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.
Highlights
Having a large population and high social ageing rate in the subsequent decades, China is being expected to reform the country in a tremendous degree and adapt to the foreseeable social changes with at least twenty years
The public hospital clusters are considerably large in scale and contributing over 75% in the hospital sector, elderly patients contribute over half of the total patient admissions in public hospitals
The similarity of the data records within their own clusters increases while the distances from the cluster centre, i.e. the mean value of the cluster, is shortened
Summary
Having a large population and high social ageing rate in the subsequent decades, China is being expected to reform the country in a tremendous degree and adapt to the foreseeable social changes with at least twenty years. The public hospital clusters are considerably large in scale and contributing over 75% in the hospital sector, elderly patients contribute over half of the total patient admissions in public hospitals. This reveals the elderly relies much more on the public hospital healthcare services and resources than the people from other age groups. It denotes that the overall public hospital services demand, the demand from elderly, are extremely high. In this paper, an Elderly Behaviour Analytics Model (EBAM) is designed to identify the preferences among different types of elderly and facilitate comprehensive understanding on the elderly healthcare needs
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