Locating relevant patient information in electronic health record data using representations of clinical concepts and database structures.

Xuequn Pan, James J Cimino

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Xuequn PanLister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD ; Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD.

James J CiminoLister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD ; Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD.

Funding

Using large Clinical Data bases to assess the association between patient factors, medication usage and patient outcomesZIALM008927 · NLM · NATIONAL LIBRARY OF MEDICINE · PI MCDONALD, CLEMENT · 2009 to 2015

$1.5M

Intramural NIH HHS

6 · The paper itself

Abstract

Clinicians and clinical researchers often seek information in electronic health records (EHRs) that are relevant to some concept of interest, such as a disease or finding. The heterogeneous nature of EHRs can complicate retrieval, risking incomplete results. We frame this problem as the presence of two gaps: 1) a gap between clinical concepts and their representations in EHR data and 2) a gap between data representations and their locations within EHR data structures. We bridge these gaps with a knowledge structure that comprises relationships among clinical concepts (including concepts of interest and concepts that may be instantiated in EHR data) and relationships between clinical concepts and the database structures. We make use of available knowledge resources to develop a reproducible, scalable process for creating a knowledge base that can support automated query expansion from a clinical concept to all relevant EHR data.

Indexed as

Biological OntologiesDatabases as TopicElectronic Health RecordsSystematized Nomenclature of MedicineHumansInformation Storage and RetrievalKnowledge BasesLyme Disease

Identifiers

PMID25954405

PMCPMC4419946

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.