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

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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

– [email protected]

– +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) 7810 373045

• General Inquiries:


Technology

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