Evidence map›Paper›PMID 26392849›Full record

ArticleOnline journal of public health informatics2015

Building an Ontology for Identity Resolution in Healthcare and Public Health.

Jeffrey Duncan, Karen Eilbeck, Scott P Narus, Stephen Clyde, Sidney Thornton, Catherine Staes

Abstract read
In one paragraph

Article in Online journal of public health informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 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.

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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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

6 authors.

Jeffrey DuncanDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT USA.
Karen EilbeckDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT USA.
Scott P NarusDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT USA ; Intermountain Healthcare, Salt Lake City, UT USA.
Stephen ClydeDepartment of Computer Science, Utah State University, Logan, UT USA.
Sidney ThorntonDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT USA ; Intermountain Healthcare, Salt Lake City, UT USA.
Catherine StaesDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledIntegration of disparate information from electronic health records, clinical data warehouses, birth certificate registries and other public health information systems offers great potential for clinical care, public health practice, and research. Such integration, however, depends on correctly matching patient-specific records using demographic identifiers. Without standards for these identifiers, record linkage is complicated by issues of structural and semantic heterogeneity.

objectivesOur objectives were to develop and validate an ontology to: 1) identify components of identity and events subsequent to birth that result in creation, change, or sharing of identity information; 2) develop an ontology to facilitate data integration from multiple healthcare and public health sources; and 3) validate the ontology's ability to model identity-changing events over time.

methodsWe interviewed domain experts in area hospitals and public health programs and developed process models describing the creation and transmission of identity information among various organizations for activities subsequent to a birth event. We searched for existing relevant ontologies. We validated the content of our ontology with simulated identity information conforming to scenarios identified in our process models.

resultsWe chose the Simple Event Model (SEM) to describe events in early childhood and integrated the Clinical Element Model (CEM) for demographic information. We demonstrated the ability of the combined SEM-CEM ontology to model identity events over time.

conclusionThe use of an ontology can overcome issues of semantic and syntactic heterogeneity to facilitate record linkage.

Indexed as

integrated child health information systemsmedical record linkageontology

Identifiers

PMID26392849
PMCPMC4576444

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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.