Digital Innovations, or Dpharm, organized by The Conference Forum was held in Boston in September 2018. Amit Gulwadi, Senior Vice President, Clinical Innovations, Saama Technologies, will be speaking on the topic Destroy Clinical Data Silos. Data Silos are a major roadblock in the clinical trial development process. Disparate data sources and multiple teams working on them increase complexities and delays. One solution to this can be building a comprehensive platform that will streamline clinical data to derive valuable insights.

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How to Destroy Clinical Data Silos
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Agenda
01
02
03
04 Q&A
Challenges
Background
Solution

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Recent Trends in Clinical Trials: The Ground Truth
Upward Trend in Number
of Studies Registered
per Year
Global Studies
Higher Outsourcing
Technology Explosion

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Problem Areas Within an Organization
Study Manager
What is the query resolution
rate for ABC –XXX-YZ?
Global Clinical Operations Head
Across therapeutic areas how many studies are at risk?
Franchise Head
Are we above budget for
studies?
User Personas need specific Insights
“Challenge is Speed to Insights”
Complex Ecosystem
CRO 1
CRO 2
Partner 1
Partner 2
M&A
Multiple Siloed System
CTMS
EDC
IVR
Finance
Unstructured

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Solving the Problem: Improve Data Processing,
Standardization, and Creation of Analytics Ready
Data Sets

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Improve Data Processing, Standardization, and
Creation of Analytics Ready Data Sets
Strategy of managing
data-pipelines
Right Technology Stack
E.g. Visualizing data pipeline work flows
Manage
Data Quality

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Centralize (and Contextualize) Data in Flight

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Integrated and Interactive Dashboards: Speed to
Insights

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“Smart Assistant” for Clinical Operations: Conversation with data
How may I help?
Show me screen failures
rates for study ABC-1234?
12%

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Source : https://www.creditkarma.com/
John Doe
All phase II Studies are on track.
Next DBL for Study 001 Dec 1st
2018.
Study on trackBudget utilization
Avg. study periodStudy at risk
Upcoming DBLTotal Ongoing Studies
Your Study Portfolio looks
good
What the Future Will Look Like: Executive Summaries

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Machine Learning Use Cases for Clinical Operations
Planning
Startup
Conduct
Closeout
• Clinical trial design: Prediction of patient drop outs and PV/D
• Benchmark studies identification
• Country and site selection and rate of recruitment prediction
• Site document verification
• Site contracts
• Prediction of risks
• Risk identification in monitoring visit reports (MVR)
• Smart queries
• Medical monitoring
• Automated standardization of SDTM and ADaM

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Thank you !!!