Enabling Better Clinical Operations through a Clinical Operations Store

Copyright © 2016, Saama Technologies | Confidential
Enabling Better Clinical Operations through a
Clinical Operations Store
Srinivasan Anandakumar
Senior Director, Clinical Analytics Innovation
Big Data and Analytics in Pharma

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Copyright © 2017, Saama Technologies
Data to Insights
2

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Clinical Analytics –Platform Components

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Business Use Cases
Analytics Pathway
Study
management
Metrics
Site Performance
Risk Based
Management
Data Management
Metrics
Risk Based
Management
Monitoring
Metrics
Study Data
Integrity
Collected Data
Management
Trial Safety &
Efficacy Analysis
Submission
Datasets
Management
Submission Pathway

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Clinical Analytics Platform Components
ODS
Auto Ingestion DQ Workbench
Conformance
Workbench
KPI
Library
PDS
Auto Ingestion
Metadata
Workbench
Transformation
Library
Modeling
Work Bench
Operational Data Patient Data
EDC CTMS IVRS
ERP eTMF
EDC LAB CRO
MDR SCE
KPI Rules
Workbench
Workflow
Library
Reports
Library
Exploratory
Workbench
Visualization
Workbench
Data
Platform
Source
Systems
User
Interaction
Platform
Integration
Workbench
Integration
Workbench

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ODS Roadmap
ODR
Foundational
ODR – Extended
Analytics
ODR –
Predictive
Analytics
EDC CTMS
IVRS
• Study Start up
• Site Activation
• Data Management
• Study Monitoring
• Patient Management
• Site Management
• CRO Management
• Study Management
• Site Feasibility
• Investigator Selection
• Competitor Intelligence
• Metrics Benchmarking
• Metrics Forecasting
• Study Financials
• Study Document
Management
• Clinical Trial Manufacturing
• Clinical Trial Supplies
• Portfolio and Project
Management
• Patient Sample Management
eTMF ERP
Trial
Financials
External
Trial Data
CRO
Clinical
Supplies
External
Population
Data

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PDS Roadmap
PDR -
Foundational
PDR – Extended
Analytics
PDR – Predictive
Analytics
EDC
LAB
• EDC Data
Acquisition
• Third Party Data
Acquisition
• Trial Data Pre-processing
• Standardization Metadata
Management
• Standardization
and Derivations
• Patient Data – Predictive
Models• Snapshot
Management
• Study Pooling
• Data Mart Up Versioning
• Data Cut-off
• Analysis Models Creation –
Outcomes and Safety
CRO
EDC
LAB CRO
ODR Coding
MDR
MDR
EDC
LAB CRO
SCE
COA
External Patient
Data

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Operational Data Stream (ODS) – Building Components

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Operational Data Stream - Functional and Technical Blocks
CTMS
EDC
Flatfiles
MDM
Source Layer
Ingest & Rules
Apply
Standardize
(CODM)
Apply Metrics
Analytic
Ready Data
Job Management
Security
Audit Trail & Traceability
Data Quality
Data
Harmonization
Metrics Rules
Management

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Data Quality, Data Harmonization (DH), and Metric Rules
– Library of DQ rules arranged by Hierarchy – Organization, TA, Product, Project,
Study
– Configurable DQ rules on all data layers for structural data checks and functional
checks
– Workflow definition for error remediation
– Harmonize multiple data sources (for same entity) based on hierarchy DH rules
– UI based harmonization configuration at the lowest level
– Maintain history of harmonization process for audit trail and re-harmonization
– Self service functionality for metrics definition
– Hierarchy based metrics rules management
– Scalable data model to support new metrics (configurable data marts)
Data Quality
Data Harmonization
Metrics Rules
Management

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Common Data Model and Data Dictionary
– Defines the scope of Common Data model
– Manages OOB scope vs. sponsor extensions
– Hierarchical definition of operational data
elements
Data Dictionary
Common
Data Model
– Maps data dictionary at the physical data
model level
– Includes source specific canonical and
harmonized common data model
– Flat structure allows easier additions of
sponsor specific extensions

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How does a platform help in building ODS

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Source Adapters and Workflow

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Analytics Library

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Enabling AI
Support both Shallow and
Deep Learning
Ongoing Use cases
– Classification
Modelling (Smart
Queries)
– Regression Modelling (
Clinical Metrics
Prediction)
– Intelligent DQ
– Virtual assistant

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A Case Study

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Scenario Sponsor’s requirement to build a scalable platform for integrating all clinical operational data
Data Sources CTMS, EDC, Financials, MDM
Planned – IRT, eTMF, Clinical Supplies, Portfolio Management
Platform and Technology Saama’s Big Data and AI-based Clinical Development Optimizer
Key Dashboards Portfolio dashboards, country level dashboards, study start up, enrollment, site activation, Data
Management – SDV, queries, study budget and spend
Future Plans Integration with trial data sources, industry data, predictive analytics and modelling, patient data
store & unstructured data analysis
Performance Statistics Studies: 100+, Sites: 800+, Patients: 10,000+, Visits: 300,000+, Queries: 5 Mn+
Data refresh: Once daily
Time for refresh: < 4 hrs
ODS Case Study

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Lessons Learned
– Usage of reporting templates during requirements
– Usage of data dictionary to scope out OOB vs. sponsor extensions
– Need for metrics verification before the design and the build
– Account for source specific integration issues
– Importance of data profiling for downstream activities
– Identify dependencies (e.g. vendor) and enable their support
– Enable business support across multiple stakeholders
– Conference room pilots enabled easier UAT execution
– Plan for environments for support releases and multiple development teams
– Strong one time migration plan
– For user adoption, a clear roadmap needs to be established

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Thank You

Enabling Better Clinical Operations through a Clinical Operations Store

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