The document discusses implementing a data warehouse using Oracle Life Sciences Hub (LSH). It covers example types of data warehouses including operational, exploratory analysis, medical review, and safety mining. Techniques for creating data warehouses within and external to LSH are presented, along with common challenges such as auditing, expertise, and standards changes. The presentation provides an overview of data warehouse implementation using LSH.

1. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 1
Data Warehouse
Implementation
September, 2013
Mike Grossman
Vice President of
Clinical Data Warehousing and
Analytics
BioPharm Systems

2. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 2
Welcome & Introductions
Mike Grossman
Vice President of
Clinical Data Warehousing and Analytics
BioPharm Systems, Inc.
• CDW/CDA practice lead since 2010
– Expertise in managing data for all phases and styles of clinical trials
– Leads the team that implements, supports, enhances, and integrates
Oracle’s LSH and other data warehousing and analytic solutions
• Extensive Oracle Life Sciences Hub (LSH) experience
– 10 years of experience designing and developing Oracle Life Sciences
Hub at Oracle
– 27 years in the industry
– 5+ years of experiencing implementing LSH at client sites

3. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 3
Agenda
• Example types of Data Warehouses
• Why use LSH
• Techniques for creating Data
Warehouses
• Challenges

4. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 4
Example Types of Data Warehouses
• Oracle Life Sciences Data Hub (LSH) can be
used to prepare data for reporting, analysis,
medical review, and data mining.
• One of the more complex tasks for
successful cross-study reporting, analysis,
medical review, and data mining systems is
implementing a warehouse that will withstand
the test of time.
• Types of warehouses:
– Operational data for clinical operations and data
management
– Exploratory analysis and predictive analytics
– Medical review
– Safety mining

5. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 5
Operational Metrics Data Warehouse
• Oracle Clinical Development Analytics (CDA)
• Dimensional Models proven
• Integration of CTMS, EDC, Project management, and
financial systems
• Is this part of corporate enterprise warehouse strategy?
• Match merge of key entities
• Does it need formal validation and audit?

6. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 6
Exploratory Analysis and Predictive Analytics
Stage 1. Data Preparation
(Acquire, Transform, Enhance, Standardize)
Historic Dataset Files
Study Data
EDC data and other
study data Data
Standardization
AE
DM …
Outcomes
Stage 3. Analytics & Model Building
Analyze, Define and
Train Model
Stage 4. Deployment & Reuse
Predictive Analysis ComponentsSelection Components
Ad hoc &
Std Analysis
Value Added
Processing
Stage 2. Select & Explore
(Acquire, Transform, Enhance, Standardize)
Selection Components

7. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 7
Medical Review Data Warehouse
• Sourced from EDC and other clinical trial data
• Automatically pooled study data
• Dimensional model for cross-study review
• Specialized data marts for patient profile
• Write back functionality for review status tracking
• Graphical review tool, typically Spotfire or Jreview
• Some sort of auditing is required to indicate “What has
changed since I last reviewed this subject ?”

8. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 8
Safety Mining Warehouse
• Many Sources including
– Safety System such as Argus
– FDA AERS database
– Clinical Trial data
– Healthcare records
• Specific data marts needed for structured mining and
signal management
– Empirica Signal and Empirica Topics
• Broad data model for exploratory mining
– Oracle Health Sciences Translational Research Center
– Oracle Healthcare Data Warehouse Foundation

9. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 9
Why Use LSH?
• Version control, snapshots, and Auditing
• Multiple environments in a single application
– Development, Test, Production
• Security
• Data Blinding/Unblinding
• Life Cycle Management
• Reusability
• LSH APIS can automate complex tasks such as
– Automatically adding studies to dimensional models
– Automatically generate longitudinal data marts from subject subsets

10. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 10
Techniques for creating a Warehouse
• Within LSH
– Using Programs to pool, Conform, aggregate data
– Use generated pooling/conformation tools
• External to LSH
– Using data sourced from LSH and/or external sources
– Using Informatica external, store data mart in LSH
– Using PLSQL
• Common tools
– Data loads
– Pass-through views
– No coding using reusable components
– Automatic creation of target structures from source
– Familiar use of Oracle tables and views, SAS datasets, Text files
– Automated batch loads (scheduled or triggered by message)

11. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 11
Example Data Warehouse Build Processes (show a
few)
• Conform data
from multiple
sources to a
single format
Conform
• Merge the data
from multiple
sources into a
single structure
format
Pool
• Evaluate data
for audit, if
audit is
unavailable
Audit
• Establish facts
from pooled data
using Audit data
to establish SCD
Base Facts
• Aggregate base
facts to higher
levels of
aggregation
Aggregate

12. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 12
Challenges in Warehousing Implementation
• Auditing may not be available
• Appropriate expertise may not be available
• Multiple version of Standards/changing
standards
– For source data
– For target data mart
• Big single corporate enterprise warehouse
balances with special purpose warehouses
• Tracking the process around data review
and signal management

13. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 13
Q&A

14. PREVIOUS NEXTPREVIOUS NEXTOracle Health Sciences User group September 2013 Slide 14
Contact Us
• North America Sales Contacts:
– Rod Roderick, VP of Sales, Trial Management Solutions
– rroderick@biopharm.com
– +1 877 654 0033
– Vicky Green, VP of Sales, Data Management Solutions
– vgreen@biopharm.com
– +1 877 654 0033
• Europe/Middle East/Africa Sales Contact:
– Rudolf Coetzee, Director of Business Development
– rcoetzee@biopharm.com
– +44 (0) 1865 910200
• General Inquiries:
– info@biopharm.com