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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Does the implementation of an electronic prescribing system create unintended medication errors? A study of the sociotechnical context through the analysis of reported medication incidents
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, May 2011
|
| DOI | 10.1186/1472-6947-11-29 |
| Pubmed ID | |
| Authors |
Sabi Redwood, Anna Rajakumar, James Hodson, Jamie J Coleman |
| Abstract |
Even though electronic prescribing systems are widely advocated as one of the most effective means of improving patient safety, they may also introduce new risks that are not immediately obvious. Through the study of specific incidents related to the processes involved in the administration of medication, we sought to find out if the prescribing system had unintended consequences in creating new errors. The focus of this study was a large acute hospital in the Midlands in the United Kingdom, which implemented a Prescribing, Information and Communication System (PICS). |
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 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| United Kingdom | 1 | <1% |
| Germany | 1 | <1% |
| Australia | 1 | <1% |
| Unknown | 145 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 32 | 21% |
| Student > Master | 29 | 19% |
| Researcher | 12 | 8% |
| Student > Postgraduate | 12 | 8% |
| Other | 10 | 7% |
| Other | 35 | 23% |
| Unknown | 21 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 43 | 28% |
| Computer Science | 20 | 13% |
| Nursing and Health Professions | 19 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 9 | 6% |
| Business, Management and Accounting | 8 | 5% |
| Other | 24 | 16% |
| Unknown | 28 | 19% |
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 07 August 2013.
All research outputs
#17,670,096
of 22,684,168 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,494
of 1,979 outputs
Outputs of similar age
#93,601
of 109,702 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#16
of 22 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,979 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 109,702 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
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 is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.