|Wong, Siu Fung Bennie
|An intelligent e-healthcare system for domestic care industry
|Tsui, Eric (ISE)
Ho, George (ISE)
|Hong Kong Polytechnic University -- Dissertations
Older people -- Medical care -- China -- Hong Kong
Medical care -- Technological innovations
|Faculty of Engineering
|xi, 152 pages : color illustrations
|Population ageing is a common phenomenon of throughout the world. In Hong Kong, the proportion of the population aged 65 and over is projected to rise markedly from 15% in 2014 to 33% in 2064. To cope with the remarkable elderly service needs and associated challenges, many e-Healthcare systems for supporting elderly healthcare have been introduced. For instance, the e-Healthcare application of real time non-invasively biomedical monitoring without affecting the normal life of a patient. Although e-Healthcare systems contain large amounts of useful information about individual health-related data, the hidden relationships in the available data are difficult to be discovered without effective analysis tools. Recently, much research work has been done to support disease prediction or diagnosis, disease correlation, disease risk analysis and drug reaction detection, however, research in the elderly healthcare service area is still limited. In view of the elderly caregiving service being in high demand nowadays, this study aims to demonstrate the processes in mining useful knowledge from the data attributes in the e-Healthcare system. These support the elderly healthcare condition monitoring services by effectively making use of the existing data to provide decision support for the caregivers. Case studies were carried out to identify the association between the specific collected demographic data, behavioral data and the health measurements data in the e-Healthcare system in order to support care management tasks in the elderly, as well as the use of artificial intelligence (AI) to improve the efficiency and effectiveness of the online healthcare services appointment making process. Knowledge discovery in databases (KDD) uses various data mining techniques, such as the decision tree, Apriori, and rule based artificial intelligence algorithms. The proposed fuzzy association algorithm in this paper integrates membership functions to evaluate the fuzzy sets of the linguistic representation of the quantitative attributes to find significant relationships between the selected data attributes in healthcare systems. The study not only demonstrates the capability of an e-Healthcare system in supporting the elderly care services via the data collection phase, data analysis phase and data reporting phase but also proves how to integrate AI with healthcare mobile apps to improve the customer satisfaction and domestic care service provision. The caregivers can use the discovered rules for proactive decision support of healthcare services and to improve the overall service quality by enhancing the elderly healthcare service responsiveness. In addition, although e-Healthcare has been proven to effectively improve the quality of services in the health sector, there are still barriers in adopting the technology in healthcare organizations. To clarify the challenges regarding the adoption of e-Healthcare in healthcare organizations for the elderly, this study also assessed the attitudes and preferences of nursing professionals and public users in supporting healthcare and public users in using e-Healthcare in Hong Kong so as to identify the critical factors that affect their behavioral intention to use the technology.
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