ProductionEvidence: Medium55/100

Huron Accelerates Clinical Trial Coverage Analysis with Amazon Bedrock

Use case typeMedical document automationUpdated Jun 13, 2026

Huron, a healthcare consulting firm, uses generative AI to accelerate the analysis of clinical trial coverage documents. The company needed to extract critical billing and coverage information from complex unstructured documents, including graphs, charts, text, and images, which previously required manual search across multiple documents. Huron built a generative AI solution on AWS with Amazon Bedrock, Amazon Bedrock Knowledge Bases, and Amazon OpenSearch Serverless to support faster, more accurate clinical trial administration.

Organization
Huron
Industry
Healthcare
Location
United States
Published
May 2026

Reported outcomes

Strategic outcomes

Speed & agilityShortened clinical trial launch turnaroundNew product / capabilityEnabled querying across all documentsSpeed & agilityEliminated manual document searchingBetter decisions & insightImproved response speed and accuracy
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
Huron
Provider
AWS
Maturity
Production
Linked source
AWS Case Study

Built and deployed a proof of concept using Amazon Bedrock in March 2024 after evaluating a homegrown LLM approach

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

  • 1Clinical Trial Enablement
  • 2Document Analysis
  • 3Knowledge Retrieval
  • Extract critical billing and coverage information from complex unstructured medical documents.
  • Reduce the manual, time-consuming process that delayed clinical trial launch and administration.
  • Improve speed and accuracy while maintaining security and control over sensitive data.
  • Built and deployed a proof of concept using Amazon Bedrock in March 2024 after evaluating a homegrown LLM approach.
  • Used Amazon Bedrock native capabilities for chunking, vector storage, and information retrieval instead of multiple custom scripts.
  • Implemented Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless to support retrieval and search across documents.
  • Standardized a generative AI foundation that lets users query all relevant documents at once and ask follow-up questions.
Technologies
Amazon BedrockAmazon Bedrock Knowledge BasesAmazon OpenSearch Serverless
  • The company said the solution significantly shortened turnaround times for launching clinical trials.
  • Huron reduced development and debugging effort for content vectorization by about half.
  • Manual searching across documents was eliminated for the targeted workflow.
  • The approach improved response speed and accuracy for predictive and coverage analysis workflows.
Architecture

Huron's AI team moved from a custom LLM-based proof of concept to Amazon Bedrock. The solution uses Amazon Bedrock native chunking, vector storage, and information retrieval capabilities, Amazon Bedrock Knowledge Bases for retrieval-augmented responses, and Amazon OpenSearch Serverless for search and log analytics without infrastructure management.

Sources & evidence1
Evidence: Medium55/100Evidence strength
  • Customer explicitly identified
  • Deployment status explicitly supported
  • Primary source available
  • Technical implementation details available
Type: Customer StoryPublished: May 13, 2026Publisher: AWSEvidence: PrimaryConfidence: High
Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

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AI patterns
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