長照2.0服務據點規劃之研究：以屏東縣為例
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國立交通大學經營管理研究所
Institute of Business and Magement, National Chiao Tung University
Institute of Business and Magement, National Chiao Tung University
DOI
10.29416/JMS.201904_26(2).0004
Abstract
政府面對國內日益嚴重的人口高齡化問題持續推動長期照顧政策，其中長期照護2.0政策即是將全國長照資源分成ABC三個等級，各縣市運用三個等級長照機構建構服務平均化與照護可近性的服務體系。為確保以上政策目標，本研究針對服務機構ABC三個等級的服務區域提出劃分方式，其中A級機構以鏈結方式建構其管轄範圍，另外以佛羅諾伊法切割出目前政府列冊B級長照機構的服務範圍，C級長照機構與住民更有密切關係，本研究運用K-平均分群演算法將村里活動中心依距離進行分群，再針對住民家屬進行問卷調查，以層級分析法找出住民適合的C級長照機構。本研究最後以屏東縣市長照機構為實證對象，依據長照2.0的設置規範規劃出現有ABC級服務據點的服務區域，以達到區域照護人口平均化與住民服務近便性的目的。
The issue of population ageing is an important issue for our country. The purpose of the 2.0 version of the government's plan to promote long-term care is to build a long-term service system based on county and city units by dividing existing long-term resources into three levels of ABC. This study uses the list of government units to list the institutions currently providing long-term care in the community as the bases of the B-level institutions, and uses Voronoi as a regional division method to plan the scope of service areas of existing long-term care institutions. The research is based on the research object of the community activity center, using the K-average clustering algorithm to group the activity centers by distance, and then using the hierarchical analysis to select the service base of the C-level organization. This study uses Pingtung as an example and the results obtained can be used as a reference for local governments in planning long-term care systems.
The issue of population ageing is an important issue for our country. The purpose of the 2.0 version of the government's plan to promote long-term care is to build a long-term service system based on county and city units by dividing existing long-term resources into three levels of ABC. This study uses the list of government units to list the institutions currently providing long-term care in the community as the bases of the B-level institutions, and uses Voronoi as a regional division method to plan the scope of service areas of existing long-term care institutions. The research is based on the research object of the community activity center, using the K-average clustering algorithm to group the activity centers by distance, and then using the hierarchical analysis to select the service base of the C-level organization. This study uses Pingtung as an example and the results obtained can be used as a reference for local governments in planning long-term care systems.