ArticlePloS one2026
Investigation into EHR data coverage in the All of Us Research Program via linkage to health insurance claims.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Cross-biobank comparison of ASCVD heritability and genetic correlation.Journal of human genetics · 2026Article
- Association of coronary artery bypass with cognitive impairment in coronary artery disease across APO (ε) genotypes in AllofUS.medRxiv : the preprint server for health sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In 2020, the All of Us (AoU) Research Program Data Completeness Task Force identified several challenges in the state of program health data, including the high likelihood that Electronic Health Record (EHR) data from recruitment institutions is missing a significant portion of care received by participants. To improve data availability, the AoU Data Completeness Task Force recommended efforts to work with industry partners to better understand the degree of missingness within AoU EHR data, with an initial focus on claims data. In this study we describe efforts from AoU's collaboration with Swoop, a commercial analytics company holding health insurance claims data for over 80% of Americans, to assess the degree to which health insurance claims data can fill out the complete picture of care received by AoU participants. Using record linkage to link between individual participant AoU EHR data and their respective insurance claims, we quantitatively assess the amount of missing health data in both Swoop claims data and AoU EHR data over a decade long sample, identifying trends in data missingness based on participant characteristics. Our analysis demonstrates that AoU would greatly benefit from ingestion of claims data, gaining an estimated 16 million (90 per person) unique diagnosis codes, 17.8 million (99 per person) unique procedure codes, and 9.4 million (53 per person) unique drug codes through linkage to claims data.
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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.