Large Language Models in Post-Acute and Long-Term Care: Bridging Promise and Practice

Post-acute and long-term care (PA/LTC) operates under sustained pressure from workforce shortages, multimorbidity, and a documentation burden implicated in clinician burnout.1 In 2026, the frontier large language model (LLM) landscape, led by Claude Opus 4.7, GPT-5.5, Gemini 3.1 Pro, and Meta Muse-Spark, with open-weight alternatives, such as Kimi K2.6, GLM-5.1, and DeepSeek-V4, offers million-token context, native multimodality, configurable reasoning, and standardized tool use.2 In this letter, frontier LLMs refer to the most advanced, general-purpose generative models at the leading edge of scale and capability, typically trained on hundreds of billions to trillions of parameters with broad performance across language, reasoning, and multimodal tasks and offered by a small number of well-resourced developers.