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AI/Automation Strategy for Clinical Content
- •Led the strategic scoping and documentation of an AI-driven content operations model for Tata 1MG's Clinical Content team.
- •Mapped the end-to-end content creation, review, audit, and publication lifecycle — identifying where AI could replace manual effort, augment clinical decision-making, or accelerate throughput.
- •Covered pharma drug content, diagnostics content, and OTC/VMS content streams as distinct but related AI opportunity areas.
The Challenge
- Clinical content at scale requires both clinical accuracy (which demands human expertise) and operational speed (which demands automation).
- Defining which content operations are safe to automate vs which require mandatory human review in a regulated health-tech environment.
The Approach
- Created a detailed AI strategy document and Figma whiteboard mapping AI use cases across the content lifecycle.
- Defined prompt frameworks for accuracy evaluation, comprehensiveness scoring, and FAQ generation.
- Established the governance model for AI-assisted vs AI-generated content, with mandatory human review gates for safety-critical attributes.
Results
AI/Automation Strategy for Clinical Content.
Prompt frameworks in production use for content accuracy audits.
Comprehensiveness screening pipeline.
Tech Stack
FigmaGenAI (LLM-based)PythonPrompt engineeringExcelChatGPTGeminiConfluence