PATIENT-CENTERED DECISION MAKING: LESSONS FROM MULTI-CRITERIA DECISION ANALYSIS FOR QUANTIFYING PATIENT PREFERENCES. (26th December 2017)

• Record Type: Journal Article
• Authors: Marsh, Kevin; Caro, J. Jaime; Zaiser, Erica; Heywood, James; Hamed, Alaa
• Is Part Of: International journal of technology assessment in health care. Volume 34:Number 1(2018)
• Page Start: 105
• Page End: 110
• Publication Date: 2017-12-26
• Subjects: MCDA, -- Methodology, -- Patient voice, -- Patient preferences, -- Patient centered care; Medical technology -- Periodicals; Technology assessment -- Periodicals; 610.28
• DOI: 10.1017/S0266462317001118
• Languages: English
• ISSNs: 0266-4623

Abstract

Abstract : 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.