An Intelligent Risk Detection Framework Using Business Intelligence Tools to Improve Decision Efficiency in Healthcare Contexts

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Moghimi, Fatemeh Hoda; Zadeh, Dr. Hossein Seif; Cheung, Dr. Michael; Wickramasinghe, Nilmini
Business Intelligence (BI); Risk detection; Decision support; intelligence continuum (IC); healthcare; Congenital Heart Disease (CHD).
article description
Leading healthcare organizations are recognizing the need to incorporate the power of a decision efficiency approach drivenby intelligent solutions. The primary drivers for this include the time pressures faced by healthcare professionals coupledwith the need to process voluminous and growing amounts of disparate data and information in shorter and shorter timeframes and yet make accurate and suitable treatment decisions which have a critical impact on successful healthcareoutcomes. This research contends that such a context is appropriate for the application of real time intelligent risk detectiondecision support systems using Business Intelligence (BI) technologies. The following thus proposes such a model in thecontext of the case of Congenital Heart Disease (CHD), an area which requires complex high risk decisions which need to bemade expeditiously and accurately in order to ensure successful healthcare outcomes.