Big data in Healthcare & Life Sciences

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Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine

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Big data in Healthcare & Life Sciences

  1. 1. Big Data Webinar Series
  2. 2. Big Data Webinar Series Customer Intimacy & Develop New BusinessMarch 11, 2014 Operational ExcellenceMarch 18, 2014 March 25, 2014 April 1, 2014 April 3, 2014 Big Data in Public Services Big Data in Healthcare & Life Sciences Big Data in Human Resources
  3. 3. Big Data Applications New Data Structured Semi Not structured Social Media Mobile Sensor data Analytics becomes 1-on-1 & Real-Time Enables a huge number of ‘new’ or improved Big Data Applications Open Data Analytics becomes 1-on-1 & Real-Time Business domains Operational Excellence Customer Intimacy New Business Risk Management Retail Banking/securities Healthcare Life Sci Media & entertainment Government Utilities Insurance Manufacturing Telecom Transport Services X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X … Source: Gartner
  4. 4. Big Data Applications Operational Excellence Customer Intimacy New Business Risk Management Generic Predictive asset maintenance Scenario testing Targeted Advertising Customer entity resolution Sentiment analysis Information-based products & services Crowdsourcing Cybersecurity Fraud detection Auditing for compliance Retail Dynamic pricing Dynamic forecasting Market basket analysis Shopping cart defection Real-time store management Fuzzy matching Recommendations Mall experience gamification Loyalty management Counter-dynamic pricing Sell retail data upstream Real-time offers Multichannel location analysis Customer-centric merchandising Healthcare Integrated patient data Assisted diagnosis Adaptive treatment planning Patient flow management Self evaluation Remote patient monitoring Hotspotting Fraud (ring) detection Life Sciences Clinical trials Translation medicine Virtual humans Global listening capabilities Personalized medication Adaptive treatment planning Self monitoring Drug & medical device safety Clinical trial fraud
  5. 5. Quantified Self
  6. 6. Genome Research
  7. 7. Genome Research
  8. 8. Healthcare & Life Sciences Collaboration
  9. 9. Healthcare & Life Sciences Collaboration Source: Mount Sinai Icahn School of Medicine
  10. 10. US Health Care Provider • 1,2 Million Patients • 15 Hospitals • 185 Day Clinics • 70 Retail Pharmacies • 30.000 Employees • 6.000 Nurses • 1.500 Physicians Benchmark clinical performance against national standards • Treatment costs • Re-admission rate • Length of stay
  11. 11. Big Data Concept HealthCare Data Hub HealthCare Analytics Scheduling Radiology Home DevicesGenomicsTranscriptionsMedications … Lab Data PharmacySurveys PACS EMR Billing
  12. 12. Data Flow Concepts AnalyzeAcquire Organize Decide OLTP Data RDBMS ETL DWH BI
  13. 13. AnalyzeAcquire Organize Decide New Data OLTP Data Data Flow Concepts RDBMS ETL DWH BI
  14. 14. Use Case Drives the Data Flows AnalyzeAcquire Organize Decide OLTP Data RDBMS ETL DWH BI New Data
  15. 15. AnalyzeAcquire Organize Decide New Data OLTP Data RDBMS ETL Use Case Drives the Data Flows
  16. 16. Big Data Reference Architecture PACS DATA SOURCES Legacy EMR Financial RTLS Device Integratio n Clinical Trials Radiology Social Media Medication Laboratory Bio Repository Home Devices Genomics Quantifie d Self Pharmacy POS Transcript ions Security HadoopCluster Operations Linear Scale Compute & HDFS Storage Multitenant Processing: YARN Scrip t Pig SQL Hive Impala Online HBase Accumulo OthersReal- Time Storm In- Memory Spark Batch Map Reduce Governance Tag, Filter & Process Metadata Management Ingest Sqoop DATA REPOSITORIES EDW EDW EDW EDW EDW Surgical Data Mart Diagnosis Data Mart Quality Data Mart Clinical Info Data MartNeo4j APPLICATIONS • Cohort discovery • Predicting read mission • Detection of sepsis pathways • Analyzing test variances • Rapid bedside response • Tracking patient wait times • Home health monitoring • Chronic disease management • Patient scorecards
  17. 17. Big Data Journey Business Defines mandate and requirements IT Acquires and integrates data Data Scientists Build and refine analytic models IT Publishes new Insights Business Consumes insights and measures effectiveness
  18. 18. Cronos Big Data Services Offering Use Case Discovery Workshop Big Data Analytics Implementation Services Proof of Concepts
  19. 19. The role of the Data Scientist Business Strategy Analyst Hadoop System Administrator Hadoop Developer Data Architect Data Analyst/ Statistician Identify Business Pains & demonstrate through Analytical skills how the available data can be exploited on a Strategic Level Hadoop Cluster installation & administration. Data Loading Build the relevant dataset by cleansing, filtering, grouping and aggregating the data using parallel processing languages like MapReduce Hive, Pig, Impala, Spark,… ETL tooling, MDM, Data Cleaning & Matching Integration with Enterprise Architecture Data Governance & Security Through statistical Analysis, conceptual and predictive data modeling, machine learning,… Discover patterns, trends, insights. Translate these to Business Opportunities
  20. 20. Cronos Big Data References Use Case Discovery Workshop Proof Of Concept Implementation Services Big Data Analytics Call Center     Healthcare    Utility    Editor    Telco   ISV  Media  Transport    Manufacturing & Distribution  Online Gaming  
  21. 21. Clever usage of data to make better decisions Conclusions
  22. 22. Contact: matthias.vallaey@cronos.be` +32 496 57 66 27

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