AWS releases automated healthcare claims pipeline using Amazon Bedrock Data Automation and AgentCore

AWS has published a technical implementation guide demonstrating how to automate healthcare claims processing using Amazon Bedrock Data Automation for document extraction and Amazon Bedrock AgentCore for validation and FHIR resource creation.

Technical architecture

The pipeline processes CMS-1500 claim forms through a multi-stage workflow. When a PDF is uploaded to Amazon S3, AWS Lambda triggers the following sequence:

1. Amazon Bedrock Data Automation extracts structured data from the form using OCR, machine learning models, and generative AI. The output includes confidence scores and bounding box data in JSON format.

2. An AI agent running on Amazon Bedrock AgentCore validates the extracted data against existing patient and provider records in AWS HealthLake, checking for completeness and consistency.

3. If validation passes, the agent creates a standardized FHIR (Fast Healthcare Interoperable Resources) claim resource in HealthLake and sends notifications via Amazon SNS.

The agent uses two tools to interact with HealthLake: create_fhir_claim and search_fhir_resources. It attempts to match Insured, Patient, Practitioner, and Coverage information using direct method calls with default search parameters, then retries with different parameters if initial searches fail.

Agent workflow details

According to AWS, the AgentCore agent follows a specific validation workflow. It first searches for required references (Insured, Patient, Practitioner, Coverage) in HealthLake using high-confidence attributes from the extracted claim data. The agent can adapt its search strategy across multiple attempts, focusing on different claim attributes when exact matches fail.

The system generates two types of responses: technical summaries for claims processors and patient-friendly explanations. In failure scenarios where required resources are missing, the agent produces human-readable error messages. In success scenarios with data discrepancies, the agent documents its resolution method.

Implementation requirements

The solution requires:

• Access to Anthropic Claude Sonnet 4.6 on Amazon Bedrock
• AWS CDK version 2.1025 or later
• Python 3.13 or later
• NodeJS 24 or later
• Administrator AWS account permissions

AWS provides the full implementation as open source code on GitHub (aws-samples/sample-agenticidptohealthlake). The deployment uses AWS CDK and the AgentCore command line interface.

What this means

This implementation demonstrates how Amazon Bedrock's newer capabilities (Data Automation and AgentCore) can be combined to automate document-heavy workflows that traditionally required human oversight. The use of FHIR standards and integration with AWS HealthLake shows AWS targeting healthcare-specific automation use cases. The agent's ability to retry searches with different parameters when exact matches fail addresses a practical problem in healthcare data matching where slight variations in formatting or data entry create false negatives. However, AWS has not disclosed accuracy benchmarks, processing times, or cost per claim for this implementation.