阿尔伯塔省COVID-19住院病人的错误归因偏差使用入院算法
Tri Dinh1, Jordan Ross2, Samantha James2
1Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada.
概括
阿尔伯塔省阿尔伯塔省的情况
科学领域:
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
背景情况:
- 早期的COVID-19反应通常假定所有住院都是COVID-19相关的.
- 阿尔伯塔省卫生服务部实施了一种算法来分类COVID-19入院病例.
- 该算法旨在区分直接,贡献和无关的原因.
研究的目的:
- 为了评估COVID-19入院算法的精度.
- 为了比较算法判断的原因与专家医生审查.
- 识别和纠正住院数据中的错误归因偏差.
主要方法:
- 进行了一项质量改善倡议.
- 从2022年1月至2月的261例COVID-19住院病例进行了采样.
- 算法结果与一个由传染病医生的小组进行了比较.
主要成果:
- 医生审查发现39.9%的直接,17.2%的贡献和37.6%的无关COVID-19原因.
- 算法确定了42.9%的直接原因,24.5%的贡献原因,30.3%的无关原因.
- 观察到中等一致性 (科恩卡帕=0.475),算法过度归因无关病例.
结论:
- 招生算法表现出错误的归因偏差,特别是高估了导致的原因.
- 这些发现导致了算法改进,以提高精度.
- 公共卫生机构应验证COVID-19住院报告方法.
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