1. 9/10/2018
Largest US Healthcare Dataset in Hadoop enables Patient-level Analytics in Near Real Time September 28, 2016
Navdeep Alam Director of Data Warehousing

2. Agenda Who is IMS Health Health care data ecosystem at IMS
Opportunity and Challenges: Make a Greater Difference in Patient Healthcare
Solution – Anonymous Patient Longitudinal Analysis
Lessons Learned

3. Who is IMS Health? Market
Healthcare Information, Technology and Services
Solutions
Deliver unique insights into diseases, treatments, costs and outcomes
Experience
Founded 1954, Operation 100+ Countries, 15,000 Employees, 55+ Billion Health Transactions Annually
# of Customers
5000+ clients

4. Health Care Data Ecosystem
IMS Health – Where Does Our Data Come From

5. Future Data Growth is Exponential
Social Media, IOT, Genomics
Billions More Transactions
Billions of Anonymous Patients

6. Make a Greater Difference in Patient Healthcare
Precision Medicine, Better Outcomes, Propel Research towards Cures
Longitudinal Studies
Find Patterns Across All Patients
Predict and Influence Outcomes
Help Reduce Healthcare Costs
Clinical Trials and Drug Research Improvements
Improve Provider Care

7. Challenges Obstacles to Realizing the Greater Opportunity Data Silos
Reduced Data Currency
Analytics Away from the Data
Analytics Too Time Consuming and Expensive
Cost High on Current Systems

8. Solution - Patient Longitudinal Records
Organized for Fast Access and Reduced Data Shuffle
Traditional Warehoused Data Rx n 
Big Data Factory Dx t 
Patient Longitudinal Records n t 
EMR r 
New Source Type  n
Each color = Unique de-identified patient ID.
Each shape = A type of patient data.
Filled shapes = Data of interest
Complex Nested Data Type

9. Solution - Different Storage Engines
Storage to Match the Access Pattern
Aggregates/Counts
Web Speed (ms)
Faceted Search
Solr
Complex Nested Type
Patient Longitudinal Records n t 
Web Applications
Fast lookup of longitudinal Entity (i.e. Patient)
HBase
HUE
RDBMS
ETL Process
Rest
Deep Learning Analytics
Longer Running Queries (min vs. days)
Hive Nested Bucketed
JDBC/SQL
ETL Process
Hive
Partitioned
BI/DW Workloads
SQL

10. Hadoop Storage Engines
Parquet/Hive vs. HBase vs. Kudu

11. Evolution of Different Storage Engines
Storage to Match the Access Pattern with Kudu
Complex Nested Type
Patient Longitudinal Records n t 
Aggregates/Counts
Web Speed (ms)
Faceted Search
Solr
Web Applications
HUE
RDBMS
Fast lookup of longitudinal Entity (i.e. Patient)
ETL Process
Rest
Deep Learning Analytics
Longer Running Queries (min vs. days)
JDBC/SQL
Kudu
BI/DW Workloads
SQL

12. Anonymous Patient Longitudinal Analysis
Rx (Prescriptions) and Dx (Medical Claims) Longitudinal Analysis

13. What does this do for us? Value Proposition See Patterns in Data
Explore the Data Before Analysis
Variety of Analysis in Parallel
Time-to-Value Greatly Increased
Reduced Cost
Innovation

14. Rethink Everything! Lessons Learned
Technology, Cultural, and Process Management Changes
Rethink Everything!

15. Thank You Navdeep Alam Director of Data Warehousing 9/10/2018