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Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases

Overview of attention for article published in BMC Medical Informatics and Decision Making, September 2016
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Title
Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases
Published in
BMC Medical Informatics and Decision Making, September 2016
DOI 10.1186/s12911-016-0346-8
Pubmed ID
Authors

Frédéric Pauer, Katharina Schmidt, Ana Babac, Kathrin Damm, Martin Frank, J.-Matthias Graf von der Schulenburg

Abstract

The Analytic Hierarchy Process (AHP) is increasingly used to measure patient priorities. Studies have shown that there are several different approaches to data acquisition and data aggregation. The aim of this study was to measure the information needs of patients having a rare disease and to analyze the effects of these different AHP approaches. The ranking of information needs is then used to display information categories on a web-based information portal about rare diseases according to the patient's priorities. The information needs of patients suffering from rare diseases were identified by an Internet research study and a preliminary qualitative study. Hence, we designed a three-level hierarchy containing 13 criteria. For data acquisition, the differences in outcomes were investigated using individual versus group judgements separately. Furthermore, we analyzed the different effects when using the median and arithmetic and geometric means for data aggregation. A consistency ratio ≤0.2 was determined to represent an acceptable consistency level. Forty individual and three group judgements were collected from patients suffering from a rare disease and their close relatives. The consistency ratio of 31 individual and three group judgements was acceptable and thus these judgements were included in the study. To a large extent, the local ranks for individual and group judgements were similar. Interestingly, group judgements were in a significantly smaller range than individual judgements. According to our data, the ranks of the criteria differed slightly according to the data aggregation method used. It is important to explain and justify the choice of an appropriate method for data acquisition because response behaviors differ according to the method. We conclude that researchers should select a suitable method based on the thematic perspective or investigated topics in the study. Because the arithmetic mean is very vulnerable to outliers, the geometric mean and the median seem to be acceptable alternatives for data aggregation. Overall, using the AHP to identify patient priorities and enhance the user-friendliness of information websites offers an important contribution to medical informatics.

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Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 14%
Researcher 7 10%
Student > Bachelor 7 10%
Student > Master 6 8%
Other 4 5%
Other 19 26%
Unknown 20 27%
Readers by discipline Count As %
Medicine and Dentistry 7 10%
Engineering 7 10%
Business, Management and Accounting 6 8%
Computer Science 5 7%
Nursing and Health Professions 5 7%
Other 19 26%
Unknown 24 33%
Attention Score in Context

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 11 September 2016.
All research outputs
#17,814,957
of 22,888,307 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,506
of 1,994 outputs
Outputs of similar age
#237,916
of 330,061 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#26
of 31 outputs
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