Medicomp Launches AI Validation Tools to Stop Clinical Hallucinations

Why It Matters

By grounding AI output in validated medical knowledge, Medicomp reduces patient‑safety risks and regulatory exposure, accelerating trustworthy AI deployment across health systems.

Key Takeaways

• MCP links LLMs to 45‑year clinical knowledge graph
• Filters AI hallucinations before EHR entry
• Enables safe AI APIs without exposing PHI
• Supports diagnostic prompting, chart summarization, coding crosswalks
• Positions Medicomp for enterprise AI adoption at HIMSS

Pulse Analysis

The rapid integration of generative large‑language models into clinical workflows has sparked both excitement and alarm. While ambient scribes and AI‑driven decision support promise efficiency gains, they also introduce the danger of “hallucinations”—fabricated or inaccurate data that can corrupt electronic health records. Such errors threaten patient safety, regulatory compliance, and provider trust. As hospitals scale AI pilots to enterprise‑wide deployments, the industry is searching for mechanisms that can verify output in real time, ensuring that every AI‑generated note aligns with established medical facts. Regulators are already drafting guidance that could make such safeguards mandatory.

Medicomp’s Model Context Protocol (MCP) offers a pragmatic solution by inserting a validation layer between unrestricted LLMs and its proprietary 45‑year‑old clinical knowledge graph. The protocol routes AI queries through structured ontologies, cross‑checking terminology, diagnoses, and coding against vetted sources before any information reaches the EHR. This approach not only curtails hallucinations but also safeguards protected health information, as the AI never directly accesses raw PHI. Developers can thus build “agentic” applications—diagnostic prompting, chart summarization, quality‑measure evaluation—while relying on a trusted data backbone. The protocol also logs each validation step, creating an audit trail for compliance teams.

The launch arrives ahead of major health‑IT gatherings such as ViVE 2026 and HIMSS26, positioning Medicomp as a frontrunner in AI governance. By delivering enterprise‑ready validation tools, the company addresses a critical barrier to broader AI adoption, potentially accelerating contracts with health systems wary of liability. Competitors may follow suit, prompting a wave of standards around model grounding and data integrity. For investors and clinicians alike, the MCP signals a shift from experimental AI pilots toward regulated, scalable solutions that preserve clinical accuracy while unlocking the productivity promised by generative technologies. Early adopters report faster chart completion times and reduced chart‑review errors, reinforcing the business case.

Medicomp Launches AI Validation Tools to Stop Clinical Hallucinations

By Jasmine Pennic · February 13, 2026

What You Should Know

• The Launch: Medicomp Systems has unveiled a new suite of intelligence tools designed to “ground” AI in clinical reality. The announcement comes ahead of the ViVE 2026 and HIMSS26 conferences.

• The Problem: As healthcare rushes to adopt generative AI (like ambient scribes), there is a growing risk of “inaccurate, incomplete, or poorly structured data” entering the Electronic Health Record (EHR) without validation.

• The Tech: Medicomp is introducing a Model Context Protocol (MCP) layer that connects wild LLMs to its curated, 45‑year‑old clinical knowledge graph. This allows the system to identify and filter out “hallucinations” or inconsistent data before it is saved.

The “Model Context Protocol”

A key technical innovation in this release is the addition of a Model Context Protocol (MCP) layer. This architecture allows Medicomp to safely expose its APIs to AI models without giving the AI free rein over Patient Health Information (PHI). It acts as a bridge, allowing developers to build tools for:

• Diagnostic Prompting

• Chart Summarization

• Quality Measure Evaluation

• Coding Crosswalks

By using the MCP, health‑tech developers can build “agentic” workflows—like asking a voice assistant to “filter this patient’s chart for all cardiac issues”—knowing the AI is retrieving structured, validated data rather than just guessing.

“Healthcare organizations are increasingly focused on advancing AI from isolated use cases into reliable, enterprise‑ready capabilities,” said David Lareau, President and CEO of Medicomp. “Our approach is to enable such innovation while preserving clinical integrity.”