Mathematica Policy Research, Cambridge, MA.
Mathematica Policy Research, Princeton, NJ.
Health Serv Res. 2018 Apr;53(2):991-1007. doi: 10.1111/1475-6773.12689. Epub 2017 Mar 27.
To identify the most robust methods for evaluating alternative payment models (APMs) in the emerging health care delivery system environment.
STUDY DESIGN (APPROACH): We assess the impact of widespread testing of alternative payment models on the ability to find credible comparison groups. We consider the applicability of factorial research designs for assessing the effects of these models.
The widespread adoption of alternative payment models could effectively eliminate the possibility of comparing APM results with a "pure" control or comparison group unaffected by other interventions. In this new environment, factorial experiments have distinct advantages over the single-model experimental or quasi-experimental designs that have been the mainstay of recent tests of Medicare payment and delivery models.
The best prospects for producing definitive evidence of the effects of payment incentives for APMs include fractional factorial experiments that systematically vary requirements and payment provisions within a payment model.
确定在新兴医疗服务提供系统环境中评估替代支付模式(APM)的最可靠方法。
研究设计(方法):我们评估了广泛测试替代支付模式对寻找可信对照组能力的影响。我们考虑了析因研究设计在评估这些模式效果方面的适用性。
替代支付模式的广泛采用可能会有效地消除将 APM 结果与不受其他干预措施影响的“纯”对照或对照组进行比较的可能性。在这种新环境下,析因实验相对于单模型实验或准实验设计具有明显的优势,后者一直是最近对医疗保险支付和交付模式进行测试的主要方法。
产生支付激励对 APM 影响的明确证据的最佳前景包括分数析因实验,该实验在支付模型内系统地改变要求和支付条款。