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Assessing the Quality of Teleconsultations in a Store-And-Forward Telemedicine Network – Long-Term Monitoring Taking into Account Differences between Cases

Overview of attention for article published in Frontiers in Public Health, October 2014
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Title
Assessing the Quality of Teleconsultations in a Store-And-Forward Telemedicine Network – Long-Term Monitoring Taking into Account Differences between Cases
Published in
Frontiers in Public Health, October 2014
DOI 10.3389/fpubh.2014.00211
Pubmed ID
Authors

Richard Wootton, Joanne Liu, Laurent Bonnardot

Abstract

We have previously proposed a method for assessing the quality of individual teleconsultation cases; this paper proposes an additional step to allow the long-term monitoring of quality. The basic scenario is a teleconsultation system (aka an e-referral system or a tele-expertise system) where the referrer posts a question about a clinical case, the question is relayed to an appropriate expert, and the chosen expert provides an answer. The people running this system want assurances that it is stable, i.e., they want routine quality assurance information about the "output" from the "process." This requires two things. It needs a method of assessing the quality of individual patient consultations. And it needs a method for taking into account differences between patients, so that these quality assessments can be compared longitudinally. Building on the previously proposed methodology, the present paper proposes two techniques for measuring the difficulty posed by a particular teleconsultation. The first is an indirect method, similar to a willingness to pay economic estimation. The second is a direct method. Using these two methods with real data from a telemedicine network showed that the first method was feasible, but did not produce useful results in a pilot trial. The second method, while more laborious, was also feasible and did produce useful results. Thus, when output quality is measured, an allowance can be made for the characteristics of the case submitted. This means that fluctuations in output quality can be attributed to variations in the process (network) or to variations in the raw materials (queries submitted to the network). Long-term quality assurance should assist those providing telemedicine services in low-resource settings to ensure that the services are operated effectively and efficiently, despite the constraints and complexities of the environment.

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The data shown below were collected from the profiles of 3 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 26%
Student > Bachelor 3 11%
Student > Postgraduate 3 11%
Student > Doctoral Student 1 4%
Professor 1 4%
Other 4 15%
Unknown 8 30%
Readers by discipline Count As %
Medicine and Dentistry 7 26%
Business, Management and Accounting 3 11%
Chemical Engineering 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Agricultural and Biological Sciences 1 4%
Other 5 19%
Unknown 9 33%
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 18 November 2014.
All research outputs
#13,415,092
of 22,768,097 outputs
Outputs from Frontiers in Public Health
#3,055
of 9,790 outputs
Outputs of similar age
#124,500
of 260,390 outputs
Outputs of similar age from Frontiers in Public Health
#39
of 81 outputs
Altmetric has tracked 22,768,097 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 9,790 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one has gotten more attention than average, scoring higher than 67% 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 260,390 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 81 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.