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Title: An intelligent case-based knowledge management system for quality improvement in nursing homes
Authors: Choy, KLT 
Siu, KYP
Ho, TSG 
Wu, CH 
Lam, HY 
Tang, V 
Tsang, YP 
Keywords: Quality improvement
Knowledge management system
Long-term care services
Issue Date: 2018
Publisher: Emerald Group Publishing Limited
Source: VINE, 2018, v. 48, no. 1, p. 103-121 How to cite?
Journal: VINE 
Abstract: Purpose - This paper aims to maintain the high service quality of the long-term care service providers by establishing a knowledge-based system so as to enhance the service quality of nursing homes and the performance of its nursing staff continually.
Design/methodology/approach - An intelligent case-based knowledge management system (ICKMS) is developed with the integration of two artificial intelligence techniques, i.e. fuzzy logic and case-based reasoning (CBR). In the system, fuzzy logic is adopted to assess the performance through the analysis of the long-term care services provided, nurse performance and elderly satisfaction, whereas CBR is used to formulate a customized re-training program for quality improvement. A case study is conducted to validate the feasibility of the proposed system.
Findings - The empirical findings indicate that the ICKMS helps in identification of those nursing staff who cannot meet the essential service standard. Through the customized re-training program, the performance of the nursing staff can be greatly enhanced, whereas the medical errors and complaints can be considerably reduced. Furthermore, the proposed methodology provides a cost-saving approach in the administrative work.
Practical implications - The findings and results of the study facilitate decision-making using the ICKMS for the long-term service providers to improve their performance and service quality by providing a customized re-training program to the nursing staff.
Originality/value - This study contributes to establishing a knowledge-based system for the long-term service providers for maintaining the high service quality in the health-care industry.
ISSN: 2059-5891
EISSN: 1474-1032
DOI: 10.1108/VJIKMS-01-2017-0001
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