目前在美国医院中使用和评估人工智能和预测模型
Paige Nong1, Julia Adler-Milstein2, Nate C Apathy3
1Paige Nong (nong0016@umn.edu), University of Minnesota, Minneapolis, Minnesota.
Health affairs (Project Hope)
|January 6, 2025
概括
大多数美国医院使用预测模型,但许多医院不评估它们是否有偏见. 确保公平需要更好的工具和支持来评估医疗保健中的人工智能 (AI) 和机器学习模型.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在医疗保健中的使用越来越多,需要强大的评估框架.
- 确保预测模型是公平的,适当的,有效的,有效的和安全的 (FAVES) 对患者的结果和公平的护理至关重要.
- 目前医院AI/ML模型的治理实践需要审查.
研究的目的:
- 分析AI和预测模型在美国医院的利用和评估方法.
- 确定医院如何评估这些模型的准确性和偏差.
- 了解影响医院评估实践的因素,用于预测模型.
主要方法:
- 来自2023年美国医院协会年度调查信息技术补充的数据分析.
- 检查使用和评估人工智能和预测模型的报告实践.
- 统计分析以确定与模型评估相关的因素.
主要成果:
- 65%的美国医院使用预测模型,79%的医院从电子健康记录开发商那里获取.
- 61%的医院在当地评估模型准确性,但只有44%的医院报告了当地偏差评估.
- 医院开发自己的模型,拥有高运营利,或属于卫生系统的医院更有可能进行本地评估.
结论:
- 在美国医院对预测模型的偏差评估中存在很大的差距.
- 开发和实施标准化工具和支持评估FAVES原则至关重要.
- 需要进行政策干预,以促进安全和公平的AI采用,防止医疗保健中的数字沟.
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