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How I AIHow this PM streamlines 60k-page FDA submissions with Claude, Streamlit, and clever AI workflows
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At a glance
WHAT IT’S REALLY ABOUT
AI workflows cut months from 60k-page FDA submission drafting time
- The episode shows how life-sciences regulatory submissions (e.g., a ~60,000-page BLA/CTD package) can be accelerated using a GenAI-driven workflow that produces strict XML outputs and redacts PHI from messy clinical notes.
- Prerna starts by prompting Claude like a “software engineer,” using a PM-style narrative (impact, stakeholders, demo goals) to get a full implementation plan, setup guide, and code scaffolding.
- She then wraps the workflow in a Streamlit UI for non-technical users, adds synthetic-data generation for testing, and includes trace-and-cost transparency to address organizational concerns about scaling AI usage.
- In a second workflow, she uses Anthropic’s Prompt Generator and Projects to create a structured stakeholder-influence coach, grounded in public-domain persuasion/literature sources, that outputs meeting prep, agendas, and anticipated exec questions.
IDEAS WORTH REMEMBERING
5 ideasTreat the LLM like an engineering partner, not just a chatbot.
Prerna’s strongest results came from giving Claude a PM-grade problem statement (why it matters, expected product, demo goals), which yielded not only code but also setup docs and a demo narrative—accelerating initial solution shaping.
Strict output formats (XML) must be a first-class requirement.
Because CTD/BLA submissions require rigid XML structure, she made “structured, schema-like generation” a core constraint from the start—reducing downstream rework and making the workflow viable for regulated submissions.
PHI redaction is harder in free-text notes than in tables.
Clinical data contains both structured fields and unstructured clinician notes; effective redaction needs medical NER-style detection beyond column-based masking, especially to catch names, dates, and other identifiers embedded in prose.
A simple UI can be the adoption unlock for non-technical stakeholders.
Wrapping the pipeline in Streamlit turned a script into a usable internal tool—critical for cross-functional teams like medical writers and regulatory specialists who need buttons and previews, not notebooks.
Use synthetic data to test privacy and formatting workflows safely.
Generating synthetic trial data enabled validating PHI detection/redaction and document outputs without exposing real patient information—an important practice in regulated environments.
WORDS WORTH SAVING
5 quotesWe had to develop a nearly 60,000-page document. Would have taken about four to six months of effort and nearly 20 specialists...
— Prerna Kaul
The thinking I had in mind is that Claude is a software engineer... trying to tell them why it matters... what end product we want to produce.
— Prerna Kaul
The first is... the BLA is a structured document... XML-based... The second was... detect PHI... because it's patient data.
— Prerna Kaul
If you're saving on time, you're bringing life-saving vaccines in the hands of people who actually need it.
— Prerna Kaul
If you're getting internal resistance to cost... bring true transparency [to] ROI and investment.
— Claire Vo
High quality AI-generated summary created from speaker-labeled transcript.
