scholarly journals HT2 APPLYING MULTI CRITERIA DECISION ANALYSIS TO GUIDE HEALTHCARE DECISION MAKING IN A DEVELOPING COUNTRY

2020 ◽  
Vol 23 ◽  
pp. S6
Author(s):  
S. Aderian ◽  
S. Nasser ◽  
H. Khoury ◽  
D. Samaha
2017 ◽  
Vol 34 (1) ◽  
pp. 105-110 ◽  
Author(s):  
Kevin Marsh ◽  
J. Jaime Caro ◽  
Erica Zaiser ◽  
James Heywood ◽  
Alaa Hamed

Objectives: Patient preferences should be a central consideration in healthcare decision making. However, stories of patients challenging regulatory and reimbursement decisions has led to questions on whether patient voices are being considered sufficiently during those decision making processes. This has led some to argue that it is necessary to quantify patient preferences before they can be adequately considered.Methods: This study considers the lessons from the use of multi-criteria decision analysis (MCDA) for efforts to quantify patient preferences. It defines MCDA and summarizes the benefits it can provide to decision makers, identifies examples of MCDAs that have involved patients, and summarizes good practice guidelines as they relate to quantifying patient preferences.Results: The guidance developed to support the use of MCDA in healthcare provide some useful considerations for the quantification of patient preferences, namely that researchers should give appropriate consideration to: the heterogeneity of patient preferences, and its relevance to decision makers; the cognitive challenges posed by different elicitation methods; and validity of the results they produce. Furthermore, it is important to consider how the relevance of these considerations varies with the decision being supported.Conclusions: The MCDA literature holds important lessons for how patient preferences should be quantified to support healthcare decision making.


2016 ◽  
Vol 19 (7) ◽  
pp. A489-A490 ◽  
Author(s):  
A Gilabert-Perramon ◽  
C Lens ◽  
JI Betolaza ◽  
JC March ◽  
J Espín ◽  
...  

Author(s):  
Paul Hansen ◽  
Nancy Devlin

Multi-criteria decision analysis (MCDA) is increasingly used to support healthcare decision-making. MCDA involves decision makers evaluating the alternatives under consideration based on the explicit weighting of criteria relevant to the overarching decision—in order to, depending on the application, rank (or prioritize) or choose between the alternatives. A prominent example of MCDA applied to healthcare decision-making that has received a lot of attention in recent years and is the main subject of this article is choosing which health “technologies” (i.e., drugs, devices, procedures, etc.) to fund—a process known as health technology assessment (HTA). Other applications include prioritizing patients for surgery, prioritizing diseases for R&D, and decision-making about licensing treatments. Most applications are based on weighted-sum models. Such models involve explicitly weighting the criteria and rating the alternatives on the criteria, with each alternative’s “performance” on the criteria aggregated using a linear (i.e., additive) equation to produce the alternative’s “total score,” by which the alternatives are ranked. The steps involved in a MCDA process are explained, including an overview of methods for scoring alternatives on the criteria and weighting the criteria. The steps are: structuring the decision problem being addressed, specifying criteria, measuring alternatives’ performance, scoring alternatives on the criteria and weighting the criteria, applying the scores and weights to rank the alternatives, and presenting the MCDA results, including sensitivity analysis, to decision makers to support their decision-making. Arguments recently advanced against using MCDA for HTA and counterarguments are also considered. Finally, five questions associated with how MCDA for HTA is operationalized are discussed: Whose preferences are relevant for MCDA? Should criteria and weights be decision-specific or identical for repeated applications? How should cost or cost-effectiveness be included in MCDA? How can the opportunity cost of decisions be captured in MCDA? How can uncertainty be incorporated into MCDA?


Axioms ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 124
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Predrag S. Stanimirović ◽  
Florentin Smarandache ◽  
...  

Some decision-making problems, i.e., multi-criteria decision analysis (MCDA) problems, require taking into account the attitudes of a large number of decision-makers and/or respondents. Therefore, an approach to the transformation of crisp ratings, collected from respondents, in grey interval numbers form based on the median of collected scores, i.e., ratings, is considered in this article. In this way, the simplicity of collecting respondents’ attitudes using crisp values, i.e., by applying some form of Likert scale, is combined with the advantages that can be achieved by using grey interval numbers. In this way, a grey extension of MCDA methods is obtained. The application of the proposed approach was considered in the example of evaluating the websites of tourism organizations by using several MCDA methods. Additionally, an analysis of the application of the proposed approach in the case of a large number of respondents, done in Python, is presented. The advantages of the proposed method, as well as its possible limitations, are summarized.


2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 384-384
Author(s):  
Hyejin Kim ◽  
Molly Perkins ◽  
Thaddeus Pope ◽  
Patricia Comer ◽  
Mi-Kyung Song

Abstract ‘Unbefriended’ adults are those who lack decision-making capacity and have no surrogates or advance care plans. Little data exist on nursing homes (NHs)’ healthcare decision-making practices for unbefriended residents. This study aimed to describe NH staff’s perceptions of healthcare decision making on behalf of unbefriended residents. Sixty-six staff including administrators, physicians, nurses, and social workers from three NHs in one geographic area of Georgia, USA participated in a 31-item survey. Their responses were analyzed using descriptive statistics and conventional content analysis. Of 66 participants, eleven had been involved in healthcare decision-making for unbefriended residents. The most common decision was do-not-resuscitate orders. Decisions primarily were made by relying on the resident’s primary care physician and/or discussing within a facility interdisciplinary team. Key considerations in the decision-making process included “evidence that the resident would not have wanted further treatment” and the perception that “further treatment would not be in the resident’s best interest”. Compared with decision making for residents with surrogates, participants perceived decision making for unbefriended residents to be equally-more difficult. Key barriers to making decisions included uncertainty regarding what the resident would have wanted in the given situation and concerns regarding the ethically and legally right course of action. Facilitators (reported by 52 participants) included some information/knowledge about the resident, an understanding regarding decision-making-related law/policy, and facility-level support. The findings highlight the complexity and difficulty of healthcare decision making for unbefriended residents and suggest more discussions among all key stakeholders to develop practical strategies to support decision-making practices in NHs.


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