Evidence map›Paper›PMID 22955496›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2013

Next-generation phenotyping of electronic health records.

George Hripcsak, David J Albers

Registry-linked trialAbstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04757233 (Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data), which is not on this map. Cited by 424 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
424citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

NCT04757233 nacompletednot on this mapstarted 2018, after this paper: background citation

Dynamically Tailoring Interventions for Problem-Solving in Diabetes Self-Management Using Self-Monitoring Data

TypeinterventionalSponsorColumbia UniversityRan2018 to 2018Enrolled20ConditionsType 2 Diabetes MellitusArmsGlucoType
3 · Its place in the literature

Who cites it

424 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  15. Optimized phenotype definitions boost GWAS power.PLoS computational biology · 2026
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364 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

George HripcsakBiomedical Informatics, Columbia University, New York, NY 10027, USA.hripcsak@columbia.edu
David J Albers

Funding

DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
NLM NIH HHS R01 LM006910
6 · The paper itself

Abstract

The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new models, learning from data, and collaboration will lead to efficient use of the valuable information currently locked in health records.

Indexed as

Diffusion of InnovationBiomedical ResearchData MiningElectronic Health RecordsHumansModels, TheoreticalQuality ControlUnited States

Identifiers

PMID22955496
PMCPMC3555337

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.