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Under-triage in AI healthcare agents can lead to severe negative health consequences. A confident hallucination is more dangerous than a system error.

Most teams assume a well-prompted LLM can handle patient intake. The reality is that LLMs are probabilistic, meaning they guess the next best word. In a triage workflow, a guess is a liability.

I condensed the solution into a 12-page field guide on risk detection layers.

The mechanism works as a supervisor loop:

1. The patient describes their symptoms.
2. The AI agent drafts a response.
3. A dedicated risk layer scans for high-acuity patterns using a hybrid of LLMs and statistical models.
4. If a severe indicator is found, the system bypasses the AI and routes the patient to a human clinician.

This turns a probabilistic process into a deterministic one. Instead of hoping the AI recognizes a crisis, you have a verifiable audit trail that logs the severity level and the specific clinical indicators identified.

For an ops leader, this moves the AI from a risky experiment to a tool that enforces Standard Operating Procedures.

The full breakdown and the AI Interceptor Loop diagram are in the 12-page guide below.

How are you currently auditing your AI outputs to ensure high-risk cases are escalated to humans?

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