A4 Refereed article in a conference publication
Avoiding Hazards - What Can Health Care Learn from Aviation?
Authors: Olli Sjöblom, Juho Heimonen, Lotta Kauhanen, Veronika Laippala, Heljä Lundgrén-Laine, Laura-Maria Murtola, Tapio Salakoski, Sanna Salanterä
Editors: Kristina Eriksson-Backa, Annika Luoma, Erica Krook
Publication year: 2012
Journal: Communications in Computer and Information Science
Book title : Exploring the Abyss of Inequalities
Journal name in source: EXPLORING THE ABYSS OF INEQUALITIES
Journal acronym: COMM COM INF SC
Volume: 313
First page : 119
Last page: 127
Number of pages: 9
ISBN: 978-3-642-32849-7
ISSN: 1865-0929
DOI: https://doi.org/10.1007/978-3-642-32850-3_11(external)
Abstract
Effective methods are needed to identify and analyze risks to improve patient safety. Analysing patient records and learning from "touch and go"- situations is one possible way to prevent hazardous conditions. The eventuality for the incident or accident occurring may be markedly reduced in case the risks can be efficiently diagnosed. Through this outlook, flight safety has been successfully improved during decades. Aviation and health care share many important points and similarities, thus the methods for improving safety could be transferred between the domains. In this paper, text mining and especially clustering is applied to identify lethal trends in both patient records and aviation for comparing and evaluating these trends in the two fields.
Effective methods are needed to identify and analyze risks to improve patient safety. Analysing patient records and learning from "touch and go"- situations is one possible way to prevent hazardous conditions. The eventuality for the incident or accident occurring may be markedly reduced in case the risks can be efficiently diagnosed. Through this outlook, flight safety has been successfully improved during decades. Aviation and health care share many important points and similarities, thus the methods for improving safety could be transferred between the domains. In this paper, text mining and especially clustering is applied to identify lethal trends in both patient records and aviation for comparing and evaluating these trends in the two fields.
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