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

BACKGROUND: 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 a...

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Autores principales: Pauer, Frédéric, Schmidt, Katharina, Babac, Ana, Damm, Kathrin, Frank, Martin, von der Schulenburg, J.-Matthias Graf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016921/
https://www.ncbi.nlm.nih.gov/pubmed/27613239
http://dx.doi.org/10.1186/s12911-016-0346-8
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author Pauer, Frédéric
Schmidt, Katharina
Babac, Ana
Damm, Kathrin
Frank, Martin
von der Schulenburg, J.-Matthias Graf
author_facet Pauer, Frédéric
Schmidt, Katharina
Babac, Ana
Damm, Kathrin
Frank, Martin
von der Schulenburg, J.-Matthias Graf
author_sort Pauer, Frédéric
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-016-0346-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-50169212016-09-10 Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases Pauer, Frédéric Schmidt, Katharina Babac, Ana Damm, Kathrin Frank, Martin von der Schulenburg, J.-Matthias Graf BMC Med Inform Decis Mak Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-016-0346-8) contains supplementary material, which is available to authorized users. BioMed Central 2016-09-09 /pmc/articles/PMC5016921/ /pubmed/27613239 http://dx.doi.org/10.1186/s12911-016-0346-8 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Pauer, Frédéric
Schmidt, Katharina
Babac, Ana
Damm, Kathrin
Frank, Martin
von der Schulenburg, J.-Matthias Graf
Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases
title Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases
title_full Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases
title_fullStr Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases
title_full_unstemmed Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases
title_short Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases
title_sort comparison of different approaches applied in analytic hierarchy process – an example of information needs of patients with rare diseases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016921/
https://www.ncbi.nlm.nih.gov/pubmed/27613239
http://dx.doi.org/10.1186/s12911-016-0346-8
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