基于粗糙集和软集技术的客户感知医院排名框架

Arati Mohapatro, S. Mahendran, T. K. Das
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引用次数: 2

摘要

医院排名是一项繁琐的任务,因为它涉及到处理大量的基础数据。排名通常是通过比较不同维度的质量和服务来完成的。甚至医院的质量护理衡量也是多维的:它包括临床护理和患者护理的经验。然而,在这项研究中,作者只关注基于客户感知的评级。设计了一个由第一阶段和第二阶段组成的框架。在第一阶段,该模型使用模糊逼近空间(RSFAS)技术中的粗糙集对数据进行分类;在第二阶段,采用模糊软集(FSS)技术生成评级分数。该模型采用HCAHPS年度调查数据对美国各地区医院进行比较。本文展示了如何使用RSFAS(模糊近似空间中的粗糙集)和模糊软集技术对医疗机构进行排名。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Framework for Ranking Hospitals Based on Customer Perception Using Rough Set and Soft Set Techniques
Hospital ranking is a cumbersome task, as it involves dealing with a large volume of underlying data. Rankings are usually accomplished by comparing different dimensions of quality and services. Even the quality care measurement of a hospital is multi-dimensional: It includes the experience of both clinical care and patient care. In this research, however, the authors focus on ratings based only on customer perception. A framework which consists of two stages—Stage I and Stage II—is designed. In the first stage, the model uses a rough set in a fuzzy approximation space (RSFAS) technique to classify the data; whereas in the second stage, a fuzzy soft set (FSS) technique is employed to generate the rating score. The model is employed for comparing USA hospitals by region using annual HCAHPS survey data. This article shows how ranking of the healthcare institutions can be carried out using the RSFAS (rough set in a fuzzy approximation space) and fuzzy soft set techniques.
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