Auditable Emergency Triage for Maternal and Newborn Care in India
Quick summary
arXiv:2609.09356v1 Announce Type: cross Abstract: 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
Key takeaways
- arXiv:2609.09356v1 Announce Type: cross Abstract: 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.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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