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Using the Analytic Hierarchy Process (AHP) to understand the most important factors to design and evaluate a telehealth system for Parkinson's disease

Overview of attention for article published in BMC Medical Informatics and Decision Making, September 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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1 policy source
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4 X users

Citations

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28 Dimensions

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139 Mendeley
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Title
Using the Analytic Hierarchy Process (AHP) to understand the most important factors to design and evaluate a telehealth system for Parkinson's disease
Published in
BMC Medical Informatics and Decision Making, September 2015
DOI 10.1186/1472-6947-15-s3-s7
Pubmed ID
Authors

Jorge Cancela, Giuseppe Fico, Maria T Arredondo Waldmeyer

Abstract

The assessment of a new health technology is a multidisciplinary and multidimensional process, which requires a complex analysis and the convergence of different stakeholders into a common decision. This task is even more delicate when the assessment is carried out in early stage of development processes, when the maturity of the technology prevents conducting a large scale trials to evaluate the cost effectiveness through classic health economics methods. This lack of information may limit the future development and deployment in the clinical practice. This work aims to 1) identify the most relevant user needs of a new medical technology for managing and monitoring Parkinson's Disease (PD) patients and to 2) use these user needs for a preliminary assessment of a specific system called PERFORM, as a case study. Analytic Hierarchy Process (AHP) was used to design a hierarchy of 17 needs, grouped into 5 categories. A total of 16 experts, 6 of them with a clinical background and the remaining 10 with a technical background, were asked to rank these needs and categories. On/Off fluctuations detection, Increase wearability acceptance, and Increase self-management support have been identified as the most relevant user needs. No significant differences were found between the clinician and technical groups. These results have been used to evaluate the PERFORM system and to identify future areas of improvement. First of all, the AHP contributed to the elaboration of a unified hierarchy, integrating the needs of a variety of stakeholders, promoting the discussion and the agreement into a common framework of evaluation. Moreover, the AHP effectively supported the user need elicitation as well as the assignment of different weights and priorities to each need and, consequently, it helped to define a framework for the assessment of telehealth systems for PD management and monitoring. This framework can be used to support the decision-making process for the adoption of new technologies in PD.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Italy 1 <1%
Australia 1 <1%
Slovenia 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 133 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 15%
Researcher 15 11%
Other 14 10%
Student > Master 13 9%
Student > Bachelor 12 9%
Other 26 19%
Unknown 38 27%
Readers by discipline Count As %
Engineering 19 14%
Computer Science 13 9%
Medicine and Dentistry 12 9%
Nursing and Health Professions 11 8%
Psychology 8 6%
Other 32 23%
Unknown 44 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 23 May 2022.
All research outputs
#4,652,570
of 22,828,180 outputs
Outputs from BMC Medical Informatics and Decision Making
#422
of 1,988 outputs
Outputs of similar age
#60,176
of 267,781 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#8
of 37 outputs
Altmetric has tracked 22,828,180 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,988 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 78% 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 267,781 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.