Intelligence Factory

Looking at your publication I notice this structured data representation between the LLM and the prescription. We found that adding an Ontology to the LLM reduced hallucination from 92% (GPT 5) to 8% when mapping from Clinical terms to SNOMED.

Good reminder, Theo. Equally important is the practice of setting rates too high. As pointed out by a recent OIG report charges exceeding 200% of the national average are a "Red Flag" for audit risk. fairpath.ai/resources/rpm-gu… See our Fraud Risk Report for more info.

This isn't just about healthcare. It's a blueprint for all "grounded symbolic mapping" tasks: • Legal docs → Statutes • Cyber reports → CVEs • Product → Taxonomies • Science → Gene Ontologies

L3 (Autonomous Agent): The LLM could now search, navigate the @snomedct hierarchy, and refine its own results. Final Accuracy: 79.48% (Exact) & 89.48% (Looser). This is the accurate, verifiable, and production-ready result.

The results scaled with autonomy: * L1 (Selector): 55.48% (vs 53.91% baseline). Proved the LLM is a smarter tie-breaker. * L2 (Expander): Jumped to 63.91%. We used the LLM to generate synonyms ("HFpEF") before searching to solve the recall problem.

Our solution: Ontology-Guided Augmented Retrieval (OGAR). We stopped giving the LLM an open-ended essay and gave it a multiple-choice test. We transformed the task from generative recall to constrained reasoning.

We first asked the models to recall the code from "memory." The results were a catastrophe. • @OpenAI GPT-5: 8.43% • GPT-5-mini: 5.30% • @xai Grok-4: 1.22% • @xai Grok-4-fast: 0.26% They all hallucinated non-existent codes. This "memory-only" approach is unusable.

The task: map 1,150 real clinical terms—shorthand like “AAA” or “HFpEF”—to precise @snomedct medical codes. This is a high-stakes precision task. Get it right = analytics, billing, safety. Get it wrong = chaos.

RPM billing shouldn’t be a manual headache. One missing entry? Denied. Cash flow delayed for months. FairPath auto-tracks RPM minutes from your EMR, cutting denials to under 5% across 1.1M claims. Faster payments, fewer rejections.

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Today, our CEO @JustinBrochetti hit the SBE Small Business Expo today, connecting with our community. At Intelligence Factory, we create safe, controlled AI to streamline healthcare billing—cutting denials since 2018. How can AI help you? Let’s talk!

Are you tired of your practice losing revenue to denials? We can help you. Go to fairpath.ai and claim your free trial!

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At #HIMMS2025 all we hear is people talking about AUTOMATION, AUTOMATION, AUTOMATION. If you want to ride the billing automation wave, go to intelligencefactory.ai/fairp… to learn more.

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