The Challenge: Manual Claim Processing and Classification Inconsistency
| Industry | Core Problem |
| Medical Devices/ Implants | Slow, subjective, and error-prone manual processing of implant claims hindered classification consistency, traceability, and PMS efficiency. |
Our client, a manufacturer of medical implants, was receiving a high volume of warranty claims through manual Post-Market Surveillance (PMS) workflows. The lack of automation introduced several risks:
- Processing Delays: Manual handling led to long response times and operational bottlenecks
- Inconsistent Classification: Lack of standardized coding resulted in variable outcomes across similar claims
- Subjectivity in Decision-Making: Human interpretation introduced bias, affecting internal consistency and quality control
- Limited Traceability: Absence of structured documentation made internal review and process validation difficult
The client required a scalable, objective PMS system to ensure consistent claim classification and structured documentation.
The Solution: Intelligent Code Categorization Pipeline with LLM
We implemented an automated Post-Market Surveillance (PMS) pipeline powered by a Large Language Model (LLM), designed to:
- Automated Data Extraction: Incoming claim forms are received via Amazon API Gateway and stored in Amazon S3 (Raw Data). An event-driven workflow orchestrated by AWS Step Functions and implemented using AWS Lambda functions is triggered to automatically capture and process the relevant information.
- Structured Storage: The clean, processed data is organized and stored in a queryable format using Amazon DynamoDB for structured output and Amazon S3 (Clean Data) for data lake storage, enabling efficient downstream analysis.
- High-Precision Classification: The categorization of claims (using both client’s internal and the FDA’s required codes) is performed by the LLM, which is hosted and managed using Amazon Bedrock Endpoints for high-precision, real-time inference within the orchestrated pipeline.
- Justification Layer: The Amazon Bedrock FMs also generates the documented reasoning for each classification to support traceability and validation. Amazon CloudWatch is used for logging and monitoring the entire process, while Amazon SNS or Amazon SES can be used for final notifications and reporting.
Impact and Key Benefits
The implementation of the AI-powered PMS solution delivered significant operational and regulatory advantages:
- Accelerated Processing: Reduced resolution time for warranty claims through automated workflows
- Improved Classification Accuracy: Minimized errors and inconsistencies in claim categorization
- Objective Decision-Making: Replaced manual bias with data-driven classification logic
- Structured Documentation: Enabled consistent records to support internal review and compliance alignment
- Operational Resilience: Strengthened surveillance workflows to support market continuity