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Evaluation, Monitoring & Continuous Improvement for Medical Voice Agents

Evaluation, Monitoring & Continuous Improvement for Medical Voice Agents

We devised a 3 pillar approach called the EMCI framework for ensuring safety and accuracy of our medical voice agents.

We developed the Evaluation, Monitoring, and Continuous Improvement (EMCI) framework to address the unique challenges of using voice-enabled AI in the highly regulated life sciences sector. The framework is designed to protect patient safety and ensure regulatory compliance, as even a single off-label statement or the mispronunciation of a drug name can lead to clinical miscommunication or regulatory scrutiny. It involves using LLM-based judges and human subject matter experts to ensure the agent sounds professional and adheres strictly to approved medical consensus.

In production, the framework relies on operational telemetry and feedback loops to maintain high standards and detect issues like model drift real-time. Monitoring includes tracking voice Quality of Service (QoS), analyzing user behavior for signs of frustration, and flagging potential privacy or compliance risks immediately. These insights drive a continuous improvement cycle where structured root cause analysis is used to prioritize fixes.

Evaluation, Monitoring & Continuous Improvement for Medical Voice Agents

Sep 5, 2025

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Increase in patient engagement

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Reduction in appointment cancellations

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Improvement in treatment adherence

We developed the Evaluation, Monitoring, and Continuous Improvement (EMCI) framework to address the unique challenges of using voice-enabled AI in the highly regulated life sciences sector. The framework is designed to protect patient safety and ensure regulatory compliance, as even a single off-label statement or the mispronunciation of a drug name can lead to clinical miscommunication or regulatory scrutiny. It involves using LLM-based judges and human subject matter experts to ensure the agent sounds professional and adheres strictly to approved medical consensus.

In production, the framework relies on operational telemetry and feedback loops to maintain high standards and detect issues like model drift real-time. Monitoring includes tracking voice Quality of Service (QoS), analyzing user behavior for signs of frustration, and flagging potential privacy or compliance risks immediately. These insights drive a continuous improvement cycle where structured root cause analysis is used to prioritize fixes.

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