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从数据中获得智慧:在人工智能驱动的瘤护理时代,结构化数据的价值和持续的理由
Hamid Emamekhoo1, Irbaz B Riaz2, Daniel B Martin3
1University of Wisconsin Carbone Cancer Center, Madison, Wisconsin, USA.
Cancer
|February 17, 2026
概括
电子健康记录 (EHR) 包含碎片化,非结构化数据,阻碍了研究和患者护理. 由人工智能驱动的工作流可以自动化结构化数据捕获,提高EHR可用性和效率.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床数据管理 临床数据管理
背景情况:
- 电子健康记录 (EHR) 提高了效率,但由于非结构化数据输入而遭受数据碎片化.
- 电子健康记录中的非结构化数据限制了临床决策支持,研究 (RWE生成) 和质量测量.
- 数据碎片化使临床医生的工作流程,研究数据提取,EHR互操作性和基于价值的护理复杂化.
研究的目的:
- 为应对非结构化数据和电子健康记录中的碎片化所带来的挑战.
- 提出人工智能驱动的解决方案,用于自动化从非结构化EHR数据中生成结构化数据.
- 概述一项战略,以提高电子健康记录中的结构化数据捕获,以改善医疗保健结果.
主要方法:
- 审查当前的EHR数据挑战,重点关注非结构化数据和碎片化.
- 提出基于大型语言模型 (LLM) 的工作流程,用于自动化结构化数据生成.
- 强调平衡人工智能自动化与临床医生验证数据准确性的重点.
主要成果:
- 在临床护理,研究和管理方面确定了电子健康记录中非结构化数据的重大局限性.
- 建议人工智能 (LLM和环境监听) 作为自动化结构化数据捕获的解决方案.
- 强调需要各利益相关方协调努力,以实施人工智能创新.
结论:
- 人工智能技术,特别是LLM,为将非结构化EHR数据转换为结构化格式提供了一个有希望的方法.
- 自动化结构化数据捕获可以减少临床医生的负担,并提高各种医疗保健应用的数据实用性.
- 成功实施需要临床医生,研究人员,EHR供应商,付款人和决策者之间的合作,以使法规与AI进步保持一致.
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