探索利益相关者对使用人工智能用于诊断罕见和非典型感染的看法
Aysun Tekin1, Svetlana Herasevich2, Sarah A Minteer3
1Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Applied clinical informatics
|October 25, 2024
概括
临界护理提供者因患者因素评估不足而经历罕见感染的诊断延迟. 人工智能决策支持系统 (AI DSS) 是有前途的,但需要可靠性,可解释性和工作流集成才能克服警报疲劳.
科学领域:
- 关键护理医学 关键护理医学
- 传染性疾病 传染性疾病
- 医疗信息学医学信息学
背景情况:
- 诊断罕见和非典型感染的延迟是重症监护的重大挑战.
- 目前的诊断实践可能受到患者特定因素的评估和考虑不足的限制.
研究的目的:
- 评估重症监护人员在诊断罕见和非典型感染方面的观点.
- 评估人工智能 (AI) 作为决策支持系统 (DSS) 的潜力,以改善这些诊断.
主要方法:
- 在梅奥诊所罗切斯特的重症监护人员中进行了一项匿名的基于网络的调查.
- 该调查评估了罕见感染诊断和基于AI的DSS的经验,包括感知到的有用性,影响,风险和益处.
主要成果:
- 大多数提供者 (38/47) 同意诊断延迟,理由是患者因素评估有限.
- 虽然对AI DSS的熟悉度很高,但只有18/38人认为它们对一般患者护理具有持续的价值.
- 然而,34/47人认为AI DSS可以改善罕见感染诊断,强调可靠性,可解释性和工作流集成至关重要.
结论:
- 临界护理提供者认为罕见感染的诊断延迟,主要是由于患者的因素评估不足.
- 基于人工智能的DSS显示了改善罕见感染诊断的潜力,但可用性取决于可靠性,可解释性和工作流集成.
- 解决警报疲劳对于在重症监护机构成功实施AI DSS至关重要.
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