CASE STUDY
TrialForge AI
AI-assisted clinical trial documentation and protocol drafting, combining biostatistics expertise with OpenAI-powered generation to accelerate trial setup.
AI-Drafted
Protocol documentation generated from structured inputs
Validated
Cross-checked against trial design consistency rules
Biostatistics-Led
Prompt engineering grounded in trial methodology expertise
The Problem
Drafting clinical trial protocol documentation is slow, repetitive, and error-prone — coordinators spend significant time writing sections that follow predictable structures, while inconsistencies between sections can delay regulatory approval.
The Solution
I built an AI-assisted documentation tool that drafts protocol sections from structured trial parameters, checks them for internal consistency, and gives coordinators a structured review workflow before finalising submission-ready documents.
AI-Assisted Documentation
Generates and structures clinical trial protocol documents using OpenAI API-driven drafting.
Protocol Consistency Checks
Cross-references drafted sections against trial design rules to flag contradictions early.
Structured Workflow Engine
Guides trial coordinators through a repeatable, auditable documentation process.
Biostatistics-Informed Prompts
Prompt design grounded in biostatistics methodology to reduce hallucinated or invalid content.
How It Works
Trial Parameters Input
Coordinator enters trial design parameters — population, endpoints, arms, and methodology.
AI Drafting
OpenAI API drafts protocol sections and supporting documentation based on the input parameters.
Consistency Validation
Drafted content is checked against trial design rules for contradictions or gaps.
Human Review
Coordinator reviews and edits AI-drafted sections before finalising.
Export & Archive
Finalised protocol documentation is exported and archived for regulatory submission.
Outcome
TrialForge AI cuts the time required to produce first-draft protocol documentation, while its consistency checks catch design contradictions earlier in the process, reducing costly revisions later in regulatory review.