These AI Readiness Activities Make Advanced Technologies Implementation Faster And More Successful
Upstream
DOWNSTREAM
Build consensus on
data standards and governance
Define content
standards and governance
Create structured templates
Find your robust,
high volume data sets
Select technology
and stack partners
Define architecture and integrations
Understand where and how data repositories available
Create process updates that enable new ways of working
Enable feedback learning loops for refinement and improvement
AI application can be many things, so align the effort with the objective. We’ve included just a few examples of potential AI projects and what it takes to accomplish them.
Faster pattern recognition and anomalies and response time to change protocols, formulations, reporting requirements, etc
Business Value
Enhances signal monitoring that inform regulatory documentation associated with safety updates, reports and risk evaluations (PBRER, DSUR)
Monitors large data sets for safety issues, compliance and adverse events
Description & examples
Safety monitoring and documentation
Faster speed-to-submission, approval, and go-to-market
Less overhead and filing errors
Business Value
Enables automated reuse of content across documents used in dossiers/filings
Assembles filings
Description & examples
Regulatory submission process & automations
Better data integrity and connection between the source of truth and regulatory submission resulting in faster IND or BLA
Business Value
Automates CMC data into CMC submission to create a dynamic data feed
Description & examples
Chemical Manufacturing Controls (CMC) automation
Reduces time on critical path to a filing
Business Value
Generates CSR content by applying business rules to underlying clinical data
Automates Content Generation for Filing
Description & examples
Clinical study report generation
Faster study startup e.g., enrollment, reduce protocol amendments
Business Value
Generates protocol content using AI, ML or automation-based tools using study design attributes and business rules
Clinical trial predictive analytics
Trial inclusion and diversity optimization
Description & examples
Clinical protocol development
Post Launch
Commercialization
Commercialization
Clinical Trials
Pre-Clinical
Advanced technologies application across the product life cycle
