ReviewCurrent opinion in lipidology2020
Using the electronic health record for genomics research.
Review in Current opinion in lipidology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Sharing EHR data of patients with rare diseases for research - The role of quality assessments in a national federated research data infrastructure.BMC medical informatics and decision making · 2026Article
- Evaluating Privacy-Preserving Record Linkage in Merging Cancer Registry Data and Electronic Health Records.Journal of registry management · 2025Article
- Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology.Database : the journal of biological databases and curation · 2024Article
- Prediction and risk stratification from hospital discharge records based on Hierarchical sLDA.BMC medical informatics and decision making · 2022Article
- Leveraging the Electronic Health Record to Address the COVID-19 Pandemic.Mayo Clinic proceedings · 2021Review
- An Implementation Science Framework to Develop a Clinical Decision Support Tool for Familial Hypercholesterolemia.Journal of personalized medicine · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
purpose of reviewAlthough primarily designed for medical documentation and billing purposes, the electronic health record (EHR) has significant potential for translational research. In this article, we provide an overview of the use of the EHR for genomics research with a focus on heritable lipid disorders. RECENT
findingsLinking the EHR to genomic data enables repurposing of vast phenotype data for genomic discovery. EHR data can be used to study the genetic basis of common and rare disorders, identify subphenotypes of diseases, assess pathogenicity of novel genomic variants, investigate pleiotropy, and rapidly assemble cohorts for genomic medicine clinical trials. EHR-based discovery can inform clinical practice; examples include use of polygenic risk scores for assessing disease risk and use of phenotype data to interpret rare variants. Despite limitations such as missing data, variable use of standards and poor interoperablility between disparate systems, the EHR is a powerful resource for genomic research. SUMMARY: When linked to genomic data, the EHR can be leveraged for genomic discovery, which in turn can inform clinical care, exemplifying the virtuous cycle of a learning healthcare system.
Indexed as
Identifiers
What OpenQuestion holds
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.