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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Quality Assurance of Teleconsultations in a Store-and-Forward Telemedicine Network – Obtaining Patient Follow-up Data and User Feedback
|
|---|---|
| Published in |
Frontiers in Public Health, November 2014
|
| DOI | 10.3389/fpubh.2014.00247 |
| Pubmed ID | |
| Authors |
Richard Wootton, Joanne Liu, Laurent Bonnardot |
| Abstract |
User surveys in telemedicine networks confirm that follow-up data are essential, both for the specialists who provide advice and for those running the system. We have examined the feasibility of a method for obtaining follow-up data automatically in a store-and-forward network. We distinguish between follow-up, which is information about the progress of a patient and is based on outcomes, and user feedback, which is more general information about the telemedicine system itself, including user satisfaction and the benefits resulting from the use of telemedicine. In the present study, we were able to obtain both kinds of information using a single questionnaire. During a 9-month pilot trial in the Médecins Sans Frontières telemedicine network, an email request for information was sent automatically by the telemedicine system to each referrer exactly 21 days after the initial submission of the case. A total of 201 requests for information were issued by the system and these elicited 41 responses from referrers (a response rate of 20%). The responses were largely positive. For example, 95% of referrers found the advice helpful, 90% said that it clarified their diagnosis, 94% said that it assisted with management of the patient, and 95% said that the telemedicine response was of educational benefit to them. Analysis of the characteristics of the referrers who did not respond, and their cases, did not suggest anything different about them in comparison with referrers who did respond. We were not able to identify obvious factors associated with a failure to respond. Obtaining data by automatic request is feasible. It provides useful information for specialists and for those running the network. Since obtaining follow-up data is essential to best practice, one proposal to improve the response rate is to simplify the automatic requests so that only patient follow-up information is asked for, and to restrict user feedback requests to the cases being assessed each month by the quality assurance panel. |
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 % |
|---|---|---|
| Switzerland | 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 4% |
| Norway | 1 | 4% |
| Unknown | 26 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 18% |
| Student > Ph. D. Student | 5 | 18% |
| Other | 3 | 11% |
| Researcher | 3 | 11% |
| Student > Doctoral Student | 1 | 4% |
| Other | 4 | 14% |
| Unknown | 7 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 32% |
| Agricultural and Biological Sciences | 4 | 14% |
| Nursing and Health Professions | 2 | 7% |
| Psychology | 1 | 4% |
| Chemical Engineering | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 9 | 32% |
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 17 December 2014.
All research outputs
#17,732,540
of 22,771,140 outputs
Outputs from Frontiers in Public Health
#4,882
of 9,791 outputs
Outputs of similar age
#248,078
of 361,950 outputs
Outputs of similar age from Frontiers in Public Health
#47
of 69 outputs
Altmetric has tracked 22,771,140 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 9,791 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. This one is in the 42nd percentile – i.e., 42% 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 361,950 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 69 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.