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Source note2 min read

Toward Trustworthy Large Language Model Agents in Healthcare

Source

arXiv

arxiv.org/abs/2607.05055v1

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Source

Plain-Language Brief

The paper presents CareConnect, a safety-focused AI chatbot designed to automate healthcare appointment scheduling. It uses large language models, retrieval-augmented generation, and safety guardrails. The system completed 91.8% of tasks with a median latency of 2.2 seconds, demonstrated high safety compliance, and reduced operational costs compared to manual scheduling. The source code is available at GitHub.

What the Document Says

  • The paper discusses CareConnect, an AI system for healthcare logistics automation.
  • CareConnect uses large language models (LLMs), retrieval-augmented generation (RAG), and safety guardrails.
  • It handles appointment booking, modification, cancellation, and facility information retrieval.
  • The system enforces strict scope constraints to prevent medical advice or diagnosis.
  • Safety-critical situations are managed through deterministic short-circuit mechanisms.
  • Experimental results show a 91.8% task completion rate, median latency of 2.2 seconds, 96.0% safety compliance, and an average operational cost of $0.0324 per appointment.
  • The findings suggest that carefully scoped and safeguarded LLM-based agents can automate complex healthcare workflows while maintaining safety guarantees and achieving cost efficiency.

Key Concepts Mentioned

  • AI chatbot
  • healthcare appointment scheduling
  • large language models (LLMs)
  • retrieval-augmented generation (RAG)
  • safety guardrails
  • task completion rate
  • latency
  • safety compliance
  • operational cost

Claims Made by the Document

  • CareConnect can automate healthcare logistics.
  • The system maintains safety guarantees.
  • CareConnect achieves substantial cost efficiency.

Evidence Used

  • 91.8% task completion rate
  • Median latency of 2.2 seconds
  • 96.0% safety compliance
  • Average operational cost of $0.0324 per appointment

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high administrative overhead. These inefficiencies constrain provider availability and degrade patient access to care. This paper presents CareConnect, a safety-first conversational agent for healthcare logistics automation that leverages large language model (LLM) function calling, retrieval-augmented generation (RAG), and layered deterministic safety guardrails. The system orchestrates eight domain-specific tools to support appointment booking, modification, cancellation, and facility information retrieval, while enforcing strict scope constraints that prohibit medical advice or diagnosis. Safety-critical situations are handled through deterministic short-circuit mechanisms for emergency detection and medical intent refusal. We evaluate CareConnect on a comprehensive benchmark of 680 task-oriented scenarios spanning end-to-end workflows, multi-turn interactions, and edge cases. Experimental results demonstrate a 91.8% task completion rate with a median per-request latency of 2.2 seconds, 96.0% safety compliance on the dedicated safety-critical evaluation subset, and an average operational cost of $0.0324 per appointment, yielding a significant cost reduction compared to manual human scheduling. These findings show that carefully scoped and rigorously safeguarded LLM-based agents can reliably automate complex healthcare operational workflows while maintaining safety guarantees and achieving substantial cost efficiency. The source code and system implementation are publicly available at https://github.com/Hadi-Hsn/CareConnect.