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Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA

Overview of attention for article published in BMC Bioinformatics, September 2012
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2 X users

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68 Mendeley
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Title
Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA
Published in
BMC Bioinformatics, September 2012
DOI 10.1186/1471-2105-13-s14-s4
Pubmed ID
Authors

Xian Yang, Rui Han, Yike Guo, Jeremy Bradley, Benita Cox, Robert Dickinson, Richard Kitney

Abstract

Hospitals nowadays have to serve numerous patients with limited medical staff and equipment while maintaining healthcare quality. Clinical pathway informatics is regarded as an efficient way to solve a series of hospital challenges. To date, conventional research lacks a mathematical model to describe clinical pathways. Existing vague descriptions cannot fully capture the complexities accurately in clinical pathways and hinders the effective management and further optimization of clinical pathways.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 67 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 18%
Researcher 11 16%
Student > Ph. D. Student 7 10%
Student > Bachelor 5 7%
Student > Postgraduate 4 6%
Other 10 15%
Unknown 19 28%
Readers by discipline Count As %
Medicine and Dentistry 11 16%
Computer Science 9 13%
Business, Management and Accounting 6 9%
Engineering 6 9%
Agricultural and Biological Sciences 4 6%
Other 11 16%
Unknown 21 31%
Attention Score in Context

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 25 February 2013.
All research outputs
#13,378,113
of 22,696,971 outputs
Outputs from BMC Bioinformatics
#4,189
of 7,254 outputs
Outputs of similar age
#92,543
of 169,023 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 96 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 169,023 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.