ArticleBioinformatics (Oxford, England)2026
Statistical methods to harmonize electronic health record data across healthcare systems: case study and lessons learned.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
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Authors and funding
26 authors.
Funding
Abstract
motivationAlthough common data models for electronic health record (EHR) data can facilitate multi-site data organization and querying, the same medical event may still be coded differently between healthcare systems. In this paper, we present statistical methods to identify and mitigate coding discrepancies using summary-level data, and demonstrate these methods using data from two FDA Sentinel data partners: Kaiser Permanente Washington and Kaiser Permanente Northwest.
resultsWe first characterize differences in coding patterns, then compute a code mapping matrix to harmonize data between systems. Our findings reveal significant heterogeneity in coded EHR data, even after adopting a common data model with the same coding system, highlighting the importance of data harmonization before downstream analyses. Our study also demonstrates the effectiveness of the data harmonization approaches, which provide a foundational data quality step to promote semantic interoperability, enhance data integration, and improve the integrity of study conclusions. AVAILABILITY AND IMPLEMENTATION: Computation prototypes, including R/Python codes and examples, are included in Section 7, available as supplementary data at Bioinformatics online and will be posted on GitHub upon publication.
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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.