Using Oracle Health Sciences Data Management Workbench to Optimize the Management of Clinical Data from InForm and Other Sources

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Using Oracle Health Sciences Data Management Workbench to Optimize the Management of Clinical Data from InForm and Other Sources, January 2014
Slide 1
Using DMW to Optimize the
Management of Clinical Data from
InForm and Other Sources
January 30, 2014
Mike Grossman
VP, Clinical Data Warehousing & Analytics
BioPharm Systems

Slide 2
Welcome & Introductions
Mike Grossman
VP of Clinical Data Warehousing and Analytics
BioPharm Systems
• 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 Life Sciences Data Hub (LSH) and other data warehousing
and analytics solutions
• Extensive LSH experience
– 10 years of experience designing and developing LSH at Oracle
– 27 years in the industry
– 5+ years of experiencing implementing LSH at client sites

Slide 3
Agenda
• Overview
– Industry challenges
– Drivers and goals
• Setting up studies
– Setting up a study
– Creating data models
– Transforming data
– Creating validation checks
• Managing study data
– Reviewing data
– Creating discrepancies
– Managing discrepancies
– Exporting data
• Conclusions and Q&A

Slide 4
Overview – Industry Challenges
• Complex data validation
– Without burdening the data capture process
• Data cleaning and reconciliation
– Inconsistencies across data from multiple sources
– How to raise discrepancies and where to send them
• Medical review to flag inconsistencies in the data
• Data transformation and standardization
– Separate sources must be conformed, aggregated, and compared
– Maintain data lineage

Slide 5
Overview – Drivers
• Increased productivity and efficiency by
– Reducing manual processing
– Promoting reusability with templates of standard objects and rules
• Increased outsourcing and collaboration, both internal and
external
• Need for connected processes
– Changing regulatory environment
– Separate and complex data sources

Slide 6
Overview – Goals
• Single version of truth for cleaning, transformation, and
analysis
• Open architecture to support the right visualization and
analysis tools for the job
• Scalable, to support large data volumes
• Extensible, to integrate with enterprise applications and
architecture

Slide 7
DMW Overview

Slide 8
Setting Up a Study
• A study includes
– One or more clinical data models
– Mappings and transformation programs, to read
data from one clinical model and write it to the
next model
– Edit check programs, to validate the data in a
clinical data model and raise discrepancies
– Custom listings, created to aid the review of
clinical data

Slide 9
Setting Up a Study

Slide 10
Creating Data Models

Slide 11
Creating Data Models – Adding Central Lab Data

Slide 12
Creating Data Models – Adding InForm Data

Slide 13
Transforming Data
• Clinical data may have
– Many sources
– Many formats
• For reviewing data, design a review model that lets data
managers and other downstream users access all data
• Transform the data by mapping multiple sources to the
single review model
• Perform additional transformations, as required, for other
users

Slide 14
Transforming Data
InForm Views
Central Lab 1
Central Lab 2 Source Format
Data Models
Review Format
Data Model
Analysis Data
Model
SDTM Export

Slide 15
Transforming Data

Slide 16
Creating Validation Checks

Slide 17
Reviewing Data
• Some features that support efficient data review include
– Using searches and filtering the data
– Using custom listings
– Showing data flags (custom and InForm)
– Showing and navigating to discrepancies
– Tracing data lineage
– Exporting the data to an Excel or CSV file

Slide 18
Reviewing Data – Custom Listings
• Add columns from one or more
sources
• Reorder and sort the columns
• Add selection criteria
• Review the results
• Save the custom listing

Slide 19
Reviewing Data – InForm

Slide 20
Creating Discrepancies
• Select one or more records on which to
create a discrepancy
• Add the text message
• Choose a category and state
• Save the new discrepancy

Slide 21
Managing Discrepancies
• From the discrepancies listings you can
– Filter the data
– Set the discrepancy state (Close Discrepancy, Needs DM
Review, etc.)
– Take action, such as adding a comment, on one or more
discrepancies

Slide 22
Managing Discrepancies
You can review
• The details of each discrepancy
• Its full InForm record
• Its complete history

Slide 23
Exporting Data
• Export to Excel
– Data from any model
– Discrepancies
• Export to CSV
– Sort and filter the data
– All displayed records are included in the export

Slide 24
Exporting to Excel

Slide 25
Exporting Lab Data to CSV

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) 7810 373045
• General Inquiries:
– info@biopharm.com

Using Oracle Health Sciences Data Management Workbench to Optimize the Management of Clinical Data from InForm and Other Sources

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Using Oracle Health Sciences Data Management Workbench to Optimize the Management of Clinical Data from InForm and Other Sources