Bazemore A, et al. J Am Med Inform Assoc 2016;23:407–412. doi:10.1093/jamia/ocv088, Perspective
“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
PUBLISHED ONLINE FIRST 13 July 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,
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. All rights reserved. For Permissions, please
V
email: [email protected] For numbered affiliations see end of article.
407
PERSPECTIVE
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.
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
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 poin-
Bazemore A, et al. J Am Med Inform Assoc 2016;23:407–412. doi:10.1093/jamia/ocv088, Perspective
t-of-care EHRs. Dr Weed advocated for including SDH, such as patientreported 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
PERSPECTIVE
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
(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
408
Individual-level (patient-reported)
Race/ethnicitya
Education
Financial resource strain
Stress
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.
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
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
Bazemore A, et al. J Am Med Inform Assoc 2016;23:407–412. doi:10.1093/jamia/ocv088, Perspective
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), urban area (>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
PERSPECTIVE
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
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
Center for Disease Control and Prevention (CDC), Environmental
Public Health Tracking Network
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,
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
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Bazemore A, et al. J Am Med Inform Assoc 2016;23:407–412. doi:10.1093/jamia/ocv088, Perspective
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.
PERSPECTIVE
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.
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
410
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.
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
Bazemore A, et al. J Am Med Inform Assoc 2016;23:407–412. doi:10.1093/jamia/ocv088, Perspective
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
6
American Board of Family Medicine, Washington, DC, USA
7
2
Department of Medical Informatics and Clinical Epidemiology, Oregon Health &
Science University, Portland, OR, USA
3
Department of Family Medicine, Oregon Health & Science University, Portland,
OR, USA
8
HealthLandscape, American Academy of Family Physicians, Cincinnati, OH,
USA
OCHIN, Inc., 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
PERSPECTIVE
412