2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Sciences Data Hub (LSH)

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How to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013
Slide 1
How to Rapidly Configure LSH
to Support the Management of
Patient Data
September 17, 2013
Mike Grossman
Vice President of
Clinical Data Warehousing and
Analytics
BioPharm Systems

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

Slide 3
Agenda
• What do we mean by managing patient data
• Best practices
• Data flows
• Conforming data
• Utilities and tools
• Infrastructure
• Implementation
• Support
• Conclusions and Q&A

Slide 4
What do we mean by managing patient data?
• Everything that occurs to clinical trials data after data
capture
• Includes eCRF data, central labs data, IVRx data, CRO
data , legacy study data
• Also can include metrics data such as CTMS data

Slide 5
Best Practice Business Process
• Conform patient data to a standard within 24 hours after
data capture
• Standard data review and listings available immediately
after first patient first visit
• Data sets available for formal analysis shortly after first
patient first visit
• Standard ADaM datasets available for listings, review, and
analysis immediately after first patient first visit
• Dynamic and cross-study analysis using tools like Spotfire
supplement standard reporting and review

Slide 6
Data flow
• Stage study specific data
• Stage study specific views for cross study data
• Map to conformed standard
• Standard data review and reporting off of conformed study
specific data
• Analysis results in LSH or all analysis in LSH
• Automatic pooling views
• Special purpose data marts down stream from pooled
views

Slide 7
Data flow based on holistic architecture
Outcomes
Common Data
Model
Project level
Conformed Data
Value Added
Study Data
Conformed Study
Data
Operational Trial
Metrics
Inbound
Data
Sources
Master Meta Data
AES & Complaints
Outcomes
External Study
Data
LIMS/PK
Central Labs
CDMS/ EDC
CTMS
Staging
Area
AES & Complaints
Source Specific
Outcomes Data
Shared Study and
Project Meta
Data
Study Specific
Data Staging
Trials
Management
Warehouse
Area
Specialized Data
Marts for
Scientific
Exploration and
Mining
Specialized Data
Marts for
Scientific
Exploration and
Mining
Specialized Data
Marts for
Scientific
Exploration and
Mining
Patient Sub
Setting and
Safety
Warehouse
Clinops Data
Marts
Meta Data Libraries, Version Control, Compliance Change Mgt
Ad-Hoc Query Dashboards Structured Reports Analytical Tools
Strategic
Analysis
Regulatory
Reporting
Data Mining
Clinical
Development
Planning

Slide 8
Staging Data Best Practice
• Stage from EDC
– Direct connect for OC/RDC
– File based or web services for RAVE, Inform and Others
• Direct connect for internal systems such as LIMS, IVRS,
CTMS
• File based load for systems that are not used repeatedly
– Automated File load greatly reduces effort
– SAS macro to load data from a SAS program allows for rapid
loading of data into LSH
• Study specific blinded views of the data are available for
data such as LIMS data

Slide 9
Conforming to standard best practices
• Mapping to a standard structure should be a specification
process between data management and statistics
• Programming can act as a bottleneck and should be
minimized
• Re-usability is key to saving time and effort
• Conforming must be exportable to share with regulators
and partners
• Target service level of processing every active study every
day

Slide 10
Conforming data using a process approach
1
Specify meta data
and mappings
2
Upload and
parse for errors
3
Store under
version control
4
Generate executable
code and test
5
Recommend re-use
of mappings (TBD)
6
Download with
recommendations
7
Meta Data
Reports

Slide 11
Data delivery and data review best practices
• Automatically deliver the conformed data to statistics for
on-going analysis via programming in LSH directly or via
macro call via generic business area
• On-going data review available with Spotfire or Jreview.
– Standard listings available on top of conformed data
– Raw staged data always available for browsing and study specific
listings
• Metrics and CTMS data can be combined with patient data
– LSH works with Oracle Clinical Development Analytics

Slide 12
Example of metrics combined with patient data

Slide 13
Why LSH?
Loading
STAGING CONFORM
Transforming, Standards & Coding File and Data Mart Outputs
SDTM
ADaM
SDTM+
• TFLs
• Reconciliation Reports
• SAS Datasets
• PDF Reports
• Reporting Data marts
• Relational Data marts
• Listings
Oracle Life Sciences Hub – SCE & Validated Data Platform
CODING
POOLING
Messages
Data Sources
Text Files
Safety
Database
Connection
EDC Systems
xpt, sas7bat
Visualisation, Reporting & Program Development Applications
R JREVIEWCDA+ SASGENERIC
VISUALISATION
WEB
BROWSER
S+SPOTFIRE
This slide courtesy of Oracle Corporation

Slide 14
Utilities/Tools required
• LSH includes most of the infrastructure needed
• Utilities needed to complete the picture
– File Loading Utility
– Study Templates
– Data mapping application
– Centrally Located IVRS utility
– Centrally Located LIMS/Lab utility
– Centrally located coding utility
– Pooling Utility

Slide 15
Example Setup of Utilities and templates

Slide 16
Infrastructure required
• Production environment sized for service level
• Test/Validation environment
• Sandbox/Development environment

Slide 17
Diagram of example production environment

Slide 18
System implementation tasks
• Sandbox/Dev installation
• Requirements workshop
• Requirements and design documents
• Example configuration in Dev environment
• Develop an OQ/PQ and other validation documentation
• Install IQ in test environment including utilities
• Execute OQ/PQ and other validation steps
• Install IQ/Smoke test and of Production environment
• Training
• Ongoing Support and maintenance

Slide 19
BioPharm pre-configured environment
• Based on these best practices BioPharm has pre-
configured assets for this best practice environment
– Rapid installation of LSH
– Templates for requirements and design
– Pre-developed utilities and configurations for
• File Loading
• SAS Macro based loading and extract
• Data Pooling
• Blinded sources
– LSH integrated fully functional mapper and meta data repository
– Templates for validation documents
– Training classes

Slide 20
Example of study data mapper

Slide 21
BioPharm environments
• Hosted in dedicated environments in cloud
• Highly scalable
• Fault tolerant configuration
• LSH
• Study Data Mapper
• OBIEE
• SAS
• Informatica
• Jreview
• Spotfire
• Secure FTP
• Oracle Data Management Workbench
• Becomes part or your internal network

Slide 22
Implementation package
• Sandbox Environment
• Requirements and design workshop
• Full system configuration
• IQ/OQ/PQ
• Training
• Test environment and test execution
• Full validation pack
• Production release
• Ongoing technical and application support

Slide 23
On-going support
• Support includes both technical and application
• Technical support includes service levels, backups,
disaster recovery etc.
• Application support includes asking questions about bugs
and how to use the application itself. This can include
questions about programming and setup of studies
• Can act as liaison to to 3rd party software vendors such as
Oracle
• Telephone support and 24x7 at support.biopharm.com
• Multi-lingual support available.

Slide 24
Rapid release with low risk license and pricing
• For a hosted solution
– Sandbox environment available within 2-4 weeks
– Validated production go-live is 4-6 months
• On premise solution also possible
• Software licenses for LSH, Oracle, OBIEE, SAS, Spotfire,
Jreview, Mapper etc. can be included as part of monthly
hosting costs.
– Single contract – single monthly payment
• Implementation available as fixed price or T&M
• Hardware grows as planned and needed

Slide 25
Conclusions
• Best practice minimizes the costs and risks of managing
patient data
• LSH with supporting utilities enables best practice
• Pre-defined mapping utility accelerates the most complex
part of preparing patient data for review
• BioPharm has pre built implementation templates for all
aspects of implementation
• Reduce risks, complexity, and time to go-live by hosting via
BioPharm regulatory compliant cloud.

Slide 26
Q&A

Slide 27
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

2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Sciences Data Hub (LSH)

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2013 OHSUG - Use Cases for Using the Program Type View in Oracle Life Sciences Data Hub (LSH)