Incorporating a location-based socioeconomic index into a de-identified i2b2 clinical data warehouse

Abstract Objective Clinical research data warehouses are largely populated from information extracted from electronic health records (EHRs). While these data provide information about a patient’s medications, laboratory results, diagnoses, and history, her social, economic, and environmental determi...

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Bibliographic Details
Published in:	Journal of the American Medical Informatics Association : JAMIA Vol. 26; no. 4; pp. 286 - 293
Main Authors:	Gardner, Bret J, Pedersen, Jay G, Campbell, Mary E, McClay, James C
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 01-04-2019
Subjects:	Adolescent Adult Aged Aged, 80 and over Censuses Child Child, Preschool Data Anonymization Data Warehousing Electronic Health Records Emergency Service, Hospital - statistics & numerical data Female Geographic Information Systems Geographic Mapping Humans Infant Infant, Newborn Logistic Models Male Middle Aged Nebraska Research and Applications Social Class Social Determinants of Health Socioeconomic Factors United States Young Adult United States Nebraska i2b2 social determinants of health American Community Survey (ACS) socioeconomic status census
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Summary:	Abstract Objective Clinical research data warehouses are largely populated from information extracted from electronic health records (EHRs). While these data provide information about a patient’s medications, laboratory results, diagnoses, and history, her social, economic, and environmental determinants of health are also major contributing factors in readmission, morbidity, and mortality and are often absent or unstructured in the EHR. Details about a patient’s socioeconomic status may be found in the U.S. census. To facilitate researching the impacts of socioeconomic status on health outcomes, clinical and socioeconomic data must be linked in a repository in a fashion that supports seamless interrogation of these diverse data elements. This study demonstrates a method for linking clinical and location-based data and querying these data in a de-identified data warehouse using Informatics for Integrating Biology and the Bedside. Materials and Methods Patient data were extracted from the EHR at Nebraska Medicine. Socioeconomic variables originated from the 2011-2015 five-year block group estimates from the American Community Survey. Data querying was performed using Informatics for Integrating Biology and the Bedside. All location-based data were truncated to prevent identification of a location with a population <20 000 individuals. Results We successfully linked location-based and clinical data in a de-identified data warehouse and demonstrated its utility with a sample use case. Discussion With location-based data available for querying, research investigating the impact of socioeconomic context on health outcomes is possible. Efforts to improve geocoding can readily be incorporated into this model. Conclusion This study demonstrates a means for incorporating and querying census data in a de-identified clinical data warehouse.
ISSN:	1527-974X 1067-5027 1527-974X
DOI:	10.1093/jamia/ocy172