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Cited by 71Electronic Health Records SystemsMedical Coding and Health InformationHealth disparities and outcomes

Incorporating Geospatial Capacity within Clinical Data Systems to Address Social Determinants of Health

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Comer, Karen Frederickson, Grannis, Shaun, Dixon, Brian E., Bodenhamer, David J., Wiehe, Sarah E.·2011·Public Health Reports®
DOI 10.1177/00333549111260s310PMC PMC3150130
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Abstract

Linking electronic health record (EHR) systems with community information systems (CIS) holds great promise for addressing inequities in social determinants of health (SDH). While EHRs are rich in location-specific data that allow us to uncover geographic inequities in health outcomes, CIS are rich in data that allow us to describe community-level characteristics relating to health. When meaningfully integrated, these data systems enable clinicians, researchers, and public health professionals to actively address the social etiologies of health disparities. This article describes a process for exploring SDH by geocoding and integrating EHR data with a comprehensive CIS covering a large metropolitan area. Because the systems were initially designed for different purposes and had different teams of experts involved in their development, integrating them presents challenges that require multidisciplinary expertise in informatics, geography, public health, and medicine. We identify these challenges and the means of addressing them and discuss the significance of the project as a model for similar projects.

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  • Existing research has explored integrating patient SDoH data with geospatial datasets like census demographics and social vulnerability index (SVI) to estimate SDoH factors at a community or neighborhood level.

    “Existing research has also explored integrating patient SDoH data with geospatial datasets like census demographics and social vulnerability index (SVI) to estimate SDoH factors at a community or neighborhood level41–44.”

    From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [41]

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Authors
Comer, K. F., Grannis, S., Dixon, B. E., Bodenhamer, D. J., & Wiehe, S. E.
Affiliations
  • Indiana University – Purdue University Indianapolis (US)
  • Regenstrief Institute (US)
ORCID
Venue
Public Health Reports®
Publisher
SAGE Publications
Published
2011-09
Volume
126
Issue
3_suppl
Pages
54-61
ISSN
  • 0033-3549 (ISSN-L)
  • 1468-2877
DOI
10.1177/00333549111260s310
PMC
PMC3150130
OpenAlex
W1592969533
Cited by
71
References
64
Citations by year
2025: 3 · 2024: 6 · 2023: 3 · 2022: 4 · 2021: 6 · 2020: 4 · 2019: 4 · 2018: 10 · 2017: 7 · 2016: 6 · 2015: 5 · 2014: 3 · 2013: 5 · 2012: 3
FWCI
6.66
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Public Health Reports (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Social Sciences › Health › Health disparities and outcomes
Keywords
social determinants of health, electronic health records, community information systems, data integration, geocoding, health disparities, health information technology
MeSH terms
Data Collection, Humans, Information Systems, Public Health, Sexually Transmitted Diseases, Socioeconomic Factors, Medical Records Systems, Computerized, Geographic Information Systems, Health Status Disparities

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