VoCare AI: A Multi-Agent LLM Workflow for Improved Clinic Operational Efficiency

Abstract

In Singapore's polyclinics, touch-based self-service kiosks are widely used for administrative functions such as appointment scheduling and billing. However, these systems pose accessibility challenges for elderly patients, often resulting in increased staff workload and longer wait times. This paper presents VoCare AI, a voice-first conversational assistant designed to streamline healthcare administrative tasks through multiagent workflows powered by Large Language Models (LLMs). The system integrates a graph-based orchestration framework (LangGraph) with specialized agents that manage NRIC-based identity verification, appointment handling, billing queries, and general FAQ responses. It features a fine-tuned Automatic Speech Recognition (ASR) model adapted for Singapore-accented English (Singlish), trained on the IMDA National Speech Corpus to handle accent variability, code-switching, and elderly speech patterns. Evaluation results show that the best-performing ASR model achieved a Word Error Rate (WER) of 22.22%, while the Retrieval-Augmented Generation (RAG) module demonstrated strong performance with 1.00 groundedness and 0.92 retrieval relevance. By focusing on Singapore's linguistic diversity and incorporating localized authentication mechanisms, this work demonstrates how AI-driven conversational agents can enhance accessibility and operational efficiency in public healthcare environments. The complete source code for this work is publicly available at: https://github.com/DerrickLJH2000/langgraph-voice-assistant.

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