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Development of a web-based patient decision aid for initiating disease modifying anti-rheumatic drugs using user-centred design methods

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

Mentioned by

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5 tweeters

Citations

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17 Dimensions

Readers on

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80 Mendeley
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Title
Development of a web-based patient decision aid for initiating disease modifying anti-rheumatic drugs using user-centred design methods
Published in
BMC Medical Informatics and Decision Making, April 2017
DOI 10.1186/s12911-017-0433-5
Pubmed ID
Authors

Ingrid Nota, Constance H. C. Drossaert, Heleen C. Melissant, Erik Taal, Harald E. Vonkeman, Cees J. Haagsma, Mart A. F. J. van de Laar

Abstract

A main element of patient-centred care, Patient Decision Aids (PtDAs) facilitate shared decision-making (SDM). A recent update of the International Patient Decision Aids Standards (IPDAS) emphasised patient involvement during PtDA development, but omitted a methodology for doing so. This article reports on the value of user-centred design (UCD) methods for the development of a PtDA that aims to support inflammatory arthritis patients in their choice between disease modifying anti-rheumatic drugs (DMARDs). The IPDAS development process model in combination with UCD methods were applied. The process was overseen by an eight-member multidisciplinary steering group. Patients and health professionals were iteratively consulted. Qualitative in-depth interviews combined with rapid prototyping were conducted with patients to assess their needs for specific functionality, content and design of the PtDA. Group meetings with health professionals were organized to assess patients' needs and to determine how the PtDA should be integrated into patient pathways. The current literature was reviewed to determine the clinical evidence to include in the PtDA. To evaluate usability among patients, they were observed using the PtDA while thinking aloud and then interviewed. The combination of patient interviews with rapid prototyping revealed that patients wanted to compare multiple DMARDs both for their clinical aspects and implications for daily life. Health professionals mainly wanted to refer patients to a reliable, easily adjustable source of information about DMARDs. A web-based PtDA was constructed consisting of four parts: 1) general information about SDM, inflammatory arthritis and DMARDs; 2) an application to compare particular DMARDs; 3) value clarification exercises; and 4) a printed summary of patients' notes, preferences, worries and questions that they could bring to discuss with their rheumatologist. The study demonstrated that UCD methods can be of great value for the development of PtDAs. The early, iterative involvement of patients and health professionals was helpful in developing a novel user-friendly PtDA that allowed patients to choose between DMARDs. The PtDA fits the values of all stakeholders and easily integrates with the patient pathway and daily workflow of health professionals. This collaborative designed PtDA may improve SDM and patient participation in arthritis care.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 18%
Student > Ph. D. Student 10 13%
Student > Doctoral Student 10 13%
Researcher 9 11%
Student > Bachelor 5 6%
Other 17 21%
Unknown 15 19%
Readers by discipline Count As %
Medicine and Dentistry 21 26%
Nursing and Health Professions 10 13%
Engineering 8 10%
Social Sciences 6 8%
Psychology 5 6%
Other 13 16%
Unknown 17 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 11 June 2017.
All research outputs
#7,526,794
of 22,968,808 outputs
Outputs from BMC Medical Informatics and Decision Making
#778
of 2,001 outputs
Outputs of similar age
#119,367
of 309,828 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#13
of 36 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,001 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 58% 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 309,828 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 54% of its contemporaries.
We're also able to compare this research output to 36 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 58% of its contemporaries.