This case study highlights how Stanford Health Care improved AI performance using InterSystems IRIS for Health to power its ChatEHR application. The challenge was enabling real-time, context-rich access to fragmented patient data, as earlier approaches required multiple API calls and resulted in slow response times. By implementing a unified FHIR-based data repository (AXIOM), Stanford streamlined data ingestion, reduced latency, and cut query times from minutes to seconds. This enabled clinicians to interact with patient records in natural language, accelerating decision-making and reducing administrative burden. The solution improved productivity, enhanced care delivery, and created a scalable foundation for future AI capabilities like diagnosis support and personalized treatment recommendations.

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