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Moving Health Care Analytics to Hadoop to Build a Better Predictive Model

Hadoop Summit 2015

Moving Health Care Analytics to Hadoop to Build a Better Predictive Model

  1. 1. Moving Healthcare Analytics to Hadoop to build better predictive models - Saving Cost and Lives Dr. Joe Colorafi – Dignity Health CMIO Dr. Graham Hughes – SAS CMIO Bill Guise – Dignity Health Senior Director IT Sunil Kakade – Dignity Health Director IT June 11th, 2015
  2. 2. 2  Dignity Health  Emerging Healthcare Landscape  What outcomes we will enable?  Big Data Ecosystem  Hadoop Architecture for Healthcare Analytics Agenda
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  5. 5. 5 Healthcare Changing Landscape - Business Performance Challenges Analytics is a mission critical Enterprise Capability to drive Transparency, Insights, Collaboration and to driver operational improvements Business Performance Increasing Regulatory Pressure Decelerating price growth Continuing Cost Pressure Shifting Payer Mix Rapidly evolving technology Deteriorating Case Mix
  6. 6. 6 For example, every year, severe sepsis strikes more than a million Americans. It’s been estimated that between 28 and 50 percent of these people die—far more than the number of U.S. deaths from prostate cancer, breast cancer and AIDS combined***. The potential for Analytics in Healthcare is huge *** National Institute of General Medical Sciences
  7. 7.  What if Dignity Health’s 30 TB Clinical Data  100 billion rows of data  8000 unique provider users  1.2 million meds doses/ year  Could be turned into  Right Information  Right Time  Right Form  Right People & Processes Bringing benefits of Big Data to Health systems
  8. 8. 8 Healthcare Analytics Challenges Patient Data is everywhere but trapped in a myriad of silos • High Complexity • High Variety • Fast Data • Privacy
  9. 9. 9 Healthcare Analytics Challenges • Legacy Systems • Rigid data formats • Unstructured • Dark Data
  10. 10. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.Copyr ight © 2015, SAS Institute Inc. All rights reser ved. Infinite Volume and Variety of Data Disruptive Technology New Problem-solving Mindset BUILDING NEW ANALYTICS CULTURE WITH BIG DATA Unrivaled Processing Power
  11. 11. 11 One Platform, Many Data Sources, Multiple Workloads, All Consumers NoSQL Logs Social Media Sensors Legacy Platforms Cloudera’s Hadoop Distribution EHR CERNER Lab Patient Sat. ADT MS4 Billing Pop Health Predictive Analytics SAS Enterprise Business Intelligence Platform Unified Security Model SAS Data Governance SAS Visual Analytics Platform Text Mining Forecasting & Optimization Machine Learning Real-time Analytics Data Science Unified Audit and Logging HIE CMS Unified Privacy and Compliance Unified Person Master Index Unified API Platform Unified Enterprise Data Model Public Data Sources RDBMS Dignity Health Insights Big Data Ecosystem Unified Data Integration – Source Data Once, Analyze Multiple Times Analytic Capability SAS - Hadoop Integration Open Source Hadoop Platform with Unified Dignity Health processes Dignity Health Data Sources Open Access Data Exploration SAS Intelligence Security
  12. 12. Hadoop and SAS can enable analytics Spectrum 12 Hadoop and SAS can enable full analytics Spectrum
  13. 13. 13 Roadmap of building Dignity Health Insights Big Data Hub 1. Distributed Storage/Computing - Hadoop Ecosystem 2. Compliance - Audit & Logging 3. Security - SAS Intelligence 4. Data Governance - Dataflux 5. Analytics - SAS Enterprise Miner 6. SAS Visual Analytics
  14. 14. 14EHR Lab Patient Sat. ADT Billing Pop Health SAS Analytics Products SAS Visual Analytics HIE CMS Dignity Health Insights Open Source Based in the cloud Big Data Ecosystem Architecture Secure FTP – SQOOP – FLUME Big Data Ecosystem Cerner SAS Intelligence Platform Role Based, LDAP integrated and Metadata level security
  15. 15. Copyr ight © 2014, SAS Institute Inc. All rights reser ved. Prepare data IN Hadoop for analytics Move data FROM Hadoop into a SAS environment Deploy and manage model score code IN Hadoop Lift data IN to memory for analytics at scale Model data at scale in- memory WITH advanced modeling tools Use the right approach for what needs to be done! Explore data at scale, in- memory WITH data visualization SAS & HADOOP - THE PRAGMATIC APPROACH
  16. 16. Copyr ight © 2012, SAS Institute Inc. All rights reser ved. ENABLING THE DATA TO DECISION LIFECYCLE WITH SAS AND HADOOP ECOSYSTEM Access & Manage Data Advanced data management capabilities (ELT, ETL, DQ, virtualization) enabled for Hadoop Interactively Explore & Visualize Quickly Visualize Data in Hadoop, Discover New Patterns, Publish Reports Via Web Reports, Mobile Devices, MS Office Apps Analyze & Model Uncover Patterns and trends in Hadoop data. Interactive and visual environment for analytics. Apply Domain specific high-performance analytics Deploy & Integrate Automatically deploy and score analytic models in the parallel environment. Manage & analyze real time data
  17. 17. Copyr ight © 2014, SAS Institute Inc. All rights reser ved.Copyr ight © 2014, SAS Institute Inc. All rights reser ved. Data Store SAS Data In-Database Data Store SAS Traditional SAS HADOOP + SAS - DESIGN PATTERNS These approaches are complementary & can be combined for maximum effect Data Store SAS Data In-Memory Memory Data
  18. 18. Copyr ight © 2014, SAS Institute Inc. All rights reser ved.Copyr ight © 2014, SAS Institute Inc. All rights reser ved. Results Modeling Code HiveQL Data Enterprise Miner with Hadoop (Model Dev) Enterprise Miner Access to Hadoop Hadoop Cluster Enterprise Miner High Performance Analytics Hadoop Cluster Small Data Volumes Everything Else Data is pulled to the EM server and computation happens on the EM server Code is pushed to the Hadoop cluster and computation is executed on the cluster Results Results
  19. 19. Copyr ight © 2014, SAS Institute Inc. All rights reser ved.Copyr ight © 2014, SAS Institute Inc. All rights reser ved. SMP Architecture LASR Analytic Server Hadoop Cluster MPP Architecture LASR Analytic Server Hadoop Cluster Small Data Volumes Everything Else Data is pulled into memory on the single machine Data is pulled in parallel from the Hadoop cluster data nodes directly to the LASR worker nodes VISUAL ANALYTICS WITH SAS + HADOOP
  20. 20. Analyze Sepsis Alerts By Mortality Rate By Provider Response By Length of Stay By Facilties BioSurvillence SepsisAnalytics with Hadoop and SAS VisualAnalytics 20
  21. 21. 21EHR Lab Patient Sat. ADT Billing Pop Health SAS Analytics Products SAS Visual Analytics HIE CMS Dignity Health Insights Cloud based Security Compliant Big Data Architecture Secure FTP – SQOOP – FLUME HADOOP Ecosystem Cerner SAS Intelligence Platform Role Based, LDAP integrated and Metadata level security
  22. 22. Social Community UI, Authenticate, Submit & Request Data, Navigate & Access Applications, Collaborate & Share Insights Packaged Analytic Applications & Actionable Insights Predictive Models, Benchmarks, Actions/Alerting – Clinical, Administrative, Operations, Financial , Quality, Gaming Theory  Analytic Tools Foundation Data Connectivity, Data Quality, Visualization, Segmentation, Data Mining, Forecasting, Audit Trails, NLP, Machine Intelligence  Storage & Data Bladed Environment - EDW, ODS, Marts, Hadoop + Customer Data, In-Memory Databases, Virtual Data Marts Secure Cloud EHR Lab Patient Sat. Reg/Adt  Patients  EHR  Call Center  Visualization  MS Office  Regulatory reporting  Partners Data Insights Billing Other Patients Clinicians Managers Analysts IT Staff Users Any Customer Any Channel Any Device Any Input Source Internal/External Any Service Any vertical or horizontal Slice of stack Any Output Destination Internal/External 3rd Party App The Technology: Dignity Health Insights Manage Financial Risks & Incentives Proactively Manage Care Quality & Outcomes Improve Efficiency of Care Delivery Population Health and Engage Patients Capabilities used in Sepsis Biosurvillence  Open Source Big Data Platform  User Authentication with Dignity Security System  Audit and Logging  Integration with Registration and Clinical systems  SAS Enterprise Business Intelligence  SAS Visual Analytics  SAS Data Quality tools  Mobile Delivery Pilot Desired Export Dignity Health Insights One Platform, Many Data Sources, Multiple Workloads, All Consumers
  23. 23. 23 Dignity Health Insights use cases are endless.. • Patient readmission reduction Predictive Model • Broad System Exploration with Speed – COMPASS • Legacy Reporting System modernization • Pharmacy Analytics • UMPI – Universal Master Patient Index
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