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Med Care. 2019 Jun;57 Suppl 6 Suppl 2:S157-S163. doi: 10.1097/MLR.0000000000001049.

Measuring Exposure to Incarceration Using the Electronic Health Record.

Author information

1
Department of Internal Medicine, Yale University School of Medicine, New Haven.
2
Veterans Administration Connecticut Healthcare System, West Haven, CT.
3
Department of Policy Analysis and Management, Cornell University, Ithaca, NY.
4
Pennsylvania Department of Correction, PA.
5
Center for Interdisciplinary Research on AIDS, Yale University School of Public Health, New Haven, CT.

Abstract

BACKGROUND:

Electronic health records (EHRs) are a rich source of health information; however social determinants of health, including incarceration, and how they impact health and health care disparities can be hard to extract.

OBJECTIVE:

The main objective of this study was to compare sensitivity and specificity of patient self-report with various methods of identifying incarceration exposure using the EHR.

RESEARCH DESIGN:

Validation study using multiple data sources and types.

SUBJECTS:

Participants of the Veterans Aging Cohort Study (VACS), a national observational cohort based on data from the Veterans Health Administration (VHA) EHR that includes all human immunodeficiency virus-infected patients in care (47,805) and uninfected patients (99,060) matched on region, age, race/ethnicity, and sex.

MEASURES AND DATA SOURCES:

Self-reported incarceration history compared with: (1) linked VHA EHR data to administrative data from a state Department of Correction (DOC), (2) linked VHA EHR data to administrative data on incarceration from Centers for Medicare and Medicaid Services (CMS), (3) VHA EHR-specific identifier codes indicative of receipt of VHA incarceration reentry services, and (4) natural language processing (NLP) in unstructured text in VHA EHR.

RESULTS:

Linking the EHR to DOC data: sensitivity 2.5%, specificity 100%; linking the EHR to CMS data: sensitivity 7.9%, specificity 99.3%; VHA EHR-specific identifier for receipt of reentry services: sensitivity 7.3%, specificity 98.9%; and NLP, sensitivity 63.5%, specificity 95.9%.

CONCLUSIONS:

NLP tools hold promise as a feasible and valid method to identify individuals with exposure to incarceration in EHR. Future work should expand this approach using a larger body of documents and refinement of the methods, which may further improve operating characteristics of this method.

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