Karim Damji, SVP of Product and Marketing, presented at the Bridging Clinical Research and Clinical Health Care conference held at the Gaylord in National Harbor on April 4-5, 2018.

1. Bridging Health
Care and Clinical
Trial Data through
Technology
Karim Damji
SVP, Product and Marketing
Saama Technologies

2. About Saama
AI and machine augmented analytics company delivering
“Business Outcomes” for life sciences

3. 5
Our People First technology solutions embrace data
Standards, security and bring novel user and
conversational experiences
Facts

4.  My predictions
 Life Science Analytics Cloud – an integrated experience
for clinical development
 User experience philosophy
 How Artificial intelligence fits into all this?
Agenda

5. My Predictions 2018 – 2020:
• Patient Engagement and Patient Centricity will be
digital transformations with IoT and AI
• Robotic Process Automation (RPA) will change the
• Life Sciences/Healthcare industry will master AI
Learning
• Pharma will embrace cloud (more) – with
• CROs will expand beyond Clinical Operations to
solution offerings
• AI will drive new user experiences with data
• Blockchain POCs for data-share

7. Life Science
Analytics Cloud
Study Design, Planning
and Execution
Enabled by an Integrated
Experience

8. 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

9. 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
Collage & 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

10. Can we look at this
with a new lens?

11. Life Science Analytics Cloud Assets – Digital
Transformation through Orchestration
Clinical Development
Optimizer
Trial Planning
Optimizer
Market Analyzer Patient Pathways
Journey of a Drug Via Clinical Trial
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Planning Start-up Study Conduct
Cohort Builder
Delivers Clinical Data Analytics and Management as a Service (CDaaS) and the implementation of
Operational Data Repositories (ODR) and Patient Data Repositories (PDR)
Data Manager

12. Data-as-a-Service
A Scalable Engine to
Collect, Standardize and
Analyze Clinical Data

13. Cloud-Based Clinical Data-as-a-Service
Clinical Data
Safety Data
Syndicated & Large Data
Other Data (IoT)
Data Sources Enabled Analytics
Patient & Studies
Analytics
❖ Clinical Study
Data Mart
❖ Clinical
Outcomes
Analytics
Drug Safety &
Analytics
❖ Safety Outcome &
Reporting
Analytics
❖ Signal Detection
Real World
Analytics
Risk Based
Monitoring
❖ Electronic Data Capture
❖ Clinical Trials
Management System
❖ CRO Data
❖ Labs / Biomarker
❖ Safety Data Warehouse
❖ Global Safety Data
Warehouse
❖ ARGUS / ARISg
❖ Real World Claims
❖ EMR / EHR
❖ Omics Data
❖ Public Data (Kegg, NCBI, and,etc.)
❖ Trials Trove, CT.gov
❖ Social Media
❖ IOT/ Sensor/ Wearables
❖ Trial
Management
Analytics
❖ Non- Clinical & Pre-Clinical

14. Centralize (and Contextualize) Data in Flight

15. Workflows are Centralized, Simplified and Self
Service

16. Data Quality can be Managed within the Solution

17. Visual
Experiences with
Life Science
Analytics Cloud

19. Life Science Analytics Cloud
Clinical Development
Optimizer
Trial Planning
Optimizer
Market Analyzer Patient Pathways
Journey of a Drug Via Clinical Trial
Discovery Phase 1 Phase 2 Phase 3 Approval
Post-
Approval
Planning Start-up Study Conduct
Cohort Builder
Delivers Clinical Data Analytics and Management as a Service (CDaaS) and the implementation of
Operational Data Repositories (ODR) and Patient Data Repositories (PDR)
Data Manager

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

22. 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

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

24. User Experience at Portfolio, Study, Countries,
Region, etc.

26. S
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y
I
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y
I
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29. The Future
Speak to Me (Smart) and Tell
us the Future (Deep Learning)

30. UX CX
Impersonal Dashboard to Personalized Analytics

31. “Smart Assistant” for Clinical Operations

33. Leveraging AI to Address Feasibility and Drive New
Experiences
Problem Status
ADE Extraction Paper Published
Patient Matching Work in Progress
PHI Scrubber Work in Progress
Virtual Assistant Framework Ready
Inclusion/Exclusion criteria Recommender Ready
Inclusion/Exclusion criteria search Ready

34. An Attentive Sequence Model for
Adverse Drug Event Extraction
from Biomedical Text

35. Messaging Platforms

36. Unstructured
Data
Data Transformation
Pig
Unstructured
Data
Visualization
Unstr
ucture
d Data
Unstructured
Data
Structured
Data
Syndicated
Data
Transactional
System
Social Media
Data
Streaming Data
API
Source
Data
Unstructured Data
Operating Systems
Windows Linux
On - Premise
Cloud
AWS MS AZUREGoogle Cloud
Hadoop Distributions
Amazon EMR
Unstructured
Data
Connectors
API
JDBC
SDK
Unstructured
Data
Data Science
Data Profiler, Rule Engine
Unstructured
Data
Rules Repo,
Workbench Repo
Postgresql
Unstructured
Data
Security
Authentication
LDAP
Kerberos
SAML
Authorization
RBAC
ACL(Service Level, Directory
Level, Column Level)
Encryption SSL
Transparent
Encryption
Encryption Zones
AES 256
Unstructured
Data
Data Governance
(Hortonworks Stack)
(Cloudera Stack)
Unstructured
Data
Aggregated Entities
Impala
(Cloudera &
MapR Stack)
(Hortonworks
Stack)
Unstructured
Data
Common Data Model
(Hortonworks
Stack)
Impala
(Cloudera &
MapR Stack)
Unstructured
Data
Search
(Default)
Unstructured
Data
Change Data Capture
Technology Reference Architecture
Unstructured
Data
Landing Zone Life Science Analytics Cloud
+
CDaaS

38. 41

39. Thank You