You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Development of a context model to prioritize drug safety alerts in CPOE systems
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2011
|
| DOI | 10.1186/1472-6947-11-35 |
| Pubmed ID | |
| Authors |
Daniel Riedmann, Martin Jung, Werner O Hackl, Wolf Stühlinger, Heleen van der Sijs, Elske Ammenwerth |
| Abstract |
Computerized physician order entry systems (CPOE) can reduce the number of medication errors and adverse drug events (ADEs) in healthcare institutions. Unfortunately, they tend to produce a large number of partly irrelevant alerts, in turn leading to alert overload and causing alert fatigue. The objective of this work is to identify factors that can be used to prioritize and present alerts depending on the 'context' of a clinical situation. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 3% |
| United States | 3 | 3% |
| Austria | 2 | 2% |
| Canada | 1 | <1% |
| Germany | 1 | <1% |
| Spain | 1 | <1% |
| Argentina | 1 | <1% |
| Unknown | 98 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 15% |
| Student > Ph. D. Student | 13 | 12% |
| Student > Master | 13 | 12% |
| Student > Postgraduate | 11 | 10% |
| Student > Doctoral Student | 10 | 9% |
| Other | 32 | 29% |
| Unknown | 15 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 48 | 44% |
| Computer Science | 24 | 22% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 5% |
| Engineering | 5 | 5% |
| Agricultural and Biological Sciences | 4 | 4% |
| Other | 9 | 8% |
| Unknown | 15 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 03 January 2017.
All research outputs
#2,798,441
of 22,664,267 outputs
Outputs from BMC Medical Informatics and Decision Making
#214
of 1,978 outputs
Outputs of similar age
#13,790
of 112,034 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 22 outputs
Altmetric has tracked 22,664,267 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 88% 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 112,034 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 87% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.