An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry. Issue 2 (6th January 2019)

• Record Type: Journal Article
• Authors: Tang, Valerie; Siu, Paul Kai Yuet; Choy, King Lun; Lam, Hoi Yan; Ho, George To Sum; Lee, Carman Ka Man; Tsang, Yung Po
• Is Part Of: Expert systems. Volume 36:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-06
• Subjects: case‐based reasoning technique -- cloud computing technology -- nursing care plan formulation -- text mining; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12369
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high‐quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud‐based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case‐based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up‐to‐date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced.