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一个新的机器学习算法,用于创建风险调整的支付公式.

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此摘要是机器生成的。

这项研究开发了一种机器学习算法,使用诊断代码改进医疗保健支出预测. 新模型为决策者和临床医生提供了更加准确和透明的风险预测.

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科学领域:

  • 医疗保健服务研究 医疗服务研究
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 行政索赔数据对于医疗管理和支付至关重要,但在预测支出方面存在局限性.
  • 现有的模型很难将大量的诊断纳入其中,而不会激励不良的编码做法.
  • 需要先进的方法来提高医疗保健支出预测的准确性和透明度.

研究的目的:

  • 开发一种机器学习 (ML) 算法,自动创建临床可信和透明的医疗保健支出预测模型.
  • 该算法基于诊断项目 (DXI) 类别和诊断成本组 (DCG) 方法.
  • 目标是为决策者和临床医生提供更好的工具.

主要方法:

  • 诊断项目 (DXI) 被组织成疾病层次结构,并以适宜包括 (ATI) 评分来解决模糊性和可玩性.
  • 一个新的自动化DCG算法反复地将DXI分配给DCG,根据回归系数识别主导DXI.
  • 使用了Merative MarketScan商业索赔和遭遇数据库 (2016年1月至2018年12月),数据分为模型开发 (90%) 和验证 (10%).

主要成果:

  • 开发的算法实现了218个临床医生指定的层次结构,超过了美国卫生和人类服务部 (HHS) 层次状况类别 (HCC) 模型中的64个层次结构.
  • 基本模型排除了模糊和可玩的DXI,降低了80%的参数,并实现了0.535.5的R2.
  • 该模型预测罕见疾病的实际成本的12%内支出,显著超过HHS HCC模型,该模型为该群体支付的费用低于33%.

结论:

  • 自动化DXI集群在临床指定的层次结构内,可以从大数据集创建可解释的风险模型.
  • 该算法有效地解决了在预测建模中关于诊断模糊性和可玩性的担忧.
  • 这种方法提供了一种更准确,更透明的方法来预测医疗保健支出和结果.