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How to Automate FDA Regulatory Document Generation with Claude and Streamlit

Prototype a controlled regulatory document pipeline that ingests approved deidentified clinical data, validates its structure, produces traceable draft sections and standards compliant XML, and gives medical, statistical, privacy, quality, and regulatory specialists inspectable evidence for each output.

How to Automate FDA Regulatory Document Generation with Claude and Streamlit

Source episode · 05:00

How Prerna Kaul Automates 60,000-Page FDA Submissions and Coaches PMs with AI

with Claire Vo

Prerna starts with strict requirements for clinical data structure and PHI redaction, uses Claude to generate setup instructions and code, exposes the workflow in a local Streamlit interface, produces a common technical document preview, exports structured XML, and displays per operation cost.

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Before you start

What you need

  • A defined regulatory submission section, jurisdiction, standard, schema version, and sponsor procedure
  • Approved source datasets with lineage, access controls, deidentification, and test fixtures
  • Authoritative templates, controlled terminology, validation rules, and acceptance test cases
  • A secured runtime for Claude and Streamlit with model and prompt versioning
  • Named accountable roles for medical writing, biostatistics, privacy, quality, regulatory operations, and final sign off

What you’ll make

A versioned draft package containing source lineage, deidentification results, tables and narrative, structured XML, validation reports, cost telemetry, change history, and unresolved exceptions for accountable regulatory review.

Tools used

Step by step

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

5 steps

Step01

Prototype with Claude as Your Co-founder

Define one submission section and its governing standard, schema version, source systems, transformations, roles, acceptance tests, and non goals. Ask Claude to turn the requirements into a prototype plan and code scaffold, while labeling assumptions and regulated decisions.

Example prompt
Design a prototype for [submission section] under [jurisdiction, standard, and schema version]. Inputs: [approved deidentified data and metadata]. Specify lineage, deterministic calculations, PHI controls, terminology, XML validation, audit trail, model and prompt versioning, tests, error states, and roles. Label assumptions and do not claim regulatory compliance.
Step02

Build Core Logic for PHI Redaction

Build deidentification as a testable pipeline with approved rules, specialized detectors, model assistance where permitted, confidence, and an exception queue. Preserve protected source lineage and evaluate both missed identifiers and excessive redaction.

Example prompt
For this approved test fixture, identify potential PHI according to [policy]. Return field, span, category, confidence, and reason. Do not rewrite clinical facts. Send uncertain cases to the exception queue and produce no external output until policy checks pass.
Step03

Develop a User-Friendly UI with Streamlit

Create a Streamlit interface for authorized users to upload permitted fixtures, inspect schema and PHI findings, correct mappings, run generation, and download artifacts. Enforce authentication, access logging, upload limits, safe errors, and retention controls.

Example prompt
streamlit run app.py
Step04

Implement Document Generation and Export

Generate draft narrative and tables from validated structured data, then render the required XML. Attach source lineage and prompt, model, code, schema, and terminology versions to every artifact before running authoritative validators.

Example prompt
Generate a draft [section] from the validated structured input. Use deterministic values supplied in [tables] without recalculation. Cite source field and transformation for every claim. Mark missing or conflicting inputs as exceptions. Produce XML for [schema version], then run [validators] and return all errors without silently repairing source data.
Step05

Integrate Cost Analysis for Stakeholder Buy-in

Record cost, latency, tokens, exception rate, reviewer effort, and validation failures by stage without exposing patient data. Compare these measures with the current process and use them for capacity and investment decisions, not as evidence of regulatory correctness.

What good looks like

  • Every generated claim, statistic, population count, and table traces to an approved source record and deterministic calculation.
  • PHI detection and deidentification meet the sponsor's documented policy on representative positive, negative, and adversarial tests.
  • XML passes the exact schema, terminology, cross reference, and technical validation required for the target submission version.
  • Accountable specialists resolve exceptions and attest to the final content in the sponsor's controlled submission process.

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After the steps

Runbook notes

How to recover when the loop fails and where human judgment helps.

Recover

If it goes sideways

The system misses patient identifiers or removes information needed for valid analysis
Use layered deterministic and model based detection, representative test sets, privacy review, and a reversible secure mapping outside the generation system.
The narrative or table contains a statistic that cannot be reproduced from the source
Generate statistics with validated code, attach lineage and formula, and block the affected section until it reconciles.
An official looking XML file fails the target standard or uses the wrong version
Pin the schema and terminology versions, run authoritative validators, and retain the full error report with the draft.
A model or prompt update changes regulated output without detection
Version models, prompts, code, templates, and fixtures, then run regression comparisons and document approved changes.
A prototype is mistaken for a validated production or submission system
Label its status, keep it in the controlled test environment, and complete the organization's validation and quality procedures before regulated use.

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