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Navigating specialty care often requires patients to understand referral reasons, appointment logistics, preparation rules, insurance requirements, and follow-up expectations. These instructions are frequently distributed across separate documents and portals, creating avoidable confusion for patients and caregivers. No unified system currently converts fragmented referral, clinic, insurance, preparation, and scheduling information into one personalized, plain-language care navigation guide. As a result, patients may miss critical steps before appointments or misunderstand what they need to do. This article proposes a conceptual large language model system for generating patient-friendly care navigation instructions from clinical, administrative, and scheduling data. The objective is to describe how such a system could support clearer, safer, and more accessible patient communication. The proposed pipeline would extract relevant facts from referral orders, clinic requirements, insurance rules, preparation instructions, and scheduling constraints. A retrieval-augmented LLM would then synthesize these facts into a cohesive instruction sheet with traceability back to verified institutional sources. Conceptually, the system would generate a clear, step-by-step appointment guide tailored to the patient’s language, health literacy needs, and preferred communication channel. The output would be expected to reduce cognitive burden by consolidating complex healthcare logistics into one practical message. An LLM-based patient navigation instruction system could bridge the communication gap between healthcare operations and patient understanding. Responsible deployment would require strong grounding, validation, accessibility design, and human oversight for high-risk instructions.

Patient access to timely, appropriate care remains a persistent challenge for health systems, affecting clinical continuity, patient experience, and operational performance. Artificial intelligence has been proposed as a means of automating and optimizing navigation functions from scheduling to follow-up coordination. This systematic review examined artificial intelligence applications for care navigation and patient access across scheduling optimization, referral management, eligibility screening, digital front-door tools, and follow-up coordination. The review also assessed implementation maturity, evaluation approaches, and equity-related reporting. A PRISMA 2020-compliant search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science for publications from 2017 through 2025. Dual screening, structured data extraction, risk-of-bias assessment, and narrative synthesis were used to characterize the evidence. The literature was concentrated in scheduling optimization, particularly no-show prediction and operational appointment management, and in digital front-door tools such as symptom checkers and triage chatbots. Referral and eligibility applications were emerging, while follow-up coordination models often overlapped with readmission and care-transition prediction. Few studies reported prospective implementation, comparative deployment outcomes, or equity impacts. Artificial intelligence-driven patient access tools appear technically robust for isolated tasks, but evidence that they improve integrated, end-to-end navigation remains limited. The field requires stronger prospective evaluation, equity assessment, and implementation reporting.

Asynchronous digital communication with patients has become a routine component of modern healthcare delivery. The rapid growth of patient portals, chatbots, and digital front-door tools has created opportunities for more responsive care, while also increasing communication workload for clinical teams. This systematic review examined artificial intelligence applications in digital patient communication from 2017 to 2026. The review focused on portal message triage, chatbot support, care navigation, automated response drafting, and patient engagement analytics. A PRISMA 2020-compliant review was conducted using structured searches of PubMed, Scopus, IEEE Xplore, and Web of Science. Records were screened by two reviewers, with data extracted on communication domain, AI approach, clinical setting, evaluation strategy, safety reporting, and implementation maturity. The literature was dominated by studies of chatbot support and portal message triage, with a growing body of work on large language model-enabled response drafting. Care navigation and patient engagement analytics were less frequently evaluated, and most studies emphasized technical performance, user satisfaction, or feasibility rather than health outcomes or workload reduction in real-world settings. AI for patient communication appears technically promising in isolated tasks, particularly message classification, chatbot interaction, and draft response generation. However, evidence remains limited regarding safe, equitable, and effective deployment across integrated communication workflows.