通过自动化大血管封闭检测算法改善工作流程取决于护理团队的参与度
Emmanuel C Ebirim1, Ngoc Mai Le2, Joseph N Samaha2
1The University of Texas Medical Branch at Galveston, Galveston, Texas, USA.
护理团队使用自动机器学习 (ML) 软件来检测大血管封闭 (LVO),通过每个患者的评论来衡量,显著减少了门到部 (DTG) 时间. 更高的用户参与度,而不仅仅是用户数量,与中风护理工作流程效率的提高有关.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 针对大血管阻塞 (LVO) 检测的自动化机器学习 (ML) 算法提高了医院内工作流程,特别是门到部 (DTG) 时间.
- 护理团队参与实现这些工作流改善的确切作用需要进一步澄清.
研究的目的:
- 分析护理团队使用基于ML的LVO检测软件与DTG时间的改进之间的关联.
- 为了确定软件用户的数量或用户交互的水平是否更为关键,以提高工作流程.
主要方法:
- 一个随机前性临床试验的后期分析,涉及四个综合性中风中心 (CSCs).
- ML软件的使用量被量化为活跃用户的总数和每周每站点的评论与分析患者的比例.
- 主要结果是与ML前实施相比,DTG时间的减少,按医院利用水平分层.
主要成果:
- 增加ML软件的使用,特别是更高的评论与患者比率,与DTG时间的显著减少有关.
- 评论与患者比率的每个单位增加与DTG时间的2.6分钟减少相关.
- 活跃用户的总数与DTG时间缩短没有显著的关联.
结论:
- 网站特定的DTG时间的改进与用户与ML软件的参与有关,而不是用户的数量.
- 在ML软件中的积极交互和评论似乎是优化LVO检测工作流效率的关键驱动因素.
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