人工智能预测账单代码级别的紧急情况部门遇到的人工智能
Jacob Morey1, Richard Winters1, Derick Jones1
1Department of Emergency Medicine, Mayo Clinic, Rochester, MN.
人工智能使用临床笔记和数据准确预测急诊部 (ED) 计费代码. 这个AI应用程序可以自动化ED编码,节省大量的管理时间和成本.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 准确的医疗计费对于医疗补偿至关重要.
- 紧急部门 (ED) 的编码复杂且耗时.
- 人工智能为医疗保健中的管理任务自动化提供了潜在的解决方案.
研究的目的:
- 开发和评估用于预测ED计费代码级别的AI模型.
- 用临床笔记,特征和订单来评估模型的性能.
- 识别影响计费代码预测的关键特征.
主要方法:
- 利用组合模型将自然语言处理和机器学习结合起来.
- 在2023年1月至9月期间,对321,893名成年ED接触者进行了训练.
- 使用可解释的AI (沙普利增量解释) 来识别重要的预测特征.
主要成果:
- 人工智能模型在预测计费代码4级和5级 (AUC 0.94-0.95,精度 0.80-0.92) 方面取得了很高的性能.
- 关键预测因素包括重症监护笔记,订单数量和出院情况.
- 在95%的值下,5级预测显示0.99精度和0.57回忆.
结论:
- 人工智能模型可以从临床数据准确地预测ED计费代码水平.
- 这项技术有可能自动化ED编码过程.
- 自动化可以大大节省行政成本和时间.
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