The sequence analysis of hospitals that established social work department under fuzzy environment

Abstract

ABSTRACTThis paper reviews the existing literature on hospital social work and discusses intervention strategies for improving social work practice in hospital. The objective of this study was to improve the quality of medical care. But few studies have compared social work services between different hospitals. This study describes qualitative analysis under fuzzy environment， extracts the main influencing factors and establishes a comprehensive evaluation index system. It provides comprehensive evaluation for alternative hospitals by the fuzzy clustering method. This paper proposes a new mixed fuzzy clustering algorithm on the basis of analysing the axiomatic fuzzy set (AFS) and K-means algorithm, which is not affected by some complicated parameter issues and has higher statistical validity. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied for selecting the best option for each cluster and a comparative analysis is done. Results from a case study in Shanghai, China, confirm that the proposed approach is effective by using information entropy to test. By comparing AFS, K-means and C-means algorithms, the hybrid algorithm can find the two closest attributes of evaluation index of hospital social work, and the proposed approach can be easily help raise the level of hospital social work service.