Design of elderly behaviour analytics model in the healthcare industry in Hong Kong

Abstract

Due to the advancement of living standard and medical technologies, the life expectancy of people is further extended which brings tremendous impact to the society in the near future. The ageing population not only increases the pressure to public healthcare services, but also brings urgent needs in long term healthcare resources allocation planning in the society. This paper presents an Elderly Behaviour Analytics Model (EBAM) to identify the hospital healthcare service preferences of elderly for the future planning of healthcare industry. By conducting an elderly-targeted survey, the collected data is analysed to understand the factors affecting the decision of elderly to acquire healthcare services in hospitals. The model applies the genetic algorithm-guided clustering-based association rule mining approach for the segmentation of hospital service preferences of the elderly, and, the identification of relationship between personal characteristics within each cluster. This research study contributes to the understanding the actual healthcare needs of elderly which allows the government and healthcare service providers to adjust or modify the elderly policies and service content.