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Slide 1
How to Rapidly Configure LSH
to Support the Management ofPatient Data
September 17, 2013
Mike GrossmanVice President of
Clinical Data Warehousing and
Analytics
BioPharm Systems
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Slide 2
Welcome & Introductions
Mike GrossmanVice President ofClinical Data Warehousing and AnalyticsBioPharm 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 integratesOracles 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
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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
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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
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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
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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
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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 StudyData
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 DataMarts for
Scientific
Exploration and
Mining
Specialized DataMarts 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
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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 rapidloading of data into LSH
Study specific blinded views of the data are available fordata such as LIMS data
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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
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How to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013Slide 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
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How to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013Slide 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
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How to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013Slide 12
Example of metrics combined with patient data
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How to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013Slide 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
DatabaseConnection
EDC Systems
xpt, sas7bat
Visualisation, Reporting & Program Development Applications
R JREVIEWCDA+ SASGENERICVISUALISATION
WEBBROWSER
S+SPOTFIRE
This slide courtesy of Oracle Corporation
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How to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013Slide 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
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How to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013Slide 15
Example Setup of Utilities and templates
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Slide 16
Infrastructure required
Production environment sized for service level
Test/Validation environment
Sandbox/Development environment
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Slide 17
Diagram of example production environment
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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
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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
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Slide 20
Example of study data mapper
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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
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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
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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.
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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
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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.
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Slide 26
Q&A
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H t R idl C fi O l Lif S i D t H b (LSH) t S t th M t f P ti t D t S t b 2013
Contact Us North America Sales Contacts:
Rod Roderick, VP of Sales, Trial Management Solutions
+1 877 654 0033
Vicky Green, VP of Sales, Data Management Solutions
+1 877 654 0033
Europe/Middle East/Africa Sales Contact: Rudolf Coetzee, Director of Business Development
+44 (0) 1865 910200
General Inquiries: