在马萨诸塞州为医疗和社会复杂性付费 医疗补助 医疗补助
Matthew J Alcusky1, Eric O Mick1, Jeroan J Allison1
1Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester.
JAMA network open
|September 5, 2023
概括
大众健康的健康支付模式的社会决定因素改善了复杂患者的成本准确性. 这种新模式更好地考虑社会风险,确保为服务于弱势群体的医疗保健组织支付更公平的费用.
科学领域:
- 卫生经济学 卫生经济学
- 医疗保健政策 医疗保健政策
- 人口健康管理 人口健康管理
背景情况:
- 大众健康的初始支付模式并没有准确地为所有患者子组定价护理,特别是那些具有社会风险的患者.
- 现有的模型很难解释影响医疗保健成本的健康和社会因素的复杂相互作用.
研究的目的:
- 为群众健康会员开发和验证一个新的护理总成本模型.
- 确保支付模式准确地反映与面临复杂健康和社会挑战的会员相关的成本.
- 促进为服务于不同患者群体的负责任护理组织 (ACO) 提供公平补偿.
主要方法:
- 一项采用马萨诸塞州医疗补助计划 (MassHealth) 2016-2017年行政数据的横截面研究.
- 开发三个不同的护理总成本模型,其中模型3包含社会人口统计学特征,更新的风险得分和相互作用.
- 模型性能使用R2和观察到预期 (O:E) 成本比率进行评估,并根据前一年的数据进行验证.
主要成果:
- 改进后的Model 3表现出优越的性能 (R2=60.3%),与之前的车型 (52.1%和51.5%) 相比.
- 模型3在各种子组中实现了更准确的成本预测,包括住房不稳定,儿童和高发病率的子组.
- 该模型显著降低了为医疗和社会复杂度高的会员服务的ACO支付不足,O:E比率接近1.00.
结论:
- 对社会和医疗风险因素的准确建模对于公平的医疗保健支付至关重要.
- 开发的模型成功地提高了大众健康系统内的支付准确性和公平性.
- 这种方法减轻了对安全网医疗保健系统的支付不足,提高了他们为弱势群体提供服务的能力.
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