title: Clinical data warehousing for evidence based decision making.
abstract: Large volumes of heterogeneous health data silos pose a big challenge when exploring for information to allow for evidence based decision making and ensuring quality outcomes. In this paper, we present a proof of concept for adopting data warehousing technology to aggregate and analyse disparate health data in order to understand the impact various lifestyle factors on obesity. We present a practical model for data warehousing with detailed explanation which can be adopted similarly for studying various other health issues.
authors.affiliation: School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Queensland 4000, Australia.
authors.forename: Lekha
authors.initials: L
authors.lastname: Narra
authors.affiliation: School of Psychology, Faculty of Society & Design, Bond University Gold Coast, Queensland 4229, Australia.
authors.forename: Tony
authors.initials: T
authors.lastname: Sahama
authors.affiliation: School of Psychology, Faculty of Society & Design, Bond University Gold Coast, Queensland 4229, Australia.
authors.forename: Peta
authors.initials: P
authors.lastname: Stapleton
mesh_terms: D000293:Adolescent; D017677:Age Distribution; D000368:Aged; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D019317:Evidence-Based Medicine; D005260:Female; D006801:Humans; D008019:Life Style; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000175:diagnosis / Q000453:epidemiology* / Q000628:therapy*; D010865:Pilot Projects; D015995:Prevalence; D011793:Queensland; D012307:Risk Factors; D040242:Risk Reduction Behavior; D017678:Sex Distribution; D016896:Treatment Outcome; D055815:Young Adult