Bret J Gardner1, Jay G Pedersen2, Mary E Campbell3, James C McClay1. 1. Department of Emergency Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA. 2. Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska, USA. 3. Department of Sociology, Texas A&M University, College Station, Texas, USA.
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.
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.
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