Healthcare ERP ETL AI Agent
A regional hospital network operating Epic EHR alongside an Oracle ERP struggled with manual, error-prone ETL between clinical and financial systems. Claims reconciliation, supply-chain accruals, and ...
Project Overview
Project Information
Technologies Used
Our team leveraged modern technologies and best practices to deliver a robust, scalable solution that meets enterprise requirements.
The Challenge
Every great solution starts with understanding the problem. Here's what they were facing.
A regional hospital network operating Epic EHR alongside an Oracle ERP struggled with manual, error-prone ETL between clinical and financial systems. Claims reconciliation, supply-chain accruals, and cross-system reporting required a team of analysts to hand-key and reconcile records, while unstructured documents (EOBs, claim denial letters, purchase orders, contracts) sat outside any structured pipeline. Reconciliation cycles ran 9-11 business days and claim denials from miscoding exceeded 12%.
Our Solution
We developed a comprehensive solution that addressed their challenges and delivered measurable results.
We delivered an autonomous AI agent that orchestrates end-to-end ETL between the EHR and ERP. AWS Step Functions coordinate the workflow: Amazon Textract ingests EOBs, denial letters, and POs; Amazon Comprehend Medical extracts clinical entities and de-identifies PHI; AWS Glue performs schema transformation and loads into Amazon Aurora. The agent uses Retrieval-Augmented Generation (RAG) over an Amazon OpenSearch Serverless vector store indexed with payer contracts, billing policies, and HCPCS/CPT coding rules; Amazon Bedrock (Claude) grounds its classification and coding decisions in that knowledge base, generating reconciling journal entries and denial-appeal narratives. AWS Lambda handles event-driven triggers; Amazon SageMaker hosts a fine-tuned coding classifier. The whole pipeline runs under a HIPAA BAA with end-to-end KMS encryption and CloudWatch audit trails.
Results & Impact
The measurable outcomes that were achieved through our partnership.
Reduced EHR-to-ERP reconciliation cycle from 9 days to under 8 hours
Cut claim denial rate from 12% to 3.4% via RAG-grounded coding
Automated ingestion of 40,000+ unstructured documents per month
Saved finance team ~1,200 staff hours per month
Achieved 99.2% straight-through processing on supply-chain accruals
Technology Stack
The tools and technologies we used to build this solution.
Other
Each technology was carefully selected to ensure scalability, maintainability, and optimal performance for the project requirements.
"The AI agent fundamentally changed how finance and revenue cycle interact with clinical data. RAG over our own payer contracts means the coding decisions are explainable and auditable, which is non-negotiable in healthcare."
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