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用医疗保险索赔数据识别血友病患者的方法

Stacey A Fedewa^1,2, Tyler W Buckner³, Lorraine Cafuir^1,2

¹Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, Georgia, USA.

Haemophilia : the official journal of the World Federation of Hemophilia

|December 22, 2025

概括

这项研究比较了在医疗保险索赔中识别血友病A (PwHA) 患者的三种方法,发现仅仅诊断代码可能会过分计算病例,而算法和治疗数据可能不足以代表非严重或女性患者. 为了更好的血友病研究,需要验证的算法.

科学领域:

医疗信息学医学信息学
血液学血液学血液学
流行病学流行病学

背景情况:

医疗索赔数据越来越多地用于罕见疾病研究,包括血友病.
在索赔数据中识别患有血友病A (PwHA) 的人缺乏标准化的方法.
现有的识别方法可能会产生不同的队列特征,影响研究有效性.

研究的目的:

评估和比较三种不同的方法的队列特征,以识别医疗保险索赔中的PwHA.
评估不同识别方法对所选患者队列的影响.
利用索赔数据,为血友病研究的改进方法的开发提供信息.

主要方法:

使用的医疗保险索赔数据从2000年到2020年.
采用了三种识别方法:A队列 (历史验证的算法),B队列 (血友病治疗收据) 和C队列 (≥2个血友病诊断代码隔30天).
分析了已识别的PwHA队列的人口统计和治疗特征.

主要成果:

总共有5,602名患有血友病A的独特受益者被确定.
群体C (诊断代码) 包括76.7%的受益者,而群体A (算法) 和B (治疗) 分别包括16.1%和3.7%.
队列A和B主要由接受治疗的男性组成,而队列C的男性较少,记录治疗次数最小.

结论:

通用诊断编码可能会导致过度计数,可能包括未被确诊为血友病的个人.
基于算法和基于治疗的方法可能不足以代表患有非严重血友病的个体和女性.
需要现代,经过验证的算法来提高血友病研究索赔数据的准确性和实用性.

关键词:

算法算法是一种算法.凝血缺陷凝血缺陷凝血缺陷血友病 (Hemophilia) 是一种血友病.医疗服务研究医疗服务研究

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