Leon Surgeon spoke at CBI's Direct to Patient conference in August 2018 in Philadelphia about how technology can be used in practical applications to improve clinical trial performance.

1. Copyright © 2018, Saama Technologies
Delivering Analytics Advantage
to the Global 2000
Gain Cross-Industry Insights to Uncover
Improvements and Optimize Trial Performance

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Copyright © 2018, Saama Technologies
About Saama
AI and machine augmented clinical analytics platform company delivering “Business
Outcomes” for life sciences

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Copyright © 2018, Saama Technologies
$30
Million
$3
Billion
History
$300
Million
Clinical trial costs goes down exponentially over the next
decade with AI & Technology
Block chain
Patient Data
Drug Development Costs

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Copyright © 2018, Saama Technologies
What Do We Do?
𝑒
−
𝑑𝑣
𝑑𝑡
∙ min 𝑐𝑙𝑖𝑛𝑖𝑐𝑎𝑙 𝑡𝑟𝑖𝑎𝑙𝑠

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Top Clinical Trial Cost Drivers
Patient Retention
2%
Patient Recruitment
32%
Vendor Fees
25%
Site Recruitment
14%
CTMS, EDC (system
spend)
12%
Site Retention
8%
Data Management &
Validation
7%

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Clinical Development: Challenges
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Planning Start-up Study Conduct
Protocol to DB Lock ~1070 DaysProtocol to FPI 145 Days

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Clinical Development: Data Challenges
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Intake
Real-time data ingestion
Connected data integration
Standardized data collection
Data variety, volume, etc.
Process & Use
Data Management, Quality, Queries
Conform to Standards
Coding
Adhere to process
Collate & Submit
Initial, Final Submission Data
Data Base Lock
Clinical Study Report
Monitor & Report
Valid Safety Signals
Health Economics @ Scale
CTMS, EDC, IRT, TMF... Argus, Real WorldFiles/DBs...
Planning Start-up Study Conduct

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Gold Rush of Insights
+ Financials, Interest, Hobbies, Activities, Media Consumption, Buying & Shopping Activity
Lab Results
Gold Rush
Of Insights
Procedures
Diagnosis
Drug Usage
Physician Specialty
Demographics
Medical History
Geo Locations
Facility Type
Social Determinants
Patient Level

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Entity Extraction and Patient Matching for EHR Data

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AI can Solve the Biggest Clinical Operations Challenges

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Planning Start-up Study Conduct
AI Enabled Data
Management Technologies
Governance: Harmonization: Orchestration:
Data Models: Clinical KPIs
Current and
Future Data
Source
Agnostic
Trial Feasibility
Site, Patient &
PI Identification
Portfolio Performance
Analysis, Trial Risk Mitigation
Market Share
Performance
Patient Journey
Treatment
Evaluation
Cohort
Development
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Planning Start-up Study Conduct
Journey of a Drug Via Clinical Trial

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Strategies and User Experiences
Permit search across disparate data-sources to find information of interest
Promote collaboration & adoption through delightful user experiences
Allow for flexible & novel queries method

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Centralize (and Contextualize) Data in Flight

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Workflows are Centralized, Simplified, and Self Service

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Data Quality can be Managed within the Solution

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Save Cohort Export to Treatment
Pathways or other
Saama apps
Cross-functional Teams use Cohorts for Downstream Analysis in
other Modules and/or Applications
Several Export Options –
SQL, CSV, Cohort

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Asking the Right Questions to Evaluate the Feasibility of a Protocol
Define target #
subjects for study
Toggle Inclusion/Exclusion Criteria on/off to
refine Protocol, maximize enrollment
potential, and not sacrifice study efficacy
See geographic
distribution of potential
patients

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Investigator and Site Selection Feasibility
See clusters of
potential
patients
Define target #
Principal
Investigators

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UX CX
Impersonal Dashboard to Personalized Analytics

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“Smart Assistant” for Clinical Operations - DaLIA

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Parting Thoughts
• Have a clear and concise strategy for why your organization needs enterprise
data and AI services
• Take a practical approach to solving the right problems now!
• Your infrastructure needs to be ready to support the data needs of the future

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“The best way to predict the
future is to create it.”
– Peter Drucker

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