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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 & Introduction

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 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/in the cloud


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 with 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 JREVIEW CDA+ SAS GENERIC 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

Conclusion

• 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

– [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) 1865 910200

• General Inquiries:


how to rapidly configure oracle life sciences data hub (lsh) to support the management of patient...

Technology

data management

ctms data

visit data

metrics data

lims data

data utilities

ivrx data

cro data