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Virtualization of R&D Driving New Health IT Requirements
Vijay Pillai, Director of Translational Medicine & Strategic Planning
Presentation for The TRUST Autumn 2011 Conference
November 2, 2011
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2
Health IT – What comes to mind?
CONSENT
PRIVACY
HIPAA
PERSONAL
HEALTH
INFO (PHI)
GOVERNANCE
ELECTRONIC
MEDICAL
RECORD
PERSONAL
HEALTH
RECORD
SECURITY
ENCRYPTION
DE-IDENTIFY
DOUBLE
BLINDING
Does this make you feel more secure?
Electronic Medical Records are here to
stay … but lot more to come
Employers
Pharma/
Medical Device
Epidemiology
Standards
Hospitals
Insurance
Pharmacy
Laboratory
Physician
Office
A reality today … inside some hospitals
A peek into the future
The “Big Data” challenge in healthcare is
not very well known
Medical Images –
Clinical Records –
10s of Terabytes to
1s of Petabytes
100s of Gigabytes to
1s of Terabytes
Security/Privacy
Standards maturing
Security/Privacy
Standards well understood
for structured data
Genomic Data –
10s of Petabytes to
100s of Petabytes
Security/Privacy
Standards evolving
Bringing scale to research – Outsourced,
integrating data and workflows
• Proactive
consenting
• Sample data
access
• Testing
• Data management
• Analytics/insights
PATIENTS
Data &
Results
PATIENT / DISEASE
STRATIFICATION
Clinical
Data
Sample
Info
SAMPLE
COLLECTION
Translational
Research
Center
Data &
Results
Best-Fit Treatment
Options Report
Sample
Volumes
NEW ADMITS
ANALYTICS
OMICs
Data
BIOREPOSITORY
LABORATORY
7
Virtualization in R&D driving opportunities
for security and cloud infrastructures
CRO
Academia,
CRO &
Sponsor
Basic Research
Sponsor
Healthcare
Discovery &
Development
Healthcare
Sponsor
Point of Care
Academia
CRO
Source: adapted from DataMonitor
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The collaboration in the cloud
SOURCE
(Healthcare Providers)
ENABLER
(Enables Collaboration
between Sponsors and Providers)
Hospital
Networks
Pharma
Medical
Device
Academic
Medical Center
(AMC)
R&D
APPLICATIONS
AMC
CONSUMER
(Pharma, MedDev,
CROs, Payers, Govt.)
Biotech
AMC
Pharma
Hospital
Biotech
Medical Device
Branded Networks
Contract Research
Organization (CRO)
A collaborative network
9
Architecture considerations for the cloud
•
•
•
•
•
•
Data ownership
Access provisioning
Bound by HIPAA
Distributed architecture
De-identification/Double-blinding
“Push” technologies
SOURCE
•
•
•
•
•
•
Encrypted transport layer
Data Layer (Clinical, Image, OMICs)
Metadata management
3rd party applications security
Infrastructure to support M-to-M
Data segregation – No co-mingling
ENABLER
Hospital
Networks
•
•
•
•
•
Queries will run on cloud
No detailed row level access
Summary level
Bound by 21CFR Part 11 regulation
Workflow initiates provider alerts
CONSUMER
Pharma
Medical
Device
Academic
Medical Center
(AMC)
Biotech
AMC
AMC
Pharma
Hospital
Biotech
Medical Device
Branded Networks
Contract Research
Organization (CRO)
Health Sciences Cloud
10
Yin Yang of health sciences
PRIVACY
COLLBORATE
Seemingly contrary forces are interconnected and interdependent
Make it possible by strong security and governance processes
Use of a Common Technology Platform
Need for Common Data Model and Transformation Services
ALL HOSPITALS
CLINCAL
IMAGING
Portals
HOW ?
LABORATORY
AMC / LS
OMICS
CLINICAL TRIALS
BIOBANK
Translational Research
OPERATIONIONS
12
Clinical Research Fundamentals
Common Healthcare Data Model and Transformation Services
Master Data Management
APPLICATIONS
Healthcare
Data Model
IMAGING
Data
Integration
LABORATORY
OMICs Data
Model
RULES
SECURITY
ALL HOSPITALS
CLINCAL
COHORT ID
BIOMARKER ANALYTICS
PARTNER APPLICATIONS
RULE ENG
AMC / LS
OMICS
CLINICAL TRIALS
BIOBANK
OPERATIONIONS
Statistical Computing Environment
ETS
EMPI
TM
DI
OTHER
ETS
EMPI
TM
DI
OTHER
TRANSFORMATION SERVICES
13
Clouds enable people, applications, and
data to collaborate and develop insights
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BioPharma
Diagnostics
Medical
Devices
Healthcare
Payers
Healthcare
Providers
Government
Payers
Regulatory
Agencies
Patients &
Families
Open, standards based, flexible platforms that connect domain experts,
data and applications in a regulatory-compliant framework
14
Taking algorithms to the data
Need a powerful database with all capabilities embedded
– Regular Expression, Semantic Web (RDF)
– Naïve Bayes, ABN, SVM, NMF, K-Means, Decision Tree
– Statistical capabilities
– XML with table like functionality, Xquery/SQL
– Consolidate spreadsheets and more; rapid web apps
– Multi-Dimensional Cubes, drill-down
– Manipulate images, Native support for DICOM
– Store documents & access from anywhere/any platform
Appl.
Express
– Virtual private databases
– Advanced security
– Advanced compression including hybrid columnar
compression
– In-memory analytics
– Database machines: Exadata
Secure Files
Q&A