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Published on: July 27, 2018
Characterizing Real-World Data by Care Setting to Support Clinical Research
Lorena Prior Muñoz1, Lydia González Cid1,2, David Perez-Rey1
1Biomedical Informatics Group, Universidad Politécnica de Madrid, Spain.
Studies in Health Technology and Informatics
|May 23, 2026
Summary
Electronic Health Records (EHRs) provide valuable Real-World Data (RWD) for research. This study assessed EHR data quality across diverse healthcare settings, revealing variations influenced by encounter type, region, and the COVID-19 pandemic.
Area of Science:
- Health Informatics
- Data Science in Healthcare
- Clinical Research Methodology
Background:
- Electronic Health Records (EHRs) are a primary source of Real-World Data (RWD) for clinical research.
- Data completeness and quality in EHRs are often impacted by variations in encounter types and geographical locations.
Purpose of the Study:
- To characterize the coverage, density, and temporal trends of clinical data within a federated research network.
- To identify factors influencing data quality across diagnoses, procedures, laboratory tests, and medications.
Main Methods:
- Analysis of clinical data from 110 Healthcare Organizations within a federated research network.
- Calculation of data coverage and density across four key domains: diagnoses, procedures, laboratory tests, and medications.
- Examination of temporal data patterns and regional variations.
Main Results:
- Inpatient encounters demonstrated the highest data coverage and density.
- Outpatient visits showed accumulating records over time, indicating patient engagement.
- Regional disparities were observed, with higher procedure data density in the US and Latin America.
- A notable decline in outpatient and emergency data occurred around 2020 due to the COVID-19 pandemic, with slower outpatient recovery.
Conclusions:
- EHR data quality varies significantly by encounter type and region.
- Findings support robust data quality assessment and site benchmarking for multicenter research.
- Understanding data heterogeneity is crucial for effective utilization of RWD in research.
Keywords:
Data QualityElectronic Health RecordsEncounter TypesReal-World DataSite BenchmarkingTriNetXMore Related Videos
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