重新构想以值为基础的护理:利用数据科学进行动态的临床医生级风险预测
Derek J Baughman1,2, Paul Nagy1,2, Chirag R Parikh1,3
1Responsible AI and Data Science Education (RAISE) Center, Johns Hopkins School of Medicine.
Current opinion in nephrology and hypertension
|February 3, 2026
概括
基于价值的支付 (VBP) 改革在改善慢性病 (CKD) 护理方面取得了有限的成功. 通过患者级,次年度风险预测来现代化VBP,可以更好地协调预防护理的激励措施.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 卫生经济学 卫生经济学
- 医疗信息学 医疗信息学
背景情况:
- 慢性病 (CKD) 带来了相当大的医疗保健成本.
- 当前的基于价值的支付 (VBP) 模式无法捕捉CKD护理的纵向性质,限制了质量和成本的改善.
- 现有的支付基准并不反映CKD的实时进展或干预措施的影响.
研究的目的:
- 评估CKD护理当前VBP模型的局限性.
- 探索先进建模的潜力,以改善CKD中的VBP.
- 为CKD管理提出下一代VBP框架.
主要方法:
- 利用临床信息学和预测建模方面的进步.
- 利用标准化的电子健康记录 (EHR) 和索赔数据.
- 进行纵向,患者一级的风险估计和分析.
主要成果:
- 半年度,对CKD结果和成本的患者级风险预测是可行的.
- 临床医师表现的纵向分析是通过标准化数据实现的.
- 短期预测可以揭示干预措施和结果之间的剂量反应关系,这些关系被当前的VBP所掩盖.
结论:
- CKD是开发下一代VBP的关键领域.
- 用次年风险预测进行患者级建模对于现代化支付框架至关重要.
- 未来的VBP政策应该优先考虑EHR原生,时间精确的评估系统,奖励预防性护理.
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