tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons Learned in Academic and Life Science Settings Dan Housman, Recombinant by Deloitte The Recombinant by Deloitte team has worked with organizations such as Kimmel Cancer Center as a model to adapt existing mature i2b2 implementations to meet business and scientific needs. Other organizations are increasingly focused on how to use cloud and high performance computing models to achieve different performance levels. Advanced initiatives are progressing to link commercial tools such as Qlikview to explore tranSMART data and to solve for key gaps in scientific pipelines. Dan will present recent lessons learned, new capabilities, and some of the impact on the path forwards for future tranSMART updates.

1. A tranSMART journey back to the real world
at Deloitte
November 2013
tranSMART

2. Agenda Topics
•
•
•
•
•
2
About Recombinant By Deloitte
Hot topics from Deloitte client community
Real World Evidence + In Memory Computing
I2b2/AMC back translation
„integrated tranSMART‟ demo (1.2 components)
preview
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.

3. About
Recombinant By Deloitte

4. Recombinant + Deloitte - Organization Within Deloitte
Deloitte U.S. Firms
Audit &
Enterprise
Risk
Services
Tax
Financial
Advisory
Services
Consulting
Human Capital
Technology
Strategy & Ops
Service Area
Service Area
Service Area
Information Management & Life
Sciences Health Care
Consulting
Services
Innovation
Recombinant
By
Dedicated US-India
(USI) Resources
4
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Deloitte

5. Recombinant Vision For Capabilities for Translational Medicine
Payors
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Pharma

6. General Market Approach
Products / Tools
 Data Strategy
 Data Governance
Key
Capabilities
Target
Markets
 Data Warehousing/Bioinformatics
Implementations
 Professional Open Source Support
Contracts




 Data Trust
 Selectrus Analytics
 Miner Suite
Clinical Performance Improvement
Clinical Quality
Operational Excellence
Accountable Care
Provider /
Research
ACO / Payer
-6-




 Data Integration Hub
 Open source tools
(I2B2, SHRINE, tranSMA
RT)
Translational Research
Cost Effectiveness
Comparative Effectiveness
Pharmacovigilance
Life Sciences
Federal
6
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Services

7. -7PPT Template_Recombinant by Deloitte.potx
Client and Partner Ecosystem

8. Hot Topics

9. Real World Evidence Objectives
9
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.

10. Convergence of translational informatics data mining approaches
Observational data mining
High volume assay multimodal data
Comparative effectiveness
Target identification
Unmet health system needs
Target validation
Health economics
Pharmacogenomic markers
Safety signal sensitivity
Value based medicine
RWE
Precision
Medicine
Indication expansion
Cross-study analysis
Competitive intelligence
System biology models
Patient stratification
External Innovation
10
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.

11. (Dan Housman’s)
Translational Research Enterprise Informatics
Infrastructure Maturity Model
Level 7
Level 6
Level 5
Cognitive computing
Real time decision support
External innovation and validation/optimization
Level 4
Level 3
Level 2
Level 1
Level 0
Business focused solutions
Enterprise utilization and standardization
Data integration – data warehouse
Fragmented and siloed analyses
Reliance on external vendors

12. Translational Research Enterprise Informatics nfrastructure Maturity Model
Level 7
Cognitive computing: Advanced ‘many to many’ unsupervised discovery algorithms with sufficient supporting underlying semantic models .
Use of very large compute to identify hard to find insights. Advanced imaging feature detection analysis. Significant use of NLP enrichment
and ‘on demand’ access to external data on ad-hoc basis. Broad access and use of phenotype and genotype for large populations. Use of in
silico models for systems biology translated from inputs and molecular innovation.
Level 6
Real time decision support: Use of predictive analytics to drive decision support at multiple levels. Patient level decision support with use of
molecular markers such as trial recruitment at point of care leveraging informatics services. Real time access to data from active studies.
Rapid incorporation of a broad array of data from data platforms such as microbiome, PRO, home health devices. CFR 11 validation of
translational analysis tools for use in active studies. Broad establishment of enterprise data driven culture within organization. Advanced
rapid access to data visualizations.
Level 5
External innovation and validation/optimization: Extensive automated data exchange, broad data access contracting. Collaboration cloud
with pre-competitive partners including AMCs, patient advocacy, peers, and commercial data providers. Execution of complex pipelines
across multiple modes of data e.g. mRNA and NGS and literature. Federated queries across multiple institutions and modes to answer key
questions. Collaborative environments with shared users and identity management and social networking. Automated tiered storage and
compute to manage very large data sets and reanalysis pipelines. Use of semantic web tools to expose resources. Common internal and
external tools and approaches such as OMOP.
Level 4
Business focused solutions: Differentiated solutions by business area such as health economics, safety, research, operations, marker
discovery, lab/sample availability, competitive analysis, pre-clinical, etc. Demonstrated and published results driving key business decisions
achieved from enterprise informatics frameworks. Integration of translational research informatics with multiple enterprise systems such as
portfolio management. Significant curated library by use containing clinical studies and associated open/public data. Secure web service API
access to data. Standard and shared algorithms and methods across disparate internal teams. Access to broad array of real world evidence
sources e.g. Twitter, adverse events, surveillance partnerships.
Level 3
Enterprise utilization and standardization: Focus on use of data for decision making in major R&D cycle decisions. Documented governance
of use of data, quality processes for data, and internal/external sharing. Semantic translation of studies into common formats. Cross study
and multiple platform analysis enablement through integration of analytic pipelines and advanced standardization. Central informatics
framework for interfacing to multiple commercial, open, and internally developed research platforms. Policy based self service access to
data. Factory model and self-service curation. Acquired data sets from subscriptions converted into standard formats or repository system.
Level 2
Data integration – data warehouse: Centralization of translational research data sets in single DBMS repository. Data includes clinical
studies, molecular assays, observational studies, 3rd party data. Linkage at patient level across data and between data and analyses. Access
controlled by ad-hoc governance model with honest broker or service delivery focus on analyses on an as needed basis to share data across
groups. Self service access via web for browsing and exploring data including basic analyses. Focused pilots engage early adopter users.
Level 1
Fragmented and siloed analyses: Silo approach to clinical data controlled through experts such as biostatistics groups. Data stored in primary
forms such as SAS data sets and files in organized directories. Analyses produced are ad-hoc with specific tools. Internal development of
systems to offer intranet or file server access to data files beyond. Recognition of governance needs. Subscription services manage reference
data or to search external data. Basic catalog available through files or experts. Desktop analysis tools primary interface to data.
Level 0
Reliance on external vendors: Historical focus on clinical only data sets with no ‘Omics and data integration internally. External vendors
exclusively generate analyses for combined clinical and molecular data. Infrastructure for storage is file servers with limited governance and
generally report focus. Limited to no institutional knowledge of available data sets from historical work.

13. Deloitte Health Miner Capabilities
Outcomes
Miner
Population
Miner
Precision
Miner
•
Visually explore populations •
Propensity matched subsets
•
Omics analysis
•
View temporal relationships •
Identify advanced correlations
•
Transmart++
•
Select cohorts to analyze
•
Compare treatment effectiveness •
•
Identify basic correlations
•
Access curated data sets
•
Data delivery pipelines
•
Large population data sets
•
Subscription access to reports
•
Research data warehouse
Analysis archive
Recombinant Platform
•
Data consortia & licensed data sets
•
Cloud and on premise deployment tools
•
Data integration, cleansing, enrichment tools
•
Commercial open source support
•
Data models and analytics frameworks
•
Informatics and statistical models
13
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.

14. Translational Research Platform
Research Portal
Precision Miner
Application Layer
(Miner)
Study
Design
Study
Recruitment
Manager
Cohort
Identification
Population
Miner
Outcomes
Miner
i2b2
In Memory
Exploration
Compare
tranSMART+
Patient
Journey
Security and Identity Management
Cohort Matching
Business and
Analytical
Services
Data Management, Storage
and Processing Engine
Safety
‘Omic
Explorer
Metrics Calculation
RIE Services
Data/Messaging
APIs
Clinical/Omics
Terminology
Mapping
Statistical Model
Execution
Knowledge
Management
Data Trust (DT)
Research Trust
Data Marts (ADM,
Research Mart,
CFDM, OMOP, i2b2)
Master Patient
Indexing
‘Omic Data
Management
OADM DT
Extensions
Data Processing
Pipelines
Data Integration
Data Integration
Hub (DIH)
Data De-ID/Re-ID
Services
Data Acquisition
Custom ETL
Packaged
Parsers/Adapters
Primary Sources
Research
Datasets
EMR/Clinical
Clinical Trials
Metadata/
Terminology
Services

15. Real world evidence?

16. Safety Solution Vision (Example)
Reporting & Analytics
Safety & RWE Platform
Safety Reports
Safety
Case
Reports
Query Interface
Argus DB
High Quality Real
World Data/Analytics
from Collaborators
Safety DW
(internal)
Analytics
Export to SAS,
Excel
Reports
RWE
Population Miner
OMOP
Purchased Real World
Data and Federation
RWE
Data Trust
i2b2
Others
Randomized Clinical
Trial (RCT) and L4
Data
Research
Trust
tranSMART
Precision
Miner
Reports
OMOP Analytics
Internal Analytics
Population
Stratification
Inventory of Data
Assets
RWE Portal
Outcomes Miner
Cross Study
„Omics Analysis
Social Media
DW
Signal Detection
Sentiment
Complaints
16
Copyright © 2013 Deloitte Development LLC. All rights reserved.

17. Teaming to Enable Data Driven Healthcare Improvement
- President Obama to the AMA
June 15, 2009
Chicago, Illinois
Lowering cost
through quality
improvement
Quality
“We need to build on the examples of
outstanding medicine at places like ...
Intermountain Health in Salt Lake
City, where high-quality care is being
provided at a cost well below average.
These are islands of excellence that we
need to make the standard in our
healthcare system.”
Cost
• Decrease variation in clinical processes
• Measure the processes through analytics
• Measure, adjust, measure, adjust…
“We selected Deloitte as the best partner to translate
Intermountain’s pioneering work to other systems. The use
of our technologies will allow clinicians and researchers to
more quickly discover practices to help usher in a new wave
of innovation throughout the nation’s health systems.”
- Marc Probst, CIO Intermountain Healthcare
Intermountain is the initial member of what will be a Consortium of preeminent health
systems across therapeutic areas and from around the world
17
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.

18. Leukemia
• Cancer staging
• Chemo /
radiation
therapy
• Genetic
biomarkers
•
•
•
•
Breast Cancer
Tumor data
Cancer staging
Chemo / radiation
therapy
Genetic
biomarkers
Chronic
Respiratory
Diseases
• Pulmonary
Function Test
• Respiratory
Rate
Medication
(prescription
and
adherence)
Vitals
Renal Diseases
• Glomerular
Filtration Rate
• Creatinine
levels
Infectious
Diseases
• Lab results
• Microbiology
results
18
Patient
Encounters
Treatment
procedures
(medical
and
surgical)
Diabetes
Mellitus
• HbA1c
• Blood glucose
levels
Patient
Demographic
(e.g., Age, Gen
der, Ethnicity)
• Approximately 2.1
million patients
• 137,881,670
diagnoses
• > 10 years
longitudinal data set
• At least 2 years
visibility for all
patients
Clinical
Diagnosis
and
Symptoms
Alzheimer’s
Disease
• Cognitive
scores
• EEG results
• Genetic data
•
•
•
•
Heart
Failure
Echo data
Staging of CHF
EKG
Stress test
Lifestyle
Parameters
(e.g., Smoking,
Body Mass
Index)
Lab Results
(numerical
values and
text
information)
Mortality data
(with primary /
secondary
causes)
Rheumatoid
Arthritis
• Bone Mineral
Density
• Fracture Risk
• Biologics use
Hypertension
• Blood
pressure
• EKG
• Cardiac
status
Ischemic Heart
Disease
• Pulse
oximetry
• Cath lab data
• Inpatient
activity
Osteoporosis
• Bone Mineral
Density
• Fracture Risk
• Menstrual and
HRT status
Available
Intermountain Data
Copyright © 2013 Deloitte Development LLC. All rights reserved.

19. Smart Data Approach to the Deloitte-Intermountain consortium
Deloitte is focused on providing life sciences companies with the deepest insights from “near real time” medical record
data in the world through:
 Analytics based upon Intermountain (in the future other consortium health data) systems; ~200 systems covering the
entire patient clinical experience
 Insights from leading health systems who are mastering the care processes
 Innovative business model to facilitate rapid learning with collaborative research with systems including Intermountain
health care
Analytics Platform
Deloitte has jointly developed an analytic platform
with Intermountain Healthcare’s Homer Warner
Center
Analytic Platform
2
Analytic
Platform
3
Analytics Provisioning
DHI Provides Analytic Results to Customers
Illustrative
Visualizations
& Tools
Customer
Deloitte is
implementing the
analytics platform
at provider
organizations
participating in a
consortium and
providing their
EHR / EDW data
DHI Portal
Data Providers
EHR / EDW
Analytic
Platform
EHR / EDW
Analytic
Platform
Access Via Subscription
1
Study of patient
outcomes for selected
therapeutic area (TA)
EHR / EDW
Business Model:
Provider Partners
 Produce analytic results
 Help generate analytics
algorithms for new products
 Conduct follow-on studies
19
Data/Technology
Custom Studies
Deloitte as a Service
Provider
 Aggregate analytic results
 Deliver content to customers
 Broker follow-on studies
Subscription &
Custom Study Fees
Customers
Custom
Informatics & Insights
On-Demand
 Access the results
through subscription
portal
Copyright © 2013 Deloitte Development LLC. All rights reserved.

20. Subscription Portal
RWE Reports from RWE Data Sets
Safety DW Report
20
Report based on
purchased claims
Report summarizing social
media analytics
Copyright © 2013 Deloitte Development LLC. All rights reserved.

21. Demos
Population Miner
Outcomes Miner
21
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.

22. Development of a learning loop that leverages RWE and
the experience of healthcare providers
Evaluating
Evidence
from Studies
Implement
Learning
Focused
Studies to
Generate
New
Evidence
22
Validating
Evidence in
Real World
Collaborate
to Develop
New Insights
Copyright © 2013 Deloitte Development LLC. All rights reserved.

23. Vision: Leveraging tranSMART + workbench to identify
insights from existing study
 Using Deloitte‟s translational research tools suite of tools for
evaluating current studies
Evaluating
Evidence from
Studies
Implementatio
n of Learnings
Focused
Studies to
Generate New
Evidence
Validating
Evidence in
Real World
Collaborate to
Develop New
Insights
 Studying phenotypic and genotypic profile of patients
participating in a recent Asthma study
 Variants of PDE4 gene and CYP 450 gene indicate variation in
outcomes (however not statistically significant)
Step 1: Viewing at insights into a single research
study, specifically, a box plot of a gene signature list
against all participants in an asthma study who have
genomic data loaded. This shows us large variants in
two distinct subgroups (Type I and Type IV)
23
Step 2: Heatmap view limiting our selections to those
subgroups showing the variance in genetic markers. It
shows variations, but they are not as significant to
generalize insights
Copyright © 2013 Deloitte Development LLC. All rights reserved.

24. Vision: Pooled analysis of asthma studies to identify
impact of genomics on treatment outcomes
 Performing a pooled analysis of “multiple studies” across
various asthma studies
Evaluating
Evidence from
Studies
Implementatio
n of Learnings
Focused
Studies to
Generate New
Evidence
Validating
Evidence in
Real World
Collaborate to
Develop New
Insights
 Larger sample size enables studying phenotypic and genotypic
profile of patients with greater confidence
 Analysis indicates variants of PDE4 gene and CYP 450 gene
showing significant variation in outcomes for certain treatments
Step 3: Now we perform a comparison of multiple
different study groups to observe first the phenotypic
differences (Age, Sex, etc.) and then compare the
specific variances of two gene variants between the
study groups
24
Step 4: Heatmap view now indicates significant
difference in terms of how the genetic variations are
impacting the outcomes of treatments
Copyright © 2013 Deloitte Development LLC. All rights reserved.

25. Vision: Overview of Asthma patients in real-world to
enable better characterization of disease
 Overview of all the asthma patients treated in the real-world
setting in the past decade
Evaluating
Evidence from
Studies
Implementatio
n of Learnings
Focused
Studies to
Generate New
Evidence
Validating
Evidence in
Real World
Collaborate to
Develop New
Insights
 Evaluation of current treatment paradigms in the real-world and
correlation with outcomes
 Identification of two key treatments medications that are the
cornerstone of treatment for further evaluation
Step 5: Evaluating all the patients having ‘Asthma’ at
Intermountain Healthcare to identify
age, gender, disease frequency distribution.
Identification of treatment, lifestyle, ethnicity and
comorbidity patterns for the patients
25
Step 6: Ability to identify two most common
medications used in patients with severe asthma
condition for further evaluation using Outcomes Miner
Copyright © 2013 Deloitte Development LLC. All rights reserved.

26. Vision: Evaluation of outcomes for two asthma
medications in real-world setting
 Comparison of patients on „Drug A‟ versus „Drug B‟ to identify
difference in outcomes
Evaluating
Evidence from
Studies
Implementatio
n of Learnings
Focused
Studies to
Generate New
Evidence
Validating
Evidence in
Real World
Collaborate to
Develop New
Insights
 Evaluating „Emergency Visits‟ as an outcomes and then
filtering them by „Emergency Visits specific to Asthma‟
 Patients with CHF as comorbidity and treatment with Betablocker treatment indicate higher Emergency Visits
Step 7: Evaluating ‘Emergency Room Visits’ as the
outcome on the dashboard, overall more Emergency
Room Visits for Asthma patients with CHF disease as
comorbidity
26
Step 8: Evaluating ‘Emergency Room Visits for
Asthma’ as the outcome, high Emergency Room Visits
for patients with beta blocker treatment for CHF
Copyright © 2013 Deloitte Development LLC. All rights reserved.

27. Vision: Evaluation of outcomes for two asthma
medications in real-world setting (contd …)
Evaluating
Evidence from
Studies
Implementatio
n of Learnings
Focused
Studies to
Generate New
Evidence
Validating
Evidence in
Real World
 Comparison indicates patients on „Drug A‟
depression, neurological conditions and psychosis have high
degree of correlation with Emergency Visits
 CHF and Beta Blockers believed to have an association with
CYP 450 gene variants
Collaborate to
Develop New
Insights
 Indicates the need to further study impact of CYP 450 genes in
drug outcomes
Step 9: Evaluating ‘Emergency Room Visits for
Asthma’ as the outcome for patients on Drug A shows
same degree of correlation with
depression, neurological conditions and psychosis
27
Step 10: Evaluating ‘Emergency Room Visits for
Asthma’ as the outcome for patients on Drug B shows
limited correlation with depression, neurological
conditions and psychosis
Copyright © 2013 Deloitte Development LLC. All rights reserved.

28. Knowing more about i2b2
data?

29. Big picture type solution for ‘AMC’ genomics initiatives
Closed Loop
Data Warehouse /
Research Stores
Source Data
Clinical EMRs
& Claims
Data Workflow/
Enhancement
RI Analytics & Care Delivery
Translational Research
Applications
Data Trust
Extended Systems
Clinical
Partner
Clinical data
Research Trust
Labs
Clinical Trials,
Registries,
Internal/External
Results
Data
Curation
Honest Broker
Data Pipeline
Omics/Cohort
Explorer
Research Open Source
Research
i2b2
Data DeIdentification
tranSMART/
Sample Explorer
Omics
File Store
e.g. genomics (BAM, VCF, CEL)
Publications, PDF, Pathology
Biobanks
LIMS
Statistical Analysis
‘Omics
Platforms
(CLC Bio)
Master Data Management
MPI/Provider
Common
Services
Research
Data Marts
Research
Information
Exchange
Research Portal
ETL
Study
Recruitment
Manager
Security
MPI
Scientific
Reference
R
SPSS
SAS
Terminology
Reference
Ref Data Mgmt Hub
Collaboration
*Note: Representative diagram – not all integrations are shown
- 29 -
HPC
Portal
Storage

30. Representative View: Select Cohorts via i2b2 Query & Analysis interface
- 30 -

31. Representative View: i2b2 passport profiles available data
- 31 -

32. Representative View: i2b2 Passport, cont.
- 32 -

33. Representative View: i2b2 Passport – summary of data over time
- 33 -

34. Automated research request data mart production system
- 34 -

35. Getting AMC registry data
into i2b2
(for tranSMART)
Harvard/CHiP
Jonathan Bickel M.D., M.S., FAAP

36. REDCap Study Representation
- 36 -

37. XML to i2b2
REDCap
Archive
(ODM XML)
i2b2
Staging
File system
Oracle Schema
• Ontology
• CRC
EDC
system
of
choice
- 37 -
i2b2
PRD

38. Choose your Stud(ies)
• Choose
studies to be
imported
• Supply token
to be used
for study
• Click to
initiate export
- 38 -

39. Choose your Stud(ies)
• If a project that
has been
previously
exported is
selected, the
export process
begins by
cleaning out all
references to the
project from the
i2b2 staging
database.
- 39 -

40. CDISC ODM XML
<?xml version="1.0" encoding="UTF-8" standalone="yes" ?>
<ODM ODMVersion="1.3.1"
CreationDateTime="2012-02-03T10:59:14.175-05:00"
FileOID="000-000-000"
FileType="Transactional"
xmlns:ns2=http://www.w3.org/2000/09/xmldsig#
xmlns="http://www.cdisc.org/ns/odm/v1.3">
<Study OID="10">
<GlobalVariables>
</GlobalVariables>
<BasicDefinitions />
<MetaDataVersion Name="Version 1.3.1" OID="v1.3.1">
{YOUR METADATA HERE}
</MetaDataVersion>
</Study>
<ClinicalData MetaDataVersionOID="v1.3.1" StudyOID="10">
{YOUR STUDY DATA HERE}
</ClinicalData>
</ODM>
- 40 -

41. REDCap Study Representation
- 41 -

42. Modifiers POC (Kimmel Cancer Center)
Informatics Core Director, KCC, TJU Director of
Research Informatics, and Research Professor
of Cancer Biology
Dr Jack London
Informaticist, KCC Informatics Shared Resource
Devjani Chatterjee, PhD
Kimmel Cancer Center Deputy Director for Basic
Science and Professor of Cancer Biology
Karen Knudsen, PhD
Assistant Professor Medical Oncology
Hushan Yang, PhD
Professor, Cancer Biology
Hallgeir Rui, MD, PhD
Vice President and CIO
Stephen Tranquillo

43. Jefferson Kimmel Cancer Center - i2b2 Ontology
© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
- 43 -

44. Jefferson Kimmel Cancer Center
i2b2 v1.6 Biospecimen Ontology
de-identified case
ID
Specimen class
(solid
tissue, fluid, serum
, etc.)
Specimen type
(frozen or paraffin)
de-identified specimen
ID
Pathologic status (normal or malignant)
© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
- 44 -

45. Jefferson Kimmel Cancer Center
i2b2 v1.6 Tumor Registry Ontology
Tumor identifier modifier links
different facts about the same
tumor.
Changing these facts from concepts to
modifiers allows multiple occurrences of
the same fact for the same individual to
be associated with the correct
corresponding tumor.
© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
- 45 -

46. Jefferson Kimmel Cancer Center
i2b2 v1.6 Genomic Profile Ontology
Results for a 58 gene assay panel.
chromosome
number
mutation classification
© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
- 46 -

47. Jefferson Kimmel Cancer Center tranSMART (prototype 1)
© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
- 47 -

48. The FDA needed to explore new approaches to data management and analysis for
effective evaluation of product safety and efficacy
Business Problem
Ideal Effort
Current Effort
The FDA has committed to
improving their overall submission
review process
•
Resources were spending too
much time on basic tasks to
aggregate data across clinical trials
•
As a result, fewer resources were
available for high-value data and
regulatory analysis
Effort
•
Review Activities
Data
Curation &
Loading
Data
Selection
Data Management
Data
Analysis
Innovation
Learning
Sharing
Regulatory Science
Strategic Goals
•
•
•
Implement improved data management systems across the following multiple FDA Centers
Enable the ability to:
• Automate the process of loading clinical trial data from multiple source formats
• Correlate data across clinical trials through a simple and intuitive user interface
• Conduct advanced analytics across multiple data sets to better inform regulatory decisions
Shift the utilization of resources from basic data management to high-value regulatory science
- 48 -

49. tranSMART 1.2 prep

50. Demo tranSMART Modifiers
Integrated faceted GWAS results
Cross trials
Query by ‘sequence’
Workspace ‘save’
© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
- 50 -

51. Switch data center co-locates multiple hosting options for Life Sciences
Deloitte – Internal Users
Deloitte – SaaS Solutions
Deloitte – Client Hosting
Cloud and Hosting Eco-System
Amazon
• Open burstable
compute
• Client managed
cloud purchases
• Connectivity
• Low
cost/commodity on
demand
Bluelock
Client Hosting
• Approved for
PII, PHA, HIPPA
• High Performance
• Metered
applications
• Subscription data
• Cloud provisioning
of Deloitte managed
burstable resources
• Traditional Hosting
• Local sensitive
data
Switch
- 51 -

52. Disclaimer: This publication contains general information only, and none of the member firms of Deloitte Touche Tohmatsu Limited, its member firms, or their related
entities (collective, the “Deloitte Network”) is, by means of this publication, rendering professional advice or services. Before making any decision or taking any action
that may affect your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever
sustained by any person who relies on this publication.
Definition footnote: As used in this document, "Deloitte" means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a
detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of
public accounting.
Copyright notice: Copyright © 2013 Deloitte Development LLC. All rights reserved.
Member of Deloitte Touche Tohmatsu Limited
Copyright © 2013 Deloitte Development LLC. All rights reserved.