ArticleJournal of the American Medical Informatics Association : JAMIA2019
Incorporating a location-based socioeconomic index into a de-identified i2b2 clinical data warehouse.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- One cutoff is not enough: Assessing different area deprivation index cutoffs for insurance types on surgical Desirability of Outcome Ranking (DOOR).Healthcare (Amsterdam, Netherlands) · 2025Article
- Area-Level Indices and Health Care Use in a Pediatric Brain and Central Nervous System Tumor Cohort: Observational Study.JMIR public health and surveillance · 2025Observational
- Predictors of new persistent opioid use after surgery in adults.Anesthesiology and perioperative science · 2025Article
- Leveraging Electronic Health Records to Assess Residential Mobility Among Veterans in the Veterans Health Administration.Medical care · 2024Article
- Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.Journal of clinical and translational science · 2024Article
- Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.Journal of clinical and translational science · 2024Article
- Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024Article
- Avenues for Strengthening PCORnet's Capacity to Advance Patient-Centered Economic Outcomes in Patient-Centered Outcomes Research (PCOR).Medical care · 2023Article
- Impact of routine urine cultures on antibiotic usage in those undergoing a routine annual spinal cord injury evaluation.Spinal cord · 2023Article
- Assessment of Value of Neighborhood Socioeconomic Status in Models That Use Electronic Health Record Data to Predict Health Care Use Rates and Mortality.JAMA network open · 2020Article
- What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions.JMIR formative research · 2020Review
- bench4gis: Benchmarking Privacy-aware Geocoding with Open Big Data.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveClinical 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
methodsPatient 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.
resultsWe 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.
conclusionThis study demonstrates a means for incorporating and querying census data in a de-identified clinical data warehouse.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.