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Applying the Analytic Hierarchy Process in healthcare research: A systematic literature review and evaluation of reporting

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2015
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

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2 tweeters

Citations

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64 Dimensions

Readers on

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181 Mendeley
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Title
Applying the Analytic Hierarchy Process in healthcare research: A systematic literature review and evaluation of reporting
Published in
BMC Medical Informatics and Decision Making, December 2015
DOI 10.1186/s12911-015-0234-7
Pubmed ID
Authors

Katharina Schmidt, Ines Aumann, Ines Hollander, Kathrin Damm, J.-Matthias Graf von der Schulenburg

Abstract

The Analytic Hierarchy Process (AHP), developed by Saaty in the late 1970s, is one of the methods for multi-criteria decision making. The AHP disaggregates a complex decision problem into different hierarchical levels. The weight for each criterion and alternative are judged in pairwise comparisons and priorities are calculated by the Eigenvector method. The slowly increasing application of the AHP was the motivation for this study to explore the current state of its methodology in the healthcare context. A systematic literature review was conducted by searching the Pubmed and Web of Science databases for articles with the following keywords in their titles or abstracts: "Analytic Hierarchy Process," "Analytical Hierarchy Process," "multi-criteria decision analysis," "multiple criteria decision," "stated preference," and "pairwise comparison." In addition, we developed reporting criteria to indicate whether the authors reported important aspects and evaluated the resulting studies' reporting. The systematic review resulted in 121 articles. The number of studies applying AHP has increased since 2005. Most studies were from Asia (almost 30 %), followed by the US (25.6 %). On average, the studies used 19.64 criteria throughout their hierarchical levels. Furthermore, we restricted a detailed analysis to those articles published within the last 5 years (n = 69). The mean of participants in these studies were 109, whereas we identified major differences in how the surveys were conducted. The evaluation of reporting showed that the mean of reported elements was about 6.75 out of 10. Thus, 12 out of 69 studies reported less than half of the criteria. The AHP has been applied inconsistently in healthcare research. A minority of studies described all the relevant aspects. Thus, the statements in this review may be biased, as they are restricted to the information available in the papers. Hence, further research is required to discover who should be interviewed and how, how inconsistent answers should be dealt with, and how the outcome and stability of the results should be presented. In addition, we need new insights to determine which target group can best handle the challenges of the AHP.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 181 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 <1%
Spain 1 <1%
Unknown 179 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 36 20%
Student > Bachelor 29 16%
Student > Ph. D. Student 28 15%
Student > Doctoral Student 13 7%
Other 11 6%
Other 41 23%
Unknown 23 13%
Readers by discipline Count As %
Medicine and Dentistry 27 15%
Engineering 26 14%
Business, Management and Accounting 20 11%
Computer Science 17 9%
Nursing and Health Professions 8 4%
Other 47 26%
Unknown 36 20%

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 04 January 2016.
All research outputs
#2,678,298
of 6,905,088 outputs
Outputs from BMC Medical Informatics and Decision Making
#424
of 888 outputs
Outputs of similar age
#110,247
of 301,208 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#16
of 37 outputs
Altmetric has tracked 6,905,088 research outputs across all sources so far. This one has received more attention than most of these and is in the 59th percentile.
So far Altmetric has tracked 888 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 47th percentile – i.e., 47% 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 301,208 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 61% 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 gotten more attention than average, scoring higher than 51% of its contemporaries.