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Mendeley readers
Attention Score in Context
| Title |
Semantic validation of the use of SNOMED CT in HL7 clinical documents
|
|---|---|
| Published in |
Journal of Biomedical Semantics, July 2011
|
| DOI | 10.1186/2041-1480-2-2 |
| Pubmed ID | |
| Authors |
Stijn Heymans, Matthew McKennirey, Joshua Phillips |
| Abstract |
The HL7 Clinical Document Architecture (CDA) constrains the HL7 Reference Information model (RIM) to specify the format of HL7-compliant clinical documents, dubbed CDA documents. The use of clinical terminologies such as SNOMED CT® further improves interoperability as they provide a shared understanding of concepts used in clinical documents. However, despite the use of the RIM and of shared terminologies such as SNOMED CT®, gaps remain as to how to use both the RIM and SNOMED CT® in HL7 clinical documents. The HL7 implementation guide on Using SNOMED CT in HL7 Version 3 is an effort to close this gap. It is, however, a human-readable document that is not suited for automatic processing. As such, health care professionals designing clinical documents need to ensure validity of documents manually. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 3% |
| Netherlands | 2 | 3% |
| United Kingdom | 2 | 3% |
| United States | 2 | 3% |
| Belgium | 2 | 3% |
| Australia | 1 | 1% |
| Portugal | 1 | 1% |
| Switzerland | 1 | 1% |
| Unknown | 62 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 20% |
| Student > Master | 12 | 16% |
| Researcher | 11 | 15% |
| Student > Bachelor | 6 | 8% |
| Other | 5 | 7% |
| Other | 18 | 24% |
| Unknown | 8 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 26 | 35% |
| Medicine and Dentistry | 23 | 31% |
| Agricultural and Biological Sciences | 5 | 7% |
| Psychology | 2 | 3% |
| Engineering | 2 | 3% |
| Other | 6 | 8% |
| Unknown | 11 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 23 February 2012.
All research outputs
#16,722,190
of 25,374,917 outputs
Outputs from Journal of Biomedical Semantics
#234
of 368 outputs
Outputs of similar age
#91,913
of 128,787 outputs
Outputs of similar age from Journal of Biomedical Semantics
#5
of 6 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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 128,787 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.