AI-Enabled Public Health Agent for SupportingFrontline Healthcare Workers in Primary CareSettings: Design, Evaluation, and Decision Support
Abstract
Abstract Background: Primary healthcare centres, community health centres, dispensaries, and nursing facilities play a vital role in healthcare delivery. However, frontline healthcare workers, nursing staff, healthcare assistants, and community health personnel often face increasing workloads related to patient interaction, symptom collection, referral coordination, documentation, and operational support. Artificial Intelligence (AI) offers opportunities to augment these healthcare workflows by providing rapid access to relevant healthcare information and decision-support resources. Materials and Methods: This study developed and evaluated a lightweight Retrieval-Augmented Generation (RAG)-based Public Health Agent designed to support operational healthcare workflows. The framework integrates domain-aware routing, semantic retrieval, structured healthcare repositories, deterministic physician and outpatient department (OPD) lookup, and rule-based triage mechanisms. Five healthcare repositories were incorporated, including Public Health, Doctor Registry, Patient Management, OPD Scheduling, and Case Triage datasets. The system was evaluated using a benchmark of 200 healthcare queries spanning public-health information, physician referral, patient management, appointment scheduling, and triage scenarios. Performance was compared against a retrieval-only baseline using retrieval effectiveness, response quality, groundedness, routing accuracy, triage accuracy, latency, and…
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