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Source note2 min read

Toward Trustworthy Large Language Model Agents in Healthcare

Source

arXiv

arxiv.org/abs/2607.05055v1

Read the source

Source

Plain-Language Brief

A study presents CareConnect, a safety-first conversational agent for automating healthcare appointment scheduling using large language models (LLMs), retrieval-augmented generation (RAG), and safety guardrails. The system achieves a high completion rate, low latency, and cost efficiency in managing various appointment scenarios while ensuring safety.

What the Document Says

  • The paper introduces CareConnect, an agent for healthcare logistics automation using LLMs, RAG, and safety guardrails.
  • It orchestrates eight domain-specific tools for booking, modifying, cancelling appointments, and retrieving facility information.
  • Strict scope constraints are enforced to prohibit medical advice or diagnosis.
  • Safety-critical situations are handled through deterministic short-circuit mechanisms.
  • The system was evaluated on a benchmark of 680 scenarios with a 91.8% completion rate, median latency of 2.2 seconds, 96.0% safety compliance, and an average cost of $0.0324 per appointment.
  • The code and implementation are publicly available at https://github.com/Hadi-Hsn/CareConnect.

Key Concepts Mentioned

  • Healthcare appointment scheduling
  • Large language models (LLMs)
  • Retrieval-augmented generation (RAG)
  • Safety guardrails
  • Task completion rate
  • Latency
  • Cost efficiency
  • Safety compliance
  • Source code

Claims Made by the Document

  • CareConnect reliably automates complex healthcare operational workflows
  • The system maintains safety guarantees
  • CareConnect achieves substantial cost efficiency

Evidence Used

  • 91.8% task completion rate
  • Median per-request latency of 2.2 seconds
  • 96.0% safety compliance on dedicated evaluation subset
  • 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.