With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical Model (LMM), a generative pre-trained transformer (GPT) designed to guide and predict the broad facets of patient care and healthcare administration. The model is trained on medical event sequences from over 140M longitudinal patient claims records with a specialized vocabulary built from medical terminology systems and demonstrates a superior capability to forecast healthcare costs and identify potential risk factors. Through experimentation and validation, we showcase the LMM's proficiency in not only in cost and risk predictions, but also in discerning intricate patterns within complex medical conditions and an ability to identify novel relationships in patient care. The LMM is able to improve both cost prediction by 14.1% over the best commercial models and chronic conditions prediction by 1.9% over the best transformer models in research predicting a broad set of conditions. The LMM is a substantial advancement in healthcare analytics, offering the potential to significantly enhance risk assessment, cost management, and personalized medicine.

翻译：随着美国医疗支出接近5万亿美元（2024年国家医疗支出概况），且其中约25%被估计为浪费性支出（美国医疗系统浪费：成本估算与节约潜力），更精准预测风险与优化患者护理的需求日益迫切。本文提出大型医疗模型（LMM），这是一种基于生成式预训练Transformer（GPT）架构的模型，旨在指导并预测患者护理与医疗管理的多维层面。该模型基于超过1.4亿条纵向患者理赔记录中的医疗事件序列进行训练，采用从医学术语体系构建的专业化词汇表，展现出卓越的医疗成本预测与潜在风险因素识别能力。通过实验验证，我们证明LMM不仅在成本与风险预测方面表现优异，还能辨识复杂病症中的精细模式，并揭示患者护理中新颖的关联关系。在广泛病症预测任务中，LMM将成本预测精度较最佳商业模型提升14.1%，慢性病预测精度较研究领域最佳Transformer模型提升1.9%。LMM代表了医疗分析领域的重大进展，为风险评估、成本管理与个性化医疗的显著提升提供了新的可能。