Please use this identifier to cite or link to this item: http://hdl.handle.net/11434/1870
Title: An intelligent risk detection framework using business intelligence tools to improve decision efficiency in healthcare contexts.
Authors: Wickramasinghe, Nilmini
Other Authors: Mogimi, Fatemeh
Zadeh, Hossein
Cheung, Michael
Keywords: Risk Management
Clinical Decision Making
Business Intelligence Technologies
Congenital Heart Disease
Heart Defects, Congenital
Health Informatics Clinical Institute, Epworth HealthCare, Victoria, Australia
Epworth HealthCare, Victoria, Australia
Issue Date: 4-Aug-2011
Conference Name: AMCIS
Conference Location: Detroit USA
Abstract: Leading healthcare organisations are recognising the need to incorporate the power of a decision efficiency approach driven by intelligent solutions. The primary drivers for this include the time pressures faced by healthcare professionals coupled with the need to process voluminous and growing amounts of disparate data and information in shorter and shorter time frames and yet make accurate and suitable treatment decisions which have a critical impact on successful healthcare outcomes. This research contends that such a context is appropriate for the application of real time intelligent risk detection decision support systems using Business Intelligence (BI) technologies. The following thus proposes such a model in the context of the case of Congenital Heart Disease (CHD), an area which requires complex high risk decisions which need to be made expeditiously and accurately in order to ensure successful healthcare outcomes.
URI: http://hdl.handle.net/11434/1870
DOI: https://aisel.aisnet.org/amcis2011_submissions/173
Type: Conference Paper
Affiliated Organisations: PhD Student in the school of Business IT & Logistics, RMIT University
RMIT University
Department of Cardiology, Royal Children Hospital, Australia
Appears in Collections:Health Informatics

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