Aetion is a provider of real-world evidence software for the healthcare industry, serving biopharma companies, payors, and regulatory agencies. This case study examines how they implemented a production LLM system to help researchers interact with complex patient data through natural language queries. The core problem they addressed was that valuable insights often remain hidden in complex healthcare datasets because researchers need both the right structured queries and deep familiarity with the data to discover patterns. Their solution, called Smart Subgroups Interpreter, combines traditional machine learning with generative AI to make these insights more accessible. ## Technical Architecture and Implementation The system architecture consists of several key components working together: * A feature generation pipeline that computes over 1,000 standardized medical features for each patient using their Aetion Measure Library (AML) * An unsupervised clustering component that identifies patient subgroups with similar characteristics * A generative AI interface powered by Amazon Bedrock and Claude 3 Haiku that enables natural language interaction with the clusters The implementation demonstrates several important LLMOps best practices: ### Infrastructure and Deployment * The solution runs on Kubernetes on AWS for scalability and portability * Uses Amazon S3 and Aurora for persistent storage with proper encryption via AWS KMS * All data transmission is secured using TLS 1.2 * Applications are structured as microservices for maintainability ### LLM Integration * Carefully selected Claude 3 Haiku model based on performance and speed requirements * Implemented composite prompt engineering techniques including: * Versioned prompt templates for generating subgroup descriptions * Dynamic inclusion of AML feature descriptions to provide medical context * Integration of statistical data about feature prevalence in clusters * Used Amazon Bedrock's unified API for model access and management ### Data Processing Pipeline * Standardized feature definitions using scientifically validated algorithms * Automated generation of over 1,000 features per patient * Integration of unsupervised learning for cluster generation * Classification models to identify distinctive features within clusters ## Production Considerations and Outcomes The system shows careful attention to several critical production aspects: ### Security and Compliance * Healthcare data security maintained through encryption at rest and in transit * Controlled access through proper authentication and authorization * Compliance with healthcare data regulations ### Performance and Scalability * Kubernetes deployment enables elastic scaling * Batch and transactional workloads properly separated * Model selection optimized for response time and accuracy ### User Experience * Natural language interface reduces barrier to entry * Interactive heat map visualization of clusters * Ability to drill down into specific subgroup characteristics * Seamless integration with other Aetion analysis tools ### Quality and Validation * Maintains scientific rigor while increasing accessibility * Results can be used to generate formal hypotheses for further research * Integration with existing validated healthcare analytics pipelines ## Results and Impact The implementation has delivered significant business value: * Reduced time-to-insight from days to minutes * Eliminated need for support staff for many analyses * Enabled non-experts to discover meaningful patterns in healthcare data * Maintained scientific validity while increasing accessibility * Created a path from exploratory analysis to rigorous evidence generation ### Challenges and Considerations The case study highlights several important challenges in implementing LLMs in healthcare: * Balancing ease of use with scientific rigor * Ensuring appropriate context is provided to the LLM * Managing healthcare data security and compliance * Integrating with existing validated analytics systems ### Future Directions Aetion continues to expand their generative AI capabilities while maintaining their commitment to scientific validity. The platform demonstrates how LLMs can be effectively integrated into specialized domain applications while maintaining high standards for accuracy and compliance. This implementation serves as an excellent example of how to properly deploy LLMs in a regulated industry, showing how to balance user accessibility with domain expertise and scientific rigor. The architecture demonstrates good practices in security, scalability, and maintainability while delivering clear business value through reduced analysis time and increased insight discovery.