评估用于识别Medicaid索赔数据中堕胎的算法状态之间的变化
Maria I Rodriguez1, Ashley Daly2, Kelsey Watson2
1Department of Obstetrics and Gynecology, Oregon Health & Science University; Center for Health Systems Effectiveness, Oregon Health & Science University.
Contraception
|December 22, 2025
概括
在医疗补助申请数据中识别堕胎的四种算法在各州之间显示出显著的变化. 这强调了在使用索赔数据来估计堕胎率时需要谨慎,并表明算法验证至关重要.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生殖健康 生殖健康
- 数据分析数据分析数据分析.
背景情况:
- 医疗补助申请数据经常用于研究堕胎发生率.
- 在索赔数据中识别堕胎的现有算法在他们的方法学上有所不同.
- 了解算法性能对于准确的公共卫生监测至关重要.
研究的目的:
- 通过四种不同的算法来评估堕胎识别的变化.
- 用Medicaid索赔数据来评估算法性能,这些数据来自涵盖所有迹象的堕胎的州.
- 确定导致堕胎识别差异的因素.
主要方法:
- 来自14个州的2020年医疗补助TAF数据的分析.
- 包括15-44岁的女性入学者.
- 使用诊断,程序 (CPT/HCPCS) 和药物 (NDC) 代码应用四个已发表的算法.
- 检查已识别的堕胎和代码使用模式的州级变化.
主要成果:
- 在算法和状态之间观察到的确定的堕胎的实质变化 (最大至最小比从2.09到138.59).
- 算法在依赖仅诊断,仅程序或组合代码方面有所不同.
- 对于堕胎的药物代码使用情况因州而异 (0.1%至32.6%).
- 没有一个算法在所有评估状态中显示出一致的性能.
结论:
- 在各州的堕胎识别算法性能中存在显著的异质性.
- 差异可能源于计费实践,医疗补助数据报告和算法设计的变化.
- 建议在使用索赔数据来估计堕胎率时谨慎使用;建议对健康记录进行算法验证.
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