Manually processing paper-based forms remains a significant cost in the healthcare industry. Despite advancements in data extraction of scanned documents and images, human oversight is usually still needed. Entry error by the individual creating the form or lower-confidence extractions from the digitization still must be remediated.

In this post, we show you how to build an automated claims processing pipeline using two key Amazon Bedrock capabilities: Amazon Bedrock Data Automation for intelligent document extraction from healthcare claim forms, and Amazon Bedrock AgentCore for hosting an AI agent that validates and transforms the extracted data into FHIR (Fast Healthcare Interoperable Resources) resources in AWS HealthLake. You will learn how to combine these services to create an end-to-end workflow that reduces manual processing while maintaining accuracy through automated validation checks.

The solution demonstrates an automated workflow for processing healthcare claim forms using AI-powered services. When a healthcare provider uploads a CMS-1500 claim form (in PDF format) to an Amazon Simple Storage Service (Amazon S3) bucket, it triggers a processing pipeline starting with AWS Lambda that performs three main functions:

This automated workflow helps reduce manual processing time while maintaining accuracy through AI-assisted validation.

Lambda is used as an event trigger when a document is created in S3 and serves as a deterministic supervisor over the agentic workflow. It validates that each document is processed or sent to a dead letter queue for exception handling.

Bedrock Data Automation streamlines generative AI development and automates workflows involving documents, images, audio, and videos. For document processing, Bedrock Data Automation combines traditional optical character recognition (OCR), machine learning (ML) models, and generative AI to extract data accurately. You can use Blueprints (artifacts) to specify what data to extract from a document and how to extract it. You can use pre-built templates or build custom configurations tailored to your use cases. The output includes confidence scores and bounding box data for the extracted fields and tables. The custom output here produces a predictable JSON representation of the CMS-1500 claim form across its format variations.