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Microsoft Fabric Unifies Healthcare Data Analytics

Northwestern Medicine and SingHealth are leveraging Microsoft Fabric's data and AI capabilities to unify healthcare data analytics. This transformative solution helps integrate clinical data, meet regulatory compliance, and extract actionable insights to enhance patient care and health outcomes.

Industry
Healthcare
Published
October 2023

Reported outcomes

97%

quantified impactOther quantified impact

Strategic outcomes

Better decisions & insightEnabled use of previously untapped data for insightsSpeed & agilityAccelerated deployment of unified healthcare analyticsCustomer experience & trustImproved clinical decision support and patient outcomesCost efficiencyReduced manual data stitching effort

Primary read

Use case focus

Showing 3 of 4

  • 1Automated clinical data integration and harmonization
  • 2Advanced analytics for early disease detection and population health management
  • 3Real-time operational insights for resource optimization in hospitals
  • Healthcare data siloed across disparate systems (e.g., EHRs, imaging, text, video)
  • 97% of hospital data goes unused in decision-making
  • Hospitals generate 50 petabytes of data per year, overwhelming legacy analytics approaches
  • Regulatory compliance requirements complicate data integration and analytics
  • Time-consuming and costly to manually unify multimodal health data
  • Implemented Microsoft Fabric to unify data analytics across clinical, imaging, and operational sources
  • Enabled secure, governed access and analysis of multimodal data (text, images, video, EHRs)
  • Automated data integration to streamline compliance and reduce manual effort
  • Provided ready-to-use, industry-specific analytics solutions designed for healthcare
  • Enabled use of previously untapped 97% of hospital data for insights
  • Accelerated deployment of unified healthcare analytics across organizations
  • Improved clinical decision support and patient outcomes
  • Reduced cost and effort associated with manual data stitching
Sources & evidence1
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