2013 ohsug - how to rapidly configure oracle lsh to support the management of patient data

7/27/2019 2013 OHSUG - How to Rapidly Configure Oracle LSH to Support the Management of Patient Data

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PREVIOUS NEXTHow 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 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


7/27PREVIOUS NEXTPREVIOUS NEXTHow to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013

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



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



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




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




Example of metrics combined with patient data




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




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




Example Setup of Utilities and templates


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PREVIOUS NEXTPREVIOUS NEXTHow to Rapidly Configure Oracle Life Sciences Data Hub (LSH) to Support the Management of Patient Data September 2013

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

[email protected]

+1 877 654 0033

Vicky Green, VP of Sales, Data Management Solutions

[email protected]

+1 877 654 0033

Europe/Middle East/Africa Sales Contact: Rudolf Coetzee, Director of Business Development

[email protected]

+44 (0) 1865 910200

General Inquiries:

[email protected]

2013 ohsug - how to rapidly configure oracle lsh to support the management of patient data

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