arXiv:2607.05055v1 Announce Type: new Abstract: 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-specifi
The increasing maturity of large language models (LLMs) and their function-calling capabilities, combined with persistent operational inefficiencies in healthcare, makes the development of 'safety-first' agents both feasible and necessary now.
This development indicates a tangible step towards deploying sophisticated AI agents in critical, regulated sectors, showcasing how LLMs can move beyond conversational interfaces to direct operational automation with safety guardrails.
The focus on 'safety-first,' RAG, and layered deterministic guardrails signifies a shift from experimental LLM applications to robust, deployable solutions for automating complex, multi-step workflows in sensitive environments like healthcare logistics.
- · Healthcare providers
- · AI agent developers
- · Patients
- · Cloud infrastructure providers
- · Legacy administrative software vendors
- · Manual administrative staff roles (routine tasks)
Healthcare appointment scheduling becomes significantly more efficient, reducing administrative overhead and improving patient access.
The successful deployment of safety-first LLM agents in healthcare will accelerate adoption and trust in similar AI solutions across other regulated industries.
Increased automation of logistical tasks could free up human healthcare personnel to focus on higher-value patient care, potentially reshaping healthcare employment and training needs.
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Read at arXiv cs.AI