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Auditable Emergency Triage for Maternal and Newborn Care in India

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

At Noora Health, our nurses answer more than 50,000 medical queries per month on our WhatsApp-based service that provides caregivers with on-demand support. Their most time-critical task is emergency triage: deciding which queries need immediate in-person attention. To support them, we built a system that uses a large language model (LLM) to classify whether a message is an emergency and provide a rationale for interpretability. But the system was opaque: analyzing mistakes meant reading reasoning chains for each message, which is infeasible at our scale. Prompt changes meant re-running a full

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Evidence & attribution

First collected: 2026-09-20T19:52:05.078Z. This is not the publication date.