Evidence map›Paper›PMID 24551365›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013

Combining infobuttons and semantic web rules for identifying patterns and delivering highly-personalized education materials.

Nathan C Hulse, Jie Long, Cui Tao

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Formal representation of patients' care context data: the path to improving the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2020
    Article
  3. The Use of an Adapted Health IT Usability Evaluation Model (Health-ITUEM) for Evaluating Consumer Reported Ratings of Diabetes mHealth Applications: Implications for Diabetes Care and Management.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2015
    Article
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

3 authors.

Nathan C HulseIntermountain Healthcare, Salt Lake City, UT; ; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT;
Jie LongIntermountain Healthcare, Salt Lake City, UT;
Cui TaoDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN;

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infobuttons have been established to be an effective resource for addressing information needs at the point of care, as evidenced by recent research and their inclusion in government-based electronic health record incentive programs in the United States. Yet their utility has been limited to wide success for only a specific set of domains (lab data, medication orders, and problem lists) and only for discrete, singular concepts that are already documented in the electronic medical record. In this manuscript, we present an effort to broaden their utility by connecting a semantic web-based phenotyping engine with an infobutton framework in order to identify and address broader issues in patient data, derived from multiple data sources. We have tested these patterns by defining and testing semantic definitions of pre-diabetes and metabolic syndrome. We intend to carry forward relevant information to the infobutton framework to present timely, relevant education resources to patients and providers.

Indexed as

Electronic Health RecordsUser-Computer InterfaceBiological OntologiesDecision Making, Computer-AssistedHumansMetabolic SyndromePatient Education as TopicPattern Recognition, AutomatedPrediabetic StateRisk FactorsSemantics

Identifiers

PMID24551365
PMCPMC3900206

What OpenQuestion holds

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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.