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Rapid Response to Hospital Operations using Data and AI during COVID-19

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Healthcare leaders today are faced with increasingly complex and unprecedented challenges. With COVID-19 taking the world by storm, the need for an intelligent system of insights that can proactively deliver actionable and real-time knowledge on patient populations is imminent to providing better care.

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Rapid Response to Hospital Operations using Data and AI during COVID-19

  1. 1. Rapid Response to Hospital Operations using Data and AI during COVID-19 Rohan D’Souza Head of Product, KenSci Tony Pastorino VP, IS, Indiana University Health
  2. 2. Agenda Leveraging Data & AI investments How IU health leveraged the cloud and data to build self- service offerings for COVID-19 response Improving Patient Flow with ML Operationalizing Machine Learning at IU Health to improve hospital operations KenSci Realtime Command Center Learning and experience with deployment of the Realtime COVID-19 Command Center at multiple large health systems
  3. 3. Indiana University Health is a non-profit Health System, and the largest healthcare provider in the state of Indiana. PATIENT ADMISSIONS AVAILABLE BEDS TEAM MEMBERS INDIANA RESIDENTS SERVED ~120,000 ~3,000 30,000+ 1M+
  4. 4. Our Journey with Data & AI ZettabytesGigabytes 2000’s 2020’s Azure Data Platform Social Graph IoT Image LOB CRM ERP SQL Server LOB ERM ERP 2010’s Analytics Platform System APS Terabytes Petabytes
  5. 5. Our Investments in Data & AI Self-Service analytics for COVID-19 Advanced analytics & ML for improving hospital ops SQL Modern data warehousing “We require self-service dashboards to uncover insights and take action” “We’re trying to predict when our patients churn LOS ” “We want to integrate all our data—including Big Data— with our data warehouse” 1 / 2/ 3/
  6. 6. Leveraging Data & AI Investments for COVID-19 Response
  7. 7. Our Ecosystem & Process Azure Data Lake Storage STORE Power BI VISUALIZE Azure SQL Data Warehouse MODEL & SERVE Informatica Cloud Azure Databricks INGEST & PREP
  8. 8. Data Layers REPORTING LAYER Team Workspaces Intra-departmental, Securable Reporting Layer Supporting 27 different IUH Analytics Teams INTEGRATED LAYER Harmonized Layer Rebuilt every 24 hours 3+ Years currently SOURCEMART LAYER 31 Sources 1403 Tables Loaded every 24 hours 10+ Years of Raw Data 2 Types Secured By Datatype Secured by Business Owner CUBES Built from Integrated Layer Rebuilt every 24 hours 3+ Years currently ML ANALYTICS * Built from Integrated Layer Deidentified
  9. 9. Empowering Self-service analytics during COVID-19 • Over 250 BI Analysts and Citizen Data Scientists across the system • Strong Self Service Motivation • Centralized and Decentralized Analytics • Data Gathering vs. Data Insight / Analysis • Valuable time gathering, massaging, reconciling, and validating data • More time for data insights EMPOWER INSIGHT DECISION MAKING
  10. 10. Improving Patient Flow with AI/ML
  11. 11. Understanding Healthcare as a complex system Interdependent Cascading effect of bottlenecks leading to downstream patient leakage/loss & crowding Volatile Difficult to forecast variables across patient volumes, acuity, census, and staffing requirements Noisy Finding meaning and opportunities within the vast data ecosystem is a massive undertaking ED Elective Surgery Admit Transfer/ Direct Admit Inpatient OR Outpatients Discharge ARRIVALS INTERHOSPITAL TRANSPORT CAPACITY LENGTH OF STAY STAFFING READMISSION
  12. 12. DischargeAdmi t Inpatients Length of Stay Risk of Readmission Risk of Obs. Failure Discharge Disposition Inpatient Predictions Enable clinicians to prioritize the neediest patients first, and identify opportunity areas for systematic improvement. Optimize availability of inpatient beds Improve readmission rates & penalties Correct patient status assignment Outcomes
  13. 13. Length of Stay Risk of Readmission Risk of Obs. Failure Discharge Disposition Inpatient Predictions Enable clinicians to prioritize the neediest patients first, and identify opportunity areas for systematic improvement. Optimize availability of inpatient beds Improve readmission rates & penalties Correct patient status assignment Outcomes
  14. 14. © 2019 KENSCI CONFIDENTIAL Solution generating scores in minutes from data delivery 85% of encounters predicted within 1.65 days of actual LOS at the time of admission – establishing one of the best documented AI solution in a production setting Summary: Solution Performance
  15. 15. KenSci Realtime Command Center
  16. 16. Bringing Together Data & AI for COVID-19 Response Risk Stratification Realtime Hospital CensusSurge Planning IDENTIFYING PATIENTS AT HIGH-RISK PLANNING FOR PATIENT VOLUMES TRACKING PATIENT CAPACITY KENSCI SMART ON FHIR BASED DATA & AI ACCELERATOR Data Ingestion Pipeline Real-time data feed & InsightsRapid Secure Deployment Built for the Future
  17. 17. Unified Data Analytics Architecture with Azure Databricks and KenS Data Ingestion Agent Runtime ML engine Data Preparation Model Development Model Production ML OpsPipeline Monitoring Key VaultActive Directory RelationalBlob Data Ingress Integration EMR Claims IOMT Streaming Data ? Azure Cloud Services Model Experimentation Model Explainability Single Sign-on Role-based Security Visualization & Analytics Variation Analysis Feature Bank KenSci Analytics Portal
  18. 18. Learnings & Takeaways For other health systems For other Data & AI Teams
  19. 19. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

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