在紧急医疗服务中识别纳洛的数据通过处理非结构化的患者护理叙述而大大改善
Daniel R Harris1,2,3, Peter Rock4, Nicholas Anthony1,2
1Institute for Pharmaceutical Outcomes & Policy, Department of Pharmacy Practice and Science, College of Pharmacy, University of Kentucky, Lexington, Kentucky.
Prehospital emergency care
|January 9, 2025
概括
紧急医疗服务 (EMS) 的数据往往低于纳洛的管理. 将结构化数据与非结构化患者叙述相结合,可显著提高在阿片类药物过量用例中识别纳洛使用的准确性.
科学领域:
- 紧急医疗 紧急医疗
- 公共卫生监督 公共卫生监督
- 数据科学数据科学数据科学
背景情况:
- 紧急医疗服务 (EMS) 中的结构化数据字段经常用于识别阿片类药物过量服用,纳洛的使用是关键指标.
- 在2021年1月至2024年3月期间,在美国记录了大约120万例纳洛的使用情况.
研究的目的:
- 开发和验证一种方法来提高EMS数据中纳洛报告的准确性.
- 通过将结构化数据字段与非结构化的患者护理叙事集成来识别纳洛的使用.
主要方法:
- 分析了来自肯塔基州2019年全州数据库的30,000个EMS记录的随机样本.
- 应用正则表达式 (RegEx) 和自然语言处理 (NLP) 技术,从非结构化的患者护理叙述中提取纳洛相关信息.
- 对结构化和非结构化数据之间的差异进行了手动审查,以创建纳洛管理的总体指标.
主要成果:
- 结构化数据记录了纳洛在437个 (1.45%) 的记录中.
- 在RegEx方法中,从可接受的虚假阳性 (5.6%) 和虚假阴性 (4.2%) 率的叙述中确定了547次纳洛管理.
- 结合结构化数据和经过验证的非结构化数据,共确定了552名接受纳洛的患者,增加了115例 (26.3%).
结论:
- 非结构化的EMS患者护理叙述为准确的纳洛利用监测提供了关键数据.
- 将非结构化叙事分析与结构化数据相结合,可以显著提高纳洛管理的识别能力.
- 结合非结构化的EMS叙述的新监测方法对于防止低估纳洛使用和阿片类药物过量服用至关重要.
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