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Ontoserver: a syndicated terminology server

Overview of attention for article published in Journal of Biomedical Semantics, September 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#33 of 366)
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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Title
Ontoserver: a syndicated terminology server
Published in
Journal of Biomedical Semantics, September 2018
DOI 10.1186/s13326-018-0191-z
Pubmed ID
Authors

Alejandro Metke-Jimenez, Jim Steel, David Hansen, Michael Lawley

Abstract

Even though several high-quality clinical terminologies, such as SNOMED CT and LOINC, are readily available, uptake in clinical systems has been slow and many continue to capture information in plain text or using custom terminologies. This paper discusses some of the challenges behind this slow uptake and describes a clinical terminology server implementation that aims to overcome these obstacles and contribute to the widespread adoption of standardised clinical terminologies. Ontoserver is a clinical terminology server based on the Fast Health Interoperability Resources (FHIR) standard. Some of its key features include: out-of-the-box support for SNOMED CT, LOINC and OWL ontologies, such as the Human Phenotype Ontology (HPO); a fast, prefix-based search algorithm to ensure users can easily find content and are not discouraged from entering coded data; a syndication mechanism to facilitate keeping terminologies up to date; and a full implementation of SNOMED CT's Expression Constraint Language (ECL), which enables sophisticated data analytics. Ontoserver has been designed to overcome some of the challenges that have hindered adoption of standardised clinical terminologies and is used in several organisations throughout Australia. Increasing adoption is an important goal because it will help improve the quality of clinical data, which can lead to better clinical decision support and ultimately to better patient outcomes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 8 10%
Student > Ph. D. Student 8 10%
Student > Master 8 10%
Researcher 7 9%
Student > Bachelor 6 8%
Other 22 28%
Unknown 20 25%
Readers by discipline Count As %
Computer Science 13 16%
Medicine and Dentistry 12 15%
Unspecified 8 10%
Nursing and Health Professions 4 5%
Biochemistry, Genetics and Molecular Biology 4 5%
Other 14 18%
Unknown 24 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2018.
All research outputs
#2,306,177
of 23,103,436 outputs
Outputs from Journal of Biomedical Semantics
#33
of 366 outputs
Outputs of similar age
#50,580
of 341,518 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 5 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 366 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 341,518 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them