Enterprise AI Analysis

The system indexes 166 million clinical notes (484 million embedding vectors) with a one-time build cost of USD 891. Monthly operational costs are approximately USD 4,021, primarily for vector database capacity and serving. Query latency is sub-second (median 237 ms for vector search at single-user concurrency, 451 ms at 20-user concurrency), demonstrating scalability under concurrent load. A comparison with an in-memory index shows the storage-optimized approach reduces costs by over 50% for 8% of the corpus, making health-system scale deployment economically viable.

Clinical utility was assessed across three abstraction tasks. Semantic search significantly reduced time-to-completion: 24.1% for genetic conditions, 51.9% for seizure documentation, and a remarkable 89.4% for ballet-related foot injuries. Inter-rater agreement remained comparable to traditional EHR abstraction methods, indicating maintained data quality. Users noted the ability to query across the entire patient population simultaneously and surface relevant information from previously overlooked note types, enhancing cohort identification and research efficiency.

Current limitations include deployment at a single pediatric center, reliance on text-only data (lacking multimodal support for images/tables), and fixed-size chunking. Future work will explore content-aware semantic chunking, integration of multimodal data, and longitudinal user studies to quantify the impact on research outcomes. The system is designed as institutional infrastructure, supporting interactive search, cohort generation, and LLM-powered applications.