Jennifer Mendoza-Alonzo , José Zayas-Castro , Armin Lüer-Villagra
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We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, revenue for performance per team, and severity of admitted patients. We develop a mixed-integer programming formulation and analyze, using a 2k factorial design, the reimbursement models and the main elements of their adjusted P4P components (i.e., the probabilistic classification model coefficients and hospital admission threshold). The results indicate that the coefficients of the probabilistic classification model and the hospital admission threshold have a significant effect on the profit and revenue for performance per team. There is also a tendency of the PCF to admit less severe patients than the CPC+. Yet, the effects are more notable in the PCF payment model because the proportion of P4P in the total revenue under the CPC+ is minimal (16.5% versus </span><span><math><mrow><mo><</mo><mn>1</mn><mtext>%</mtext></mrow></math></span>). Similarly, the PCF’s downside is its sensitivity to P4P changes, displaying high variability in the output variables under analysis.</p></div>","PeriodicalId":46320,"journal":{"name":"Operations Research for Health Care","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.orhc.2021.100312","citationCount":"1","resultStr":"{\"title\":\"Controllable and non-controllable factors to measure performance in primary care practices under Medicare alternative payment models\",\"authors\":\"Jennifer Mendoza-Alonzo , José Zayas-Castro , Armin Lüer-Villagra\",\"doi\":\"10.1016/j.orhc.2021.100312\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). 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We develop a mixed-integer programming formulation and analyze, using a 2k factorial design, the reimbursement models and the main elements of their adjusted P4P components (i.e., the probabilistic classification model coefficients and hospital admission threshold). The results indicate that the coefficients of the probabilistic classification model and the hospital admission threshold have a significant effect on the profit and revenue for performance per team. There is also a tendency of the PCF to admit less severe patients than the CPC+. Yet, the effects are more notable in the PCF payment model because the proportion of P4P in the total revenue under the CPC+ is minimal (16.5% versus </span><span><math><mrow><mo><</mo><mn>1</mn><mtext>%</mtext></mrow></math></span>). 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引用次数: 1
摘要
我们分析了两种最近的医疗保险替代支付模式,综合初级保健加(CPC+)和初级保健优先(PCF)。这两种模式都包含按服务收费、传统资本化和按性能付费(P4P)组件。这些报销模式的主要目标是推进以价值为基础的护理。然而,由于P4P组成部分是基于不完全由实践控制的因素,增加了更健康患者的潜在入院率,并影响了小型初级保健实践的利润,因此这些模型带来了一些犹豫。我们修改了两个模型中的P4P分量,通过概率分类模型包括一个不可控制因子(分层条件类别得分)和一个可控因子(Bice-Boxerman护理连续性指数)来预测住院情况。本研究旨在确定调整CPC+和PCF报销模式中P4P部分对每个团队利润、每个团队绩效收入和住院患者严重程度的影响。我们开发了一个混合整数规划公式,并使用2k析因设计分析了报销模型及其调整后的P4P分量的主要元素(即概率分类模型系数和住院阈值)。结果表明,概率分类模型的系数和入院门槛对团队绩效的利润和收入有显著影响。与CPC+相比,PCF接收的重症患者也有减少的趋势。然而,这种影响在PCF支付模式中更为显著,因为在CPC+模式下,P4P占总收入的比例很小(16.5% vs . 1%)。同样,PCF的缺点是它对P4P变化的敏感性,在分析的输出变量中显示出很高的可变性。
Controllable and non-controllable factors to measure performance in primary care practices under Medicare alternative payment models
We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). Both models comprise fee-for-service, traditional capitation, and pay-for-performance (P4P) components. The main objective of these reimbursement models is to advance toward value-based care. However, the models confer some hesitations since the P4P component is based on factors not entirely controlled by the practice, increasing the potential admission of healthier patients and affecting the profit of small primary care practices. We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, revenue for performance per team, and severity of admitted patients. We develop a mixed-integer programming formulation and analyze, using a 2k factorial design, the reimbursement models and the main elements of their adjusted P4P components (i.e., the probabilistic classification model coefficients and hospital admission threshold). The results indicate that the coefficients of the probabilistic classification model and the hospital admission threshold have a significant effect on the profit and revenue for performance per team. There is also a tendency of the PCF to admit less severe patients than the CPC+. Yet, the effects are more notable in the PCF payment model because the proportion of P4P in the total revenue under the CPC+ is minimal (16.5% versus ). Similarly, the PCF’s downside is its sensitivity to P4P changes, displaying high variability in the output variables under analysis.