2013 ohsug - clinical data warehouse implementation

PREVIOUS NEXT PREVIOUS NEXT Oracle Health Sciences User group September 2013

Slide 1

Data Warehouse

Implementation

September, 2013

Mike Grossman Vice President of

Clinical Data Warehousing and

Analytics

BioPharm Systems


Slide 2

Welcome & Introductions

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 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


Slide 3

Agenda

• Example types of Data Warehouses

• Why use LSH

• Techniques for creating Data

Warehouses

• Challenges


Slide 4

Example Types of Data Warehouses

• Oracle Life Sciences Data Hub (LSH) can be used to prepare data for reporting, analysis, medical review, and data mining.

• One of the more complex tasks for successful cross-study reporting, analysis, medical review, and data mining systems is implementing a warehouse that will withstand the test of time.

• Types of warehouses: – Operational data for clinical operations and data

management

– Exploratory analysis and predictive analytics

– Medical review

– Safety mining


Slide 5

Operational Metrics Data Warehouse

• Oracle Clinical Development Analytics (CDA)

• Dimensional Models proven

• Integration of CTMS, EDC, Project management, and

financial systems

• Is this part of corporate enterprise warehouse strategy?

• Match merge of key entities

• Does it need formal validation and audit?


Slide 6

Exploratory Analysis and Predictive Analytics Stage 1. Data Preparation

(Acquire, Transform, Enhance, Standardize)

Historic Dataset Files

Study Data

EDC data and other

study data Data

Standardization

AE

DM …

Outcomes Stage 3. Analytics & Model Building

Analyze, Define and

Train Model

Stage 4. Deployment & Reuse

Predictive Analysis Components Selection Components

Ad hoc &

Std Analysis

Value Added

Processing

Stage 2. Select & Explore (Acquire, Transform, Enhance, Standardize)

Selection Components


Slide 7

Medical Review Data Warehouse

• Sourced from EDC and other clinical trial data

• Automatically pooled study data

• Dimensional model for cross-study review

• Specialized data marts for patient profile

• Write back functionality for review status tracking

• Graphical review tool, typically Spotfire or Jreview

• Some sort of auditing is required to indicate “What has

changed since I last reviewed this subject ?”


Slide 8

Safety Mining Warehouse

• Many Sources including – Safety System such as Argus

– FDA AERS database

– Clinical Trial data

– Healthcare records

• Specific data marts needed for structured mining and signal management – Empirica Signal and Empirica Topics

• Broad data model for exploratory mining – Oracle Health Sciences Translational Research Center

– Oracle Healthcare Data Warehouse Foundation


Slide 9

Why Use LSH?

• Version control, snapshots, and Auditing

• Multiple environments in a single application

– Development, Test, Production

• Security

• Data Blinding/Unblinding

• Life Cycle Management

• Reusability

• LSH APIS can automate complex tasks such as

– Automatically adding studies to dimensional models

– Automatically generate longitudinal data marts from subject subsets


Slide 10

Techniques for creating a Warehouse

• Within LSH – Using Programs to pool, Conform, aggregate data

– Use generated pooling/conformation tools

• External to LSH – Using data sourced from LSH and/or external sources

– Using Informatica external, store data mart in LSH

– Using PLSQL

• Common tools – Data loads

– Pass-through views

– No coding using reusable components

– Automatic creation of target structures from source

– Familiar use of Oracle tables and views, SAS datasets, Text files

– Automated batch loads (scheduled or triggered by message)


Slide 11

Example Data Warehouse Build Processes (show a

few)

• Conform data from multiple sources to a single format

Conform

• Merge the data from multiple sources into a single structure format

Pool • Evaluate data

for audit, if audit is unavailable

Audit

• Establish facts from pooled data using Audit data to establish SCD

Base Facts • Aggregate base

facts to higher levels of aggregation

Aggregate


Slide 12

Challenges in Warehousing Implementation

• Auditing may not be available

• Appropriate expertise may not be available

• Multiple version of Standards/changing

standards

– For source data

– For target data mart

• Big single corporate enterprise warehouse

balances with special purpose warehouses

• Tracking the process around data review

and signal management


Slide 13

Q&A


Slide 14

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:


2013 ohsug - clinical data warehouse implementation

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