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X Demographics
Mendeley readers
Attention Score in Context
Title |
Detecting and diagnosing hotspots for the enhanced management of hospital emergency departments in Queensland, Australia
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Published in |
BMC Medical Informatics and Decision Making, December 2013
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DOI | 10.1186/1472-6947-13-132 |
Pubmed ID | |
Authors |
Sarah Bolt, Ross Sparks |
Abstract |
Predictive tools are already being implemented to assist in Emergency Department bed management by forecasting the expected total volume of patients. Yet these tools are unable to detect and diagnose when estimates fall short. Early detection of hotspots, that is subpopulations of patients presenting in unusually high numbers, would help authorities to manage limited health resources and communicate effectively about emerging risks. We evaluate an anomaly detection tool that signals when, and in what way Emergency Departments in 18 hospitals across the state of Queensland, Australia, are significantly exceeding their forecasted patient volumes. |
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 42 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 19% |
Student > Ph. D. Student | 5 | 12% |
Student > Bachelor | 4 | 10% |
Student > Doctoral Student | 3 | 7% |
Professor | 2 | 5% |
Other | 8 | 19% |
Unknown | 12 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 9 | 21% |
Engineering | 5 | 12% |
Nursing and Health Professions | 3 | 7% |
Psychology | 2 | 5% |
Social Sciences | 2 | 5% |
Other | 5 | 12% |
Unknown | 16 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 14 December 2013.
All research outputs
#15,286,644
of 22,733,113 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,308
of 1,985 outputs
Outputs of similar age
#192,380
of 306,767 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#34
of 44 outputs
Altmetric has tracked 22,733,113 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 24th percentile – i.e., 24% 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 306,767 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 44 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.