Time-to-Event Estimation with Unreliably Reported Events in Medicare Health Plan Payment
Medicare 医保支付中事件报告不可靠时的生存时间估计AI 解读 ↓
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Medicare 医保支付中事件报告不可靠时的生存时间估计
- 研究问题
- 如何在诊断事件报告不可靠时估计新增编码强度与可能的上调编码，并为 Medicare 编码行为方法提供可复现的评价数据？
- 方法与思路
- 作者提出考虑不可靠报告的事件时间估计量，使用两类医保人群的两年模拟编码数据评估，并开发生成纵向标记数据的 R 包。
- 材料陈述的贡献
- 摘要称，新估计量在模拟中恢复监测期内及期间的上调编码差异，对漏报的敏感性较低；软件支持编码方法的可复现开发与评价。
- 阅读关注点
- 应核对新增编码与上调编码的定义、误报漏报机制、模拟数据与 All of Us 资料的关系，以及真实数据应用所需的验证。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；核心评价使用模拟编码数据，摘要未提供误报机制细节，不能据此确认真实上调编码。
原文摘要
OBJECTIVE: To propose time-to-event estimators that help evaluate incident diagnostic coding and possible upcoding in Medicare as well as introduce an open-source software package that enables more reproducible methods development relevant to Medicare billing behavior. STUDY SETTING AND DESIGN: Observational analysis of simulated upcoding based on coding by insurers or providers that may be incentivized by Medicare Advantage risk adjustment. DATA SOURCES AND ANALYTIC SAMPLE: Two years of separately simulated incident health condition coding data for a Medicare Advantage population and a Traditional Medicare population where coding patterns are aligned with known practices in each program. PRINCIPAL FINDINGS: We propose several novel time-to-event estimators of incident coding intensity and possible upcoding in Medicare Advantage, including accounting for unreliable reporting. We demonstrate estimator performance in simulated data leveraging the National Institutes of Health's All of Us study and also develop an open source R package to simulate longitudinal realistic labeled upcoding data, which were not previously available for researchers. In simulations, our novel estimators recovered differences in upcoding within and across monitoring periods. Undercoding had a limited effect on our novel estimators while an existing estimator was more sensitive to undercoding. CONCLUSIONS: Our proposed estimators can help researchers and policymakers track new coding behaviors (e.g., as may be incentivized by risk adjustment formula updates) earlier and at scale while accounting for several real-world data considerations. Further, the R package we provide can be used to improve the development, accessibility, and reproducible evaluation of coding intensity and upcoding methodology.