文章基本信息

标题：Clustering Brazilian Public Emergency Healthcare Units
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作者：Helder Gomes Costa ; Maria Helena Teixeira da Silva ; Gabriel Nascimento Santos 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：10
页码：566-571
DOI：10.1016/j.ifacol.2022.09.454
语种：English
出版社：Elsevier
摘要：This work aims to describe a modelling that classify Units of Public Healthcare into categories according to these similarities under time variables. To know this classification could help public management in providing better services, once specific strategies and actions to improve the services should be addressed to the specific groups of healthcare units. This paper contributes to this subject by describing a modelling addressed to categorize Brazilian Public Units of Healthcare that operates 24 hours in a day providing public healthcare services. These units are known in Brazil as "UPAS 24h". In the modelling we collected the data from a set composed by 10 UPAS covering 28 days of February, 2021, that have a dayly mean around 115 attendances. The data were first clustered using a k-means algorithm and with the support of the "Visual Kmeans" an web app free to use and available athttps://pykmeans.herokuapp.com.
关键词：Healthcare;Lean Healthcare;Decision;Clustering;Public services