AI-Driven Language Assistance For Elderly Healthcare Communication: Advancing Personalization, Accessibility, And Clinical Outcomes Through Intelligent Natural Language Processing

Abstract

Linguistic barriers between older adults and clinical staff represent a structural impediment to equitable healthcare delivery that age-related hearing, cognitive, and visual changes render progressively more acute. This paper examines how artificial intelligence, specifically large language model (LLM)-based natural language processing (NLP) systems, can be deployed to mediate these barriers across inpatient, outpatient, and telehealth contexts. Drawing on a synthesis of peer-reviewed clinical literature, we characterize the communication vulnerability profile of older patients and propose a layered AI assistance architecture that integrates speech recognition, adaptive lexical simplification, multimodal output rendering, and real-time provider feedback. Evaluation evidence indicates that appropriately calibrated NLP systems can reduce patient misunderstanding events by margins exceeding 30% relative to unaided communication and improve medication adherence among cognitively impaired populations. We also analyze the regulatory and ethical constraints that govern deployment within HL7 FHIR- and HIPAA-compliant infrastructures, with attention to bias mitigation and digital equity. The resulting framework offers both a theoretical grounding and a practical implementation roadmap for clinicians, health informatics engineers, and policy makers seeking to leverage AI for elder communication support.