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Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules

Background: Various studies have used multiple attribute decision making (MADM) techniques to assess and rank health technologies. The goal of the present study was to prioritize health technologies using various techniques of MADMs in combination with decision rules. Methods: The study is an applie...

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Autores principales: Mobinizadeh, Mohammadreza, Mohamadi, Efat, Arman, Hosein, Nasiripour, AmirAshkan, Olyaeemanesh, Alireza, Mohamadi, Sara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Iran University of Medical Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236113/
https://www.ncbi.nlm.nih.gov/pubmed/34211942
http://dx.doi.org/10.47176/mjiri.35.40
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author Mobinizadeh, Mohammadreza
Mohamadi, Efat
Arman, Hosein
Nasiripour, AmirAshkan
Olyaeemanesh, Alireza
Mohamadi, Sara
author_facet Mobinizadeh, Mohammadreza
Mohamadi, Efat
Arman, Hosein
Nasiripour, AmirAshkan
Olyaeemanesh, Alireza
Mohamadi, Sara
author_sort Mobinizadeh, Mohammadreza
collection PubMed
description Background: Various studies have used multiple attribute decision making (MADM) techniques to assess and rank health technologies. The goal of the present study was to prioritize health technologies using various techniques of MADMs in combination with decision rules. Methods: The study is an applied research using multi-attribute decision making (MADM) methods. This study extracted the attributes related to health technology assessment from global literature and experts’ opinions. In this study, two different types of experts were consulted: the first type, including three experts in the field of the decision-making techniques, on the subject of setting priority on health focusing on MADM; and the second one consists of seven experts in the field of HTA, asked about the selection of attributes and determination their importance. Candidate health technologies were individually weighted and ranked using TOPSIS, SAW and VIKOR by the weight and decision matrix. The results obtained from various techniques were combined and ranked using Copeland’s technique to obtain the final ranking of health technologies. To determine HTA type reports, decision rules were defined. All models were designed via MS Excel. Results: This study chose eight technologies according to six tradeoff attributes. These attributes included health benefits at the population level, vulnerable population size, availability of alternative technologies, budget impact, financial protection, and quality of evidence. Their exact weights were 0.25, 0.121, 0.146, 0.132, 0.167 and 0.181, respectively. Also, safety and uncertainty about the cost-effectiveness were considered as the veto and decision rules respectively. Copeland’s method was therefore used to combine the methods: Whereas HT2 (The technology for treating patients suffering from varicose) was ranked the highest priority and HT3 (The palliative method for patients who suffer from various cancers) was ranked the lowest (for preventing from any ethical issue, the exact name of each technology wasn’t mentioned). Conclusion: Finally, in accordance with decision rules which are based on various conditions of "uncertainty about the cost-effectiveness", it is recommended that full health technology assessment report be performed on three technologies, rapid health technology assessment report be performed on four others, and, finally no prioritizing for health technology assessment be made on one of them.
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spelling pubmed-82361132021-06-30 Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules Mobinizadeh, Mohammadreza Mohamadi, Efat Arman, Hosein Nasiripour, AmirAshkan Olyaeemanesh, Alireza Mohamadi, Sara Med J Islam Repub Iran Original Article Background: Various studies have used multiple attribute decision making (MADM) techniques to assess and rank health technologies. The goal of the present study was to prioritize health technologies using various techniques of MADMs in combination with decision rules. Methods: The study is an applied research using multi-attribute decision making (MADM) methods. This study extracted the attributes related to health technology assessment from global literature and experts’ opinions. In this study, two different types of experts were consulted: the first type, including three experts in the field of the decision-making techniques, on the subject of setting priority on health focusing on MADM; and the second one consists of seven experts in the field of HTA, asked about the selection of attributes and determination their importance. Candidate health technologies were individually weighted and ranked using TOPSIS, SAW and VIKOR by the weight and decision matrix. The results obtained from various techniques were combined and ranked using Copeland’s technique to obtain the final ranking of health technologies. To determine HTA type reports, decision rules were defined. All models were designed via MS Excel. Results: This study chose eight technologies according to six tradeoff attributes. These attributes included health benefits at the population level, vulnerable population size, availability of alternative technologies, budget impact, financial protection, and quality of evidence. Their exact weights were 0.25, 0.121, 0.146, 0.132, 0.167 and 0.181, respectively. Also, safety and uncertainty about the cost-effectiveness were considered as the veto and decision rules respectively. Copeland’s method was therefore used to combine the methods: Whereas HT2 (The technology for treating patients suffering from varicose) was ranked the highest priority and HT3 (The palliative method for patients who suffer from various cancers) was ranked the lowest (for preventing from any ethical issue, the exact name of each technology wasn’t mentioned). Conclusion: Finally, in accordance with decision rules which are based on various conditions of "uncertainty about the cost-effectiveness", it is recommended that full health technology assessment report be performed on three technologies, rapid health technology assessment report be performed on four others, and, finally no prioritizing for health technology assessment be made on one of them. Iran University of Medical Sciences 2021-03-27 /pmc/articles/PMC8236113/ /pubmed/34211942 http://dx.doi.org/10.47176/mjiri.35.40 Text en © 2021 Iran University of Medical Sciences https://creativecommons.org/licenses/by-nc-sa/1.0/This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial-ShareAlike 1.0 License (CC BY-NC-SA 1.0), which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.
spellingShingle Original Article
Mobinizadeh, Mohammadreza
Mohamadi, Efat
Arman, Hosein
Nasiripour, AmirAshkan
Olyaeemanesh, Alireza
Mohamadi, Sara
Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules
title Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules
title_full Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules
title_fullStr Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules
title_full_unstemmed Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules
title_short Topic selection for health technology assessment: An approach combining multiple attribute decision making and decision rules
title_sort topic selection for health technology assessment: an approach combining multiple attribute decision making and decision rules
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236113/
https://www.ncbi.nlm.nih.gov/pubmed/34211942
http://dx.doi.org/10.47176/mjiri.35.40
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