Collaborative Decision-Making Method for Patient-Centered Care Based on Interval Type-2 Fuzzy Sets_

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題名：	Collaborative Decision-Making Method for Patient-Centered Care Based on Interval Type-2 Fuzzy Sets
書刊名：	工業工程學刊
作者：	陳亭羽
作者(外文)：	Chen, Ting-yu
出版日期：	2012
卷期：	29:7
頁次：	頁494-513
主題關鍵詞：	以病人為中心之照護；區間型二模糊集合；協同決策；區間型二梯形模糊數；混合平均；整合型規劃模式；Patient-centered care；Interval type-2 fuzzy set；Collaborative decision-making；Interval type-2 trapezoidal fuzzy number；Hybrid average；Integrated programming model
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:2 共同引用:2 點閱:19

Patient-centered care, which is a fundamental component of practicing integrative medicine, places the patient at the center of the delivery of care, improves the continuity of care, and enhances the integration of health professionals and patients. The purpose of this article is to develop a multiperson, multi-attribute model for patient-centered decision-making. Collaborative decision information provided by patients, relatives (family and friends), and healthcare providers is inherently imprecise and involves many uncertainties. Interval type-2 fuzzy sets have a greater ability than ordinary fuzzy sets to handle imprecision and imperfect information in real-world therapeutic applications. In light of patient-centeredness, this article presents a signed distance-based method for handling a collaborative decision-making problem in which individual assessments are provided as interval type-2 trapezoidal fuzzy numbers, and the preference information about attributes is only partially known. Concerning the relative importance of decision makers and group consensus regarding fuzzy opinions, all individual decision opinions are aggregated into group opinions using a hybrid average with weighted averaging and signed distance-based ordered weighted averaging operations. An integrated programming model is developed to estimate attribute weights and to order the priorities of various treatment options based on signed distances, where some deviation variables are introduced to mitigate the inconsistent evaluations. Finally, the feasibility and the effectiveness of the proposed methods are illustrated by a practical example of cancer care.

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