Auditable Emergency Triage for Maternal and Newborn Care in India
Source
Shobhit Jagga, Aman Dalmia, Niharika Priyadarshini, Neelima Devadas, Amrita K Prasen, Nikhil Nalin, Santhosh SJ, Sreeram Nurani Ramasubramanian, Muhammed Afeer K, Anubhav Arora
Author
Shobhit Jagga, Aman Dalmia, Niharika Priyadarshini, Neelima Devadas, Amrita K Prasen, Nikhil Nalin, Santhosh SJ, Sreeram Nurani Ramasubramanian, Muhammed Afeer K, Anubhav Arora
Date
Terms in this piece · Glossary
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
Why it matters
Splitting an opaque LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → triage call into symptom extraction plus a documented clinical decision tree made errors auditable and let the team skip full re-evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → on every prompt change, a pattern reusable for any production LLM decision system.
Key quotes
“we decomposed triage into two steps: an LLM extracts canonical symptoms and patient context from the query using a clinician-authored vocabulary, and a deterministic rule engine captures the scenarios that indicate an emergency”
“the new system has triaged 152,421 patient queries and flagged 28,535 (18.7%) as emergencies”
“Clinicians have also added 48 new rules since deployment, evidence of the faster correction loop we set out to build”
articleInstitution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both MissDaniel Palacios, Matthew Brady Neeley, Angel Adetomike Otto, Shalini Dhamodharan, John P. Woodhouse, Chi-fan Lin, Mark Zobeck, Zhandong Liu, Hyun-Hwan Jeong
articleBeyond the Traceback: Using LLMs for Adaptive Explanations of Programming ErrorsAlexandru-Radu Moraru, Shreyan Biswas, Ujwal Gadiraju
articleAgentR A Stateful and Recovery-Aware Software Architecture for LLM-based Auditable WorkflowsRiya Samanta, Bidyut Saha, Soumya Kanti Ghosh, Rajkumar Buyya