What challenge did the clinical research organization face?

The organization needed to review hundreds of lengthy clinical trial protocols every month and manually identify all required laboratory tests.

What outcomes did the automation solution achieve?

The solution reduced the extraction team’s workload by 50% and projected $300,000 in savings over three years.

How can the workflow be built with modern agentic AI?

A Claude-powered document agent can read complete protocols, identify laboratory tests, provide source-page citations, and connect with governed enterprise systems.

AI Clinical Trial Protocol Automation

How InNeed helped a clinical research organization reduce protocol review workload by 50%, save $300,000 over three years, and accelerate clinical trial operations.

AI-powered clinical trial protocol automation platform

The Challenge

A clinical research organization processes roughly 300 trialprotocols a month. Each is an 80-100 page document, and a teammust read every page, identify every required lab test, and purchaseit for the trial. Missing a single lab can cause tens of millions in delaysand erode client trust. The work was slow, manual, and high-risk.

From Custom AI Pipelines to Intelligent Document Agents

What once required multiple OCR and NLP systems can now be handled by a single Claude-powered document agent. Claude reads entire protocols, extracts lab tests with source citations, and integrates with enterprise workflows-delivering faster deployment, higher accuracy, and simpler maintenance.

What We Built (Then)

Custom OCR pipeline using Amazon Textract to process trial protocols.
Medical entity extraction powered by Amazon Comprehend Medical.
Custom machine learning models built and deployed with Amazon SageMaker.
Additional NLP processing and validation using SpaCy.
Automated extraction of laboratory tests from lengthy clinical trial documents.
Required significant model training, tuning, and ongoing maintenance.
Multi-component architecture with higher deployment and support complexity.

How We Build It Now — with Claude

Claude reads entire clinical trial protocols natively without custom OCR/NLP pipelines.
Context-aware reasoning identifies laboratory tests more accurately across complex documents.
Returns structured lab-test outputs with source-page citations for verification.
Uses Retrieval-Augmented Generation (RAG) to stay grounded in governed enterprise data.
Connects to downstream systems through the Model Context Protocol (MCP).
Functions as an autonomous document-processing workflow rather than a standalone extraction tool.
Faster implementation with significantly reduced engineering effort.
Easier auditing, governance, and compliance management.
Designed for HIPAA-aware healthcare and clinical research environments.