在行政数据中识别产前护理的算法的开发和验证:对不良出生结果的预测有效性
Songyuan Deng1, Greg Barabell2, Kevin J Bennett1
1South Carolina Center for Rural and Primary Healthcare, University of South Carolina School of Medicine, Columbia, South Carolina, USA.
Health services research
|October 28, 2025
概括
一个新的算法准确地识别了医疗补助申请数据中的产前护理 (PNC) 使用情况. 与主要提供者持续照顾与较低的早产率和低出生体重有关.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 孕产妇和儿童的健康
- 医疗信息学 医疗信息学
背景情况:
- 产前护理 (PNC) 对积极的出生结果至关重要.
- 从索赔数据中准确识别PNC利用率和护理连续性是一项挑战.
- 现有的方法可能无法完全捕捉PNC提供商关系的细微差别.
研究的目的:
- 开发和验证用于使用索赔数据分配PNC遭遇的等级算法.
- 确保算法准确地反映了护理的连续性.
- 通过将提供者连续性与出生结果联系起来,评估算法的预测有效性.
主要方法:
- 南卡罗来纳州医疗补助受益人的回顾性队列研究 (2016-2021年).
- 开发了一个六步层次算法,使用专业,诊断/程序代码,并对住院/补充访问进行调整.
- 通过使用概括估计方程和交叉验证,通过检查主导提供者身份和不良出生结果之间的关联来评估预测有效性.
主要成果:
- 该算法成功地确定了98%的怀孕中至少有两次PNC遭遇的占主导地位的提供者.
- 主要提供者身份与早产风险降低 (aRR:0.68) 和低出生体重 (aRR:0.68) 有关.
- 该算法通过10倍的交叉验证证明了良好的模型合适性和性能.
结论:
- 一个经过验证的基于索赔的算法可以有效地识别PNC的使用和连续性.
- 通过占主导地位的提供者身份识别的护理连续性与改善的围产期健康结果有关.
- 这个算法为旨在优化PNC分娩和改善出生结果的干预提供了基础.
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