Are you tired of the weeks-long bottleneck in clinical trial design? Translating a simple narrative research question—like “Adults with type 2 diabetes, excluding those with end-stage renal disease”—into executable electronic health record (EHR) queries has historically been a manual, error-prone nightmare. It requires an army of clinical informaticians, terminology experts, and database engineers to navigate semantic ambiguity and fragmented medical terminologies.
But what if you could automate this entirely?
We are building a tool that aims to be a breakthrough multi-provider AI pipeline is changing the game. This system will act as a semantic compiler, transforming unstructured, free-text clinical queries into machine-actionable formats in a matter of minutes.
Here is how it is will revolutionalize cohort discovery:
- Standardized Translation: The AI automatically extracts and structures PICO (Population, Intervention, Comparison, Outcome) criteria, mapping them instantly to international interoperability standards like SNOMED CT, LOINC, RxNorm, and ICD-10-CM.
- Computable Outputs: It generates machine-ready artifacts, including definitional HL7® FHIR® Group Resources, executable Clinical Quality Language (CQL) scripts, and chained RESTful queries.
- Clinical Safety Analysis: It doesn’t just parse text blindly; it builds interactive Boolean logic trees, offering real-time sensitivity analysis and alternative terminology recommendations to prevent accidental cohort exclusions.
Crucially, the architecture prioritizes airtight institutional security. The zero-backend system operates strictly on computable definitions and never ingests Protected Health Information (PHI). With features like ephemeral RAM-only key management, automatic browser-close purge protocols, and the ability to run 100% locally via offline LLMs, it seamlessly satisfies strict HIPAA and GDPR on-premise mandates.
By eliminating the informatics bottleneck, this deterministic AI pipeline empowers researchers to focus on what actually matters: advancing medical science.