthcare workforce, population estimates, education, vital statistics, criminal justice, migration, healthcare quality indicators, demographics, poverty, social and physical environment, mental health, and substance abuse and prevention.47
Center for Disease Control and Prevention (CDC), Environmental Public Health Tracking Network
We drew upon recommendations from the IOM14,15 and ADVANCE community stakeholders to select a pilot set of Community VS potentially useful to healthcare teams. Elements selected are described in Table 2. Elements that were considered but not selected include: alternate measures of residential segregation, occupational dissimilarity, and access to public transportation (the latter was omitted due to the lack of a single comprehensive data source). Using HealthLandscape’s geospatial technology and extensive data library, we created a Community VS Geocoding Application Programming Interface (API) designed to accept bulk or single address data requests, assign detailed geographic identifiers, and append Community VS to the EHR. The API performs two broad tasks. First, using data from the originating system (in this case, the ADVANCE data network) geographic identifiers (e.g., county, census tract) are assigned to each address using latitude and longitude coordinates interpolated from street address and zip code. Second, using the assigned geographic identifiers, a core set of Community VS are joined to each address record. Next,
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Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives
the API returns the geographic identifiers and Community VS to the originating system. The result is a set of Community VS for any patient with a valid address, which can be made available to care team members at the point of care from within the EHR. The Community VS Geocoding API transports data requests and results through a secure, platform-independent system to geospatial data partner HealthLandscape, operating under a Business Associate Agreement to maintain Health Insurance Portability and Accountability Act compliance. Transfers are conducted with full data encryption and strict authentication to ensure that only authorized entities have access to the API, and that only the originating system receives the geocoding and data results. No identifiers other than address and a unique, anonymous identifier are transmitted to the API. The API produces a geocoding quality score, which provides an indication of the quality of street-level interpolation, and can be used to determine whether the quality of the appended community characteristics is sufficiently rigorous for the data to be included in the ADVANCE system, helping to ensure that clinicians are not working with poor quality Community VS data.
PERSPECTIVES
Next Steps for Incorporating Community VS into the EHR The API is developed and is being deployed in a testing environment. However, incorporating these data into the EHR is only a first step. For Community VS to indeed be vital and worthy of inclusion in EHRs, further development and integration is essential. To that end, we are engaging stakeholders in decisions regarding how to aggregate these community-level data elements into one or more Community VS and how to provide them to clinicians within the EHR. In addition, research is needed to expand our knowledge of (i) which community data elements or combination of elements best predict health outcomes, (ii) how providers and patients adapt varying definitions of “community” to the available geospatial boundaries, (iii) how best to make this information available and useful in clinical settings, and (iv) which interventions providers can employ in response to Community VS. Beyond simply displaying a set of community vitals in the EHR, similar to how traditional vital signs currently appear in most EHRs, Community VS could also be incorporated into clinical decision support (CDS) and Population Management tools. Researchers should partner with clinicians, clinical teams, and EHR developers to identify best practices for how these data should be displayed in the EHR, which member of the healthcare team should act on the data, and when in the clinical workflow the data will be most useful. EHR developers should also engage in efforts to link directories of social and community services into EHR systems and CDS tools so that care teams have information about the resources available to patients in a given community. In addition to CDS tools at the point of care, similar tools could be built for population health and panel management. For example, the EHR could be programmed to identify all patients on a provider’s panel who live in an area with a high proportion of fast food restaurants and send them information about where they can purchase fresh produce along with recipes for quick, healthy meals. Using data from the Environmental Protection Agency’s Safe Drinking Water Information System, one of the data sources selected for ADVANCE Community VS, care teams could be automatically notified of water contamination and equip patients in the affected area with information about how to create safe drinking water (e.g., boiling instructions) or where to find alternative water sources. Another CDS tool could alert a care team member during an office visit that a patient may benefit from depression screening based on a high rate of unemployment or other community predictor in the patient’s neighborhood.
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The lack of current evidence for potential Community VS should not delay development, rigorous testing, and implementation of these concepts and associated CDS tools. There is an urgent need to expand upon early efforts that link increasingly available and communitysourced spatial data into patient records48 and to investigate their effective use via practice-based research, pragmatic trials, and comparative effectiveness research studies. As this work evolves, it is likely that various definitions of community will need to be considered, using the most granular data available (e.g., census tract, block group) as building blocks. Traditional and geopolitical boundaries—including legislative boundaries, census tracts or other Census Bureau geographies, ZIP codes or even a city or county—are regularly used to define community. However, there are alternative interpretations of community and community identity that are not encompassed by these boundaries. Future efforts should include further validation and refinement of geographic constructs through measured, ground-level interactions of providers and patients, and others facilitating practice change (e.g., practice facilitators, primary care extension program agents).49 Research must also assess changes in providers’ knowledge, attitudes, and skills related to SDH; patients’ perceptions on the utility of this information in the clinical encounter; and the health outcomes associated with integrating Community VS into the EHR. Finally, the IOM’s report highlights the need to integrate additional individual-level, patient-reported SDH data to improve granularity and impact. While Community VS can be added to EHRs now, they should be developed in such a way that they can accommodate and integrate additional patient-reported data over time. This could be accomplished by adding Community VS into the Office of the National Coordinator for Health Information Technology Standards and Interoperability (S&I) Framework.50 Thus, allowing researchers to access Community VS from multiple EHR systems using distributed query methods as proposed in the S&I Data Access Framework.51 Furthermore, standardized, EHR-embedded Community VS should be incorporated into the S&I Clinical Quality Framework, an emerging national standard for clinical quality measures (CQMs), to allow benchmarking of population-level CQMs related to Community VS.52
CONCLUSION Given the impact social, demographic, and physical factors have on health and recent increases in access to big data and geospatial technologies, the time has arrived to integrate community-level SDH data at the point of care. Incorporating Community VS into every patient’s EHR will give patients and healthcare providers information that better enables context-informed and community-oriented care. This paper provides a roadmap for integrating geocoded community-level data into patient-level EHRs being piloted in a national network of FQHCs. Future steps include developing CDS tools to integrate Community VS into the clinical workflow, conducting practice-based and comparative effectiveness research to understand how care teams use and act on Community VS, and assessing the impact of Community VS and related CDS tools on the ability to provide contextinformed care.
COMPETING INTERESTS The authors have no competing interests to report.
FUNDING This work was financially supported by the Patient-Centered Outcomes Research Institute grant number CDRN-1306–04716.
Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives
CONTRIBUTORS A.B. helped conceive of the work, wrote the first draft, and gave final approval of the manuscript. E.C. contributed substantial edits, and gave final approval of the manuscript. R.G. helped conceive of the work, contributed substantial edits, and gave final approval of the manuscript. L.H. contributed substantial edits, and gave final approval of the manuscript. R.P. contributed substantial edits, and gave final approval of the manuscript. H.A. contributed substantial edits, and gave final approval of the manuscript. T.B. contributed substantial edits, and gave final approval of the manuscript. M.C. provided the specifications for the indicators, contributed substantial edits, and gave final approval of the manuscript. J.E.DeV. helped conceive of the work, secured the funding, contributed substantial edits, and gave final approval of the manuscript.
ACKNOWLEDGEMENTS We would like to acknowledge the federally qualified health center providers and patients for their time and insight that have shaped this work and the efforts of everyone working on the Accelerating Data Value Across a National Community Health Center Network project.
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AUTHOR AFFILIATIONS .................................................................................................................................................... 1
Robert Graham Center for Policy Studies in Primary Care, Washington, DC, USA
PERSPECTIVES
2
OCHIN, Inc., Portland, OR, USA
3
Department of Family Medicine, Oregon Health & Science University, Portland, OR, USA 4
Kaiser Permanente NW, Center for Health Research, Portland, OR, USA
5
R Robert Wood Johnson Foundation Clinical Scholars ProgramV , Department of Family Medicine, University of Michigan, Ann Arbor, MI, USA
6
6
American Board of Family Medicine, Washington, DC, USA
7 Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
8
HealthLandscape, American Academy of Family Physicians, Cincinnati, OH, USA
“Community Vital Signs”: Incorporating geocoded social determinants into electronic records to promote patient and population health
RECEIVED 6 January 2015 REVISED 6 May 2015 ACCEPTED 26 May 2015
Andrew W Bazemore1, Erika K Cottrell2,3, Rachel Gold2,4, Lauren S Hughes5, Robert L Phillips6, Heather Angier3, Timothy E Burdick3,7, Mark A Carrozza8, Jennifer E DeVoe2,3
ABSTRACT .................................................................................................................................................... Social determinants of health significantly impact morbidity and mortality; however, physicians lack ready access to this information in patient care and population management. Just as traditional vital signs give providers a biometric assessment of any patient, “community vital signs” (Community VS) can provide an aggregated overview of the social and environmental factors impacting patient health. Knowing Community VS could inform clinical recommendations for individual patients, facilitate referrals to community services, and expand understanding of factors impacting treatment adherence and health outcomes. This information could also help care teams target disease prevention initiatives and other health improvement efforts for clinic panels and populations. Given the proliferation of big data, geospatial technologies, and democratization of data, the time has come to integrate Community VS into the electronic health record (EHR). Here, the authors describe (i) historical precedent for this concept, (ii) opportunities to expand upon these historical foundations, and (iii) a novel approach to EHR integration.
.................................................................................................................................................... Keywords: social determinants of health, electronic health records, socioeconomic factors, residence characteristics
INTRODUCTION
Pioneers in Community-oriented Care In the 1940s, Sidney and Emily Kark pioneered the collection and integration of community data into the delivery of effective primary care, calling it Community Oriented Primary Care (COPC).17,18 The Karks’ conducted demographic and epidemiologic assessments to define and characterize the South African communities they served. In a pre-EHR era, these pioneers integrated community and patient data to understand population risk, prioritize health problems, and develop interventions tailored to their particular community context.19 These principles took root in countries around the world.20 Notably, after spending time with the Karks, H. Jack Geiger brought COPC methods to the United States in the 1960s, partnering with epidemiologist John Hatch to create the nation’s first two neighborhood health centers.21 By understanding the communities in which patients live, and addressing a broad spectrum of SDH, Geiger sought to create healthcare systems that improved the health of the community, broadening the focus beyond traditional medical care. The Tufts-Delta Health Center, the first US federally qualified health center (FQHC), not only provided health services, but also surveyed and assessed local needs, conducted outreach and health education, and implemented interventions to address community issues such as housing, water supply, sanitation.19 The legacy of Geiger’s early centers is reflected in the nation’s 8000þ FQHCs, which are governed by communitybased boards and provide services according to local need.21 Advancing the idea of providing context-informed care within a practice, Farley and Froom22 organized patient charts by family and
Correspondence to Heather Angier, Department of Family Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA; [email protected]; 503-349-6362 C The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. V All rights reserved. For Permissions, please email: [email protected] For numbered affiliations see end of article.
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PERSPECTIVES
Social determinants of health (SDH)—the milieu of social, economic, occupational, and environmental factors—influence morbidity and mortality more than traditional medical care.1–13 However, healthcare providers rarely have tools to incorporate information about patients’ SDH into healthcare decision-making. Providers begin each patient encounter with “vital signs”—biometric markers essential to clinical assessment. It is time for providers to also have meaningful information on “community vital signs” (Community VS) at the point of care to inform panel management efforts. Community VS, defined as one or aggregated measures of SDH, are constructed from community-level geocoded data from publicly available sources, such as the US Census Bureau. The Institute of Medicine’s (IOM) recent call to enhance meaningful use of electronic health records (EHRs) envisions patient report as the principal means of adding SDH data to EHRs.14,15 Though this approach is useful, as population data availability increases, so does capacity to augment EHR records with community-level data through geospatial integration. Community VS can now be imported into EHRs to enable healthcare providers to offer context-informed care—care that meaningfully accounts for neighborhood factors that affect patients’ health.16 We describe historical precedents for integrating data in clinical settings to facilitate context-informed and communityoriented care, contemporary opportunities to expand on these foundations, and our specific efforts to build and test integration of Community VS into EHRs.
Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives
neighborhood as a way to better understand the community factors that influence patient health. Curtis Hames used aerial photography and frequent assessments and observations of the community surrounding his clinic to inform individual patient care, while contributing to scientific understanding of community influences on heart disease.23,24 In the 1960s and 1970s, Larry Weed and Jan Schultz introduced the Problem-Oriented Medical Information System, one of the first point-ofcare EHRs. Dr Weed advocated for including SDH, such as patient-reported financial and housing stressors, in the problem list.25 He believed that all data, including SDH, could be acquired, stored in the EHR as structured data, and used to support clinical decision-making.
Table 1. Summary of IOM Recommended Social and Behavioral Domains for Inclusion in all EHRs Domains Individual-level (patient-reported) Race/ethnicitya Education Financial resource strain Stress
PERSPECTIVES
Building on Historical Foundations: EHRs, Geospatial Technology, and Accessible Data Present Big Opportunities The pioneers described above could only dream of today’s opportunities for collecting and organizing data. We now have the unique ability to build on this foundation by using community-level data and geospatial technologies to incorporate Community VS into EHRs. Most healthcare providers now utilize EHRs, which offer tremendous potential to aggregate, analyze, and integrate individual- and community-level data across settings and over time.16 Though EHRs provide crucial information to providers treating individual patients, they currently do not capture and integrate the wealth of publicly available population health data on SDH. Community-level SDH data has been used to develop public health and policy interventions,26,27 but providers at the point of care lack both access and education regarding the potential use of SDH in clinical decision-making and management.28 Until Community VS are encoded in the EHR using a structured terminology (e.g., Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT)); geocoded community-level SDH data will remain isolated from existing clinical workflows. National initiatives are taking first steps toward incorporating SDH data into EHRs. Most notably, the IOM’s Committee on Recommended Social and Behavioral Domains and Measures for EHRs convened to “identify [SDH] domains [to inform] recommendations for stage three Meaningful Use of EHRs.”14,15 This committee’s 2014 report, “Capturing Social and Behavioral Domains in EHRs: Phase 2,” presents a set of 11 candidate SDH data domains recommended for inclusion in all EHRs (Table 1).15 These domains (i.e., data elements) were identified based on evidence of their association with health; the potential usefulness of information about that domain in treating patients, developing interventions or policies, and conducting research; and the availability of valid measures. Most of the IOM’s candidate domains address individual-level SDH which must be collected directly from each patient then entered into the EHR by clinic staff or patients (e.g., through the patient portal); only 1 of the candidate domains would utilize geocoded communitylevel data.15 Of the 10 individual-level patient-reported domains, 4 are already recommended for collection in most EHRs as structured patient-level data. However, few healthcare settings are currently equipped with workflows, staff resources, and technological tools to systematically collect and record data about the remaining individuallevel SDH domains. With limited EHR functionality supporting collection and use of these data, and Meaningful Use Stage 3 requirements delayed until 2017, it could take years to operationalize these domains and develop care systems’ capacity to integrate the collection of individual-level SDH into the EHR. While enabling clinics’ collection of diverse individual-level patientreported data may take time, the capacity to integrate communitylevel geocoded data exists now. An increasing wealth of public data at the small area level (e.g., census tract or city block) on median income
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Depressiona Physical activity Tobacco use and exposurea Alcohol usea Social connections and social isolation Exposure to violence: intimate partner violence Community-level (geocodable) Neighborhood and community compositional characteristics (residential addressa; census tract-median income)
Source: IOM. Recommended Social and Behavioral Domains and Measures for EHRs: Phase 2. a Denotes domains which are already recommended for collection as structured data in the EHR at the patient level for various purposes, including Meaningful Use. (recommended by the IOM) and many other potentially relevant community-level factors are available now and could be used to populate discrete fields in the EHR.29 Other countries routinely use community-level data to inform and strategically resource clinics.30,31 These deprivation indices have been modeled in the United States using existing data sources and tested against disease prevalence and outcomes at the Primary Care Service Area level.32 We have an opportunity to bring these data, or derived indices, into EHRs as structured content. The SNOMED-CT terminology (a standardized, clinical terminology used by physicians and other healthcare providers for the electronic exchange of clinical health information) already includes a hierarchy for social context, including ethnicity, occupation, and economic status at the person-level. Moreover, SNOMED-CT has a hierarchy for environment and geographic locations which could be expanded to include concepts not already mapped.33 Advances in geospatial technology and access to contextual information in the form of publicly available large datasets make it possible to automate processes for embedding Community VS into every patient’s chart. With georeferenced and geocodable information readily available, and technology that enables integrating these data into the EHR, all healthcare professionals could see a patient knowing not just her blood pressure, pulse, respiratory rate, and temperature, but also whether she lives in the presence of poverty, healthy food and water sources, walkable streets and parks, and has social capital—or how these add up to predict increased risk of morbidity, early mortality, or other adverse health outcomes. Our Approach: Incorporating Community-Level Data into EHRs Through the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network (CDRN)34 (funded by the Patient-Centered Outcomes Research
Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives
Table 2: Indicators selected for ADVANCE Pilot by Community VS Type Community VS
Indicators
Data Source
Built environment
Fast food restaurants per 100 000 population; liquor stores per 100 000 population; population density
American Community Survey US Census Bureau, county business patterns US Census Bureau, ZIP code business patterns
Median housing structure age; number of person-days with maximum 8-h average ozone concentration over the National Ambient Air Quality Standard (monitored and modeled data); number of person-days with PM2.5 over the National Ambient Air Quality Standard (monitored and modeled data); percent of occupied housing units without complete plumbing facilities; percent of population potentially exposed to water exceeding a violation limit during the past year
American Community Survey
Dependency ratio (old-age); estimated percent of foreclosure starts over the past 18 months through June 2008; estimated percent of vacant addresses in June 2008 (90-day vacancy rate); Gini coefficient– inequality; overall percentile ranking for the CDC Social Vulnerability Index
Agency for Toxic Substances and Disease Registry
Neighborhood race/ethnic composition
Count and percent by race; residential segregation (dissimilarity and exposure)
American Community Survey
Neighborhood resources
Low access tract at 1 mile and at 1=2 mile for urban areas or 10 miles for rural areas; metro/non-metro classification codes; Modified Retail Food Environment Index (no. of healthy food stores divided by all food stores); percent of people in a county living more than 1 mile from a supermarket or large grocery store if in an urban area, or more than 10 miles if in a rural area; percentage of population living within 1=2 mile of a park; recreation facilities per 100 000 population; Urban Classification Code—rural, urban cluster (>10 000 population, 50 000 population)
Center for Disease Control and Prevention, Environmental Public Health Tracking Network
Neighborhood socioeconomic composition
Number with Bachelor’s Degree or higher; median household income; number and percent of persons in managerial, professional, or executive occupations; percent below 100% of Federal Poverty Level (FPL); percent below 200% of FPL; unemployment rate
American Community Survey
Social Deprivation Index
A composite measure of social deprivation validated to be more strongly associated with poor access to healthcare and poor health outcomes than a measure of poverty alone.
Robert Graham Center32
Environmental exposures
Neighborhood economic conditions
Environmental Protection Agency, Safe Drinking Water Information System
American Community Survey Department of Housing and Urban Development, Neighborhood Stabilization Program
US Census Bureau, county business patterns US Census Bureau, ZIP code business patterns USDA Food Access Research Atlas USDA, Economic Research Service
PERSPECTIVES
Institute) the Robert Graham Center (RGC), OCHIN (originally called the Oregon Community Health Information Network, renamed OCHIN as other states joined), and HealthLandscape are partnering with FQHCs to integrate geocoded information from neighborhood geospatial maps into every patient’s EHR. This work builds on similar efforts that used indices derived from US census data linked to a given patient’s home address, such as the Neighborhood SocioEconomic Status index,35–40 the Neighborhood Deprivation Index,41–44 and the Social Deprivation Index.32 HealthLandscape45 and the RGC46 have systematically acquired social, behavioral, economic, and health data from multiple national, state, and local sources for almost a decade. HealthLandscape’s comprehensive data library includes nearly 10 000 national, regional, county, and small area measures ranging from health economics, healthcare workforce, population estimates, education, vital statistics, criminal justice, migration, healthcare quality indicators, demographics, poverty, social and physical environment, mental health, and substance abuse and prevention.47
Center for Disease Control and Prevention (CDC), Environmental Public Health Tracking Network
We drew upon recommendations from the IOM14,15 and ADVANCE community stakeholders to select a pilot set of Community VS potentially useful to healthcare teams. Elements selected are described in Table 2. Elements that were considered but not selected include: alternate measures of residential segregation, occupational dissimilarity, and access to public transportation (the latter was omitted due to the lack of a single comprehensive data source). Using HealthLandscape’s geospatial technology and extensive data library, we created a Community VS Geocoding Application Programming Interface (API) designed to accept bulk or single address data requests, assign detailed geographic identifiers, and append Community VS to the EHR. The API performs two broad tasks. First, using data from the originating system (in this case, the ADVANCE data network) geographic identifiers (e.g., county, census tract) are assigned to each address using latitude and longitude coordinates interpolated from street address and zip code. Second, using the assigned geographic identifiers, a core set of Community VS are joined to each address record. Next,
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the API returns the geographic identifiers and Community VS to the originating system. The result is a set of Community VS for any patient with a valid address, which can be made available to care team members at the point of care from within the EHR. The Community VS Geocoding API transports data requests and results through a secure, platform-independent system to geospatial data partner HealthLandscape, operating under a Business Associate Agreement to maintain Health Insurance Portability and Accountability Act compliance. Transfers are conducted with full data encryption and strict authentication to ensure that only authorized entities have access to the API, and that only the originating system receives the geocoding and data results. No identifiers other than address and a unique, anonymous identifier are transmitted to the API. The API produces a geocoding quality score, which provides an indication of the quality of street-level interpolation, and can be used to determine whether the quality of the appended community characteristics is sufficiently rigorous for the data to be included in the ADVANCE system, helping to ensure that clinicians are not working with poor quality Community VS data.
PERSPECTIVES
Next Steps for Incorporating Community VS into the EHR The API is developed and is being deployed in a testing environment. However, incorporating these data into the EHR is only a first step. For Community VS to indeed be vital and worthy of inclusion in EHRs, further development and integration is essential. To that end, we are engaging stakeholders in decisions regarding how to aggregate these community-level data elements into one or more Community VS and how to provide them to clinicians within the EHR. In addition, research is needed to expand our knowledge of (i) which community data elements or combination of elements best predict health outcomes, (ii) how providers and patients adapt varying definitions of “community” to the available geospatial boundaries, (iii) how best to make this information available and useful in clinical settings, and (iv) which interventions providers can employ in response to Community VS. Beyond simply displaying a set of community vitals in the EHR, similar to how traditional vital signs currently appear in most EHRs, Community VS could also be incorporated into clinical decision support (CDS) and Population Management tools. Researchers should partner with clinicians, clinical teams, and EHR developers to identify best practices for how these data should be displayed in the EHR, which member of the healthcare team should act on the data, and when in the clinical workflow the data will be most useful. EHR developers should also engage in efforts to link directories of social and community services into EHR systems and CDS tools so that care teams have information about the resources available to patients in a given community. In addition to CDS tools at the point of care, similar tools could be built for population health and panel management. For example, the EHR could be programmed to identify all patients on a provider’s panel who live in an area with a high proportion of fast food restaurants and send them information about where they can purchase fresh produce along with recipes for quick, healthy meals. Using data from the Environmental Protection Agency’s Safe Drinking Water Information System, one of the data sources selected for ADVANCE Community VS, care teams could be automatically notified of water contamination and equip patients in the affected area with information about how to create safe drinking water (e.g., boiling instructions) or where to find alternative water sources. Another CDS tool could alert a care team member during an office visit that a patient may benefit from depression screening based on a high rate of unemployment or other community predictor in the patient’s neighborhood.
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The lack of current evidence for potential Community VS should not delay development, rigorous testing, and implementation of these concepts and associated CDS tools. There is an urgent need to expand upon early efforts that link increasingly available and communitysourced spatial data into patient records48 and to investigate their effective use via practice-based research, pragmatic trials, and comparative effectiveness research studies. As this work evolves, it is likely that various definitions of community will need to be considered, using the most granular data available (e.g., census tract, block group) as building blocks. Traditional and geopolitical boundaries—including legislative boundaries, census tracts or other Census Bureau geographies, ZIP codes or even a city or county—are regularly used to define community. However, there are alternative interpretations of community and community identity that are not encompassed by these boundaries. Future efforts should include further validation and refinement of geographic constructs through measured, ground-level interactions of providers and patients, and others facilitating practice change (e.g., practice facilitators, primary care extension program agents).49 Research must also assess changes in providers’ knowledge, attitudes, and skills related to SDH; patients’ perceptions on the utility of this information in the clinical encounter; and the health outcomes associated with integrating Community VS into the EHR. Finally, the IOM’s report highlights the need to integrate additional individual-level, patient-reported SDH data to improve granularity and impact. While Community VS can be added to EHRs now, they should be developed in such a way that they can accommodate and integrate additional patient-reported data over time. This could be accomplished by adding Community VS into the Office of the National Coordinator for Health Information Technology Standards and Interoperability (S&I) Framework.50 Thus, allowing researchers to access Community VS from multiple EHR systems using distributed query methods as proposed in the S&I Data Access Framework.51 Furthermore, standardized, EHR-embedded Community VS should be incorporated into the S&I Clinical Quality Framework, an emerging national standard for clinical quality measures (CQMs), to allow benchmarking of population-level CQMs related to Community VS.52
CONCLUSION Given the impact social, demographic, and physical factors have on health and recent increases in access to big data and geospatial technologies, the time has arrived to integrate community-level SDH data at the point of care. Incorporating Community VS into every patient’s EHR will give patients and healthcare providers information that better enables context-informed and community-oriented care. This paper provides a roadmap for integrating geocoded community-level data into patient-level EHRs being piloted in a national network of FQHCs. Future steps include developing CDS tools to integrate Community VS into the clinical workflow, conducting practice-based and comparative effectiveness research to understand how care teams use and act on Community VS, and assessing the impact of Community VS and related CDS tools on the ability to provide contextinformed care.
COMPETING INTERESTS The authors have no competing interests to report.
FUNDING This work was financially supported by the Patient-Centered Outcomes Research Institute grant number CDRN-1306–04716.
Bazemore A, et al. J Am Med Inform Assoc 2015;0:1–6. doi:10.1093/jamia/ocv088, Perspectives
CONTRIBUTORS A.B. helped conceive of the work, wrote the first draft, and gave final approval of the manuscript. E.C. contributed substantial edits, and gave final approval of the manuscript. R.G. helped conceive of the work, contributed substantial edits, and gave final approval of the manuscript. L.H. contributed substantial edits, and gave final approval of the manuscript. R.P. contributed substantial edits, and gave final approval of the manuscript. H.A. contributed substantial edits, and gave final approval of the manuscript. T.B. contributed substantial edits, and gave final approval of the manuscript. M.C. provided the specifications for the indicators, contributed substantial edits, and gave final approval of the manuscript. J.E.DeV. helped conceive of the work, secured the funding, contributed substantial edits, and gave final approval of the manuscript.
ACKNOWLEDGEMENTS We would like to acknowledge the federally qualified health center providers and patients for their time and insight that have shaped this work and the efforts of everyone working on the Accelerating Data Value Across a National Community Health Center Network project.
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AUTHOR AFFILIATIONS .................................................................................................................................................... 1
Robert Graham Center for Policy Studies in Primary Care, Washington, DC, USA
PERSPECTIVES
2
OCHIN, Inc., Portland, OR, USA
3
Department of Family Medicine, Oregon Health & Science University, Portland, OR, USA 4
Kaiser Permanente NW, Center for Health Research, Portland, OR, USA
5
R Robert Wood Johnson Foundation Clinical Scholars ProgramV , Department of Family Medicine, University of Michigan, Ann Arbor, MI, USA
6
6
American Board of Family Medicine, Washington, DC, USA
7 Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
8
HealthLandscape, American Academy of Family Physicians, Cincinnati, OH, USA
