Ghoshal-Datta Niru, Chernew Michael E, McWilliams J Michael
Niru Ghoshal-Datta, Harvard University, Boston, Massachusetts.
Michael E. Chernew (
Health Aff (Millwood). 2024 Dec;43(12):1638-1646. doi: 10.1377/hlthaff.2024.00169.
Payments to Medicare Advantage (MA) plans are adjusted by a risk-score model that is calibrated on diagnostic and demographic data from traditional Medicare beneficiaries and then applied to MA beneficiaries. If MA plans capture more diagnostic codes than traditional Medicare, they receive payment that is higher than the amount that would be spent in traditional Medicare. Although most previous research has focused on the coding practices of MA plans, less attention has been paid to the completeness of coding in traditional Medicare. We analyzed 2017-19 traditional Medicare claims data and MA encounter data to compare the persistence of diagnostic coding for sixteen chronic conditions. Our primary analysis found that the lack of persistent coding of these conditions in traditional Medicare accounted for 2.85 percentage points, or 22.3 percent, of the 2020 traditional Medicare/MA risk-score gap, translating to $8.1 billion in Medicare spending. In our most conservative sensitivity analysis, this discrepancy accounted for 9.8 percent of the total gap, although this was likely an underestimate, as it excluded acute conditions and incident chronic conditions. To resolve this discrepancy, a comprehensive approach addressing coding practices in both MA and traditional Medicare may be required.
向医疗保险优势(MA)计划的付款通过一种风险评分模型进行调整,该模型根据传统医疗保险受益人的诊断和人口统计数据进行校准,然后应用于MA受益人。如果MA计划获取的诊断代码比传统医疗保险更多,它们获得的付款就会高于传统医疗保险的支出金额。尽管之前的大多数研究都集中在MA计划的编码做法上,但对传统医疗保险编码完整性的关注较少。我们分析了2017 - 19年传统医疗保险理赔数据和MA就诊数据,以比较16种慢性病诊断编码的持续性。我们的主要分析发现,传统医疗保险中这些疾病编码缺乏持续性,占2020年传统医疗保险/MA风险评分差距的2.85个百分点,即22.3%,相当于医疗保险支出81亿美元。在我们最保守的敏感性分析中,这种差异占总差距的9.8%,不过这可能是低估了,因为它排除了急性病和新发慢性病。为了解决这种差异,可能需要一种全面的方法来处理MA和传统医疗保险中的编码做法。