评估自然语言处理在确定三级/四级病例中的预测能力,在医院间转移优先级工作流程中进行评估
Timothy Lee1, Paul J Lukac2,3, Sitaram Vangala4
1Altamed Health Services, Commerce, CA, United States.
JAMIA open
|August 21, 2023
概括
与人类工作流相比,自然语言处理 (NLP) 模型在识别复杂的三级和四级 (TQ) 护理转移案例方面表现出更高的灵敏度. 这种人工智能驱动的方法有望在医疗保健转移中更早地发现关键患者需求.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 三级和四级 (TQ) 护理涉及对复杂患者病例提供高度专业化的医疗服务.
- 准确及时识别TQ病例对于高效的医院间转移到学术卫生中心至关重要.
- 预测TQ病例的现有人类工作流程可能在灵敏度和速度上有局限性.
研究的目的:
- 将自然语言处理 (NLP) 模型的预测性能与用于识别TQ病例的人类工作流进行比较.
- 评估NLP作为转移请求过程中的自动化决策支持工具的潜力.
主要方法:
- 从2020年7月1日到2020年12月31日期间,对1895例医院间转移病例进行了回顾性分析.
- 从电子健康记录中查询数据.
- 将NLP模型预测与人类工作流预测进行了比较,并证实了TQ结果.
主要成果:
- 通过NLP模型,曲线下的面积达到0.91.1.
- NLP模型的准确性 (81.5%) 与人类预测 (80.3%) 相当,但灵敏度明显高 (83.6%与67.7%).
- 该NLP模型发现了比人类工作流程更多的TQ病例15.9%.
结论:
- 在识别TQ病例方面,NLP模型可以与人类工作流程一样准确.
- NLP模型提供了显著提高的灵敏度,导致识别更多的TQ病例.
- 将NLP整合到医疗保健工作流程中可以提高在转移期间早期检测复杂的患者需求.
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