V. LLM-in-the-Loop CQL execution with unstructured data and FHIR terminology support
Here is how you can run the CQL execution with unstructured data and FHIR terminology support using our LLM-in-the-Loop stack as presented at AMIA #CIC25.
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Here is how you can run the CQL execution with unstructured data and FHIR terminology support using our LLM-in-the-Loop stack as presented at AMIA #CIC25.
The GitHub repository below is a fork of the CQL Execution Framework, which provides a TypeScript/JavaScript library for executing Clinical Quality Language (CQL) artifacts expressed as JSON ELM. The fork introduces an experimental feature supporting LLM-based assertion checking on DocumentReference. The framework enables execution of CQL logic within different data models, such as QDM and FHIR, but does not provide direct support for data models or terminology services. The library implements various features from CQL 1.4 and 1.5 but has some limitations, such as incomplete support for specific datatypes and functions.
MedpromptJS converts CQL statements to natural language, splits unstructured data into chunks, and generates yes/no answers based on LLM-mapped facts.
My enhancement now extends support beyond VSAC, enabling the use of FHIR-compliant terminology servers for private or custom-defined Value Sets. This added feature allows healthcare organizations to leverage their own FHIR-based terminology repositories, improving flexibility for institutions that need localized or proprietary clinical vocabularies while maintaining compliance with existing standards. Users can specify a FHIR Base URL to direct queries toward non-VSAC terminology servers, ensuring broader accessibility to domain-specific terminologies.
Clinical Query Language (CQL) is a flexible, domain-independent query language designed to support clinical decision-making by enabling intuitive and standardized queries without requiring extensive technical knowledge. It works with any data model, integrates with widely used programming languages, and relies on the Expression Logical Model (ELM) as an intermediary format to ensure consistency with existing healthcare data standards. The open-source CQL-to-ELM Translator, built in Java, facilitates seamless execution of CQL queries by converting them into ELM representations, supporting various customization options and integrating with FHIR, QDM, and QUICK models to enhance clinical data interoperability.