医疗保险肿瘤护理包的成本和使用变化

S. Parente, L. Tomai
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引用次数: 1

摘要

背景:护理捆绑是一种新兴的卫生筹资创新,旨在改变护理的激励机制,旨在提高护理质量并促进更好地利用资源。2016年,联邦医疗保险概述了一项提案,通过预先确定的护理包改变门诊药物的联邦医疗保险报销。为了评估护理捆绑的潜力,我们研究了第一个全面的努力,肿瘤护理模式(OCM)。本文表明,OCM可能使用的肿瘤护理包在美国各地的每位患者的成本差异很大。方法:在本分析中,我们使用了五年(2010-2014年)的医疗保险5%有限服务索赔数据集(LDS)。分析中使用了所有七个索赔部分,包括:医生/承运人B部分、耐用医疗设备、门诊医院、住院病人、熟练护理设施、家庭保健和临终关怀。2014年,用于识别癌症捆绑患者的5%医疗保险受益人LDS样本总计为17,143。一个近似的全国估计数将是20倍的17,143,产生342,860名受益者。结果:我们对2010年至2014年三种最昂贵的医疗保险包(肺癌、前列腺癌和淋巴瘤)的医疗保险索赔的分析显示,美国各州之间的人均医疗保险包报销差异超过400%。此外,我们发现,在所有服务索赔类型的费用包中,报销的组合变化有意义。最后,我们表明,在患者层面上治疗最昂贵癌症的排名顺序与在社会层面上治疗最昂贵癌症的排名顺序无关。结论:人均癌症费用存在很大的地理差异,而前3种癌症费用并不一致。因此,基于全系统地理的决策可能不会产生一致的解决方案。因此,当以患者成本管理为目标时,政策制定将具有挑战性,特别是在创新可能比稳健和深思熟虑的成本控制战略发展得更快的医疗保健行业。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medicare Oncology Care Bundle Variation in Cost and Use
Background: Care bundling is an emerging health financing innovation to change the incentives of care, intended to improve quality of care and promote better resource use. In 2016, Medicare outlined a proposal for changing Medicare reimbursement for outpatient drugs through pre-determined care bundles. To gauge the potential for care bundling, we examine one of the first comprehensive efforts, the Oncology Care Model (OCM). This paper shows that the oncology care bundles likely used by OCM have large variation in cost per patient across the United States. Methods: For this analysis, we utilized five years (2010-2014) of the Medicare 5% limited data set (LDS) of fee for service claims. All seven claims segments were used in the analysis including: physician/carrier Part B, durable medical equipment,outpatient hospital, inpatient, skilled nursing facility, home health, and hospice. The 5% LDS sample of Medicare beneficiaries used to identify patients with cancer bundles totaled 17,143 in 2014. An approximate national estimate would be 20 times 17,143, yielding 342,860 beneficiaries. Results: Our analysis of Medicare claims for the three most expensive bundles (lung cancer, prostate cancer and lymphoma) from 2010 to 2014 shows over a 400% difference in per capita bundle reimbursement between US states. Furthermore, we found that the mix of reimbursements within all bundles of fee for service claim types varies meaningfully. Finally, we show that the rank order of most expensive cancers to treat at a patient level is not correlated with the most expensive cancers at a societal level. Conclusions: There is substantial geographic variation in per capita cancer costs that is not consistent for the top 3 cancer bundles. Therefore, policy-making based on system-wide geography will likely not produce a consistent solution. As a result, policy formulation will be challenging when patient cost management is a goal, especially in a healthcare sector where innovation is likely to move faster than robust and thoughtful cost containment strategies.
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