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.

翻译：目的：提出有助于评估医疗保险中事件诊断编码与可能的上报编码行为的时间至事件估计量，并介绍一个开源软件包，以推动与医疗保险计费行为相关的可重复方法学开发。研究背景与设计：基于医疗保险优势计划的风险调整激励机制下，对保险公司或提供者编码行为产生的模拟上报编码进行观察性分析。数据来源与分析样本：分别模拟了医疗保险优势计划人群和传统医疗保险人群两年内的事件健康条件编码数据，其中编码模式与各计划的已知实践一致。主要发现：我们提出了几种新型时间至事件估计量，用于评估医疗保险优势计划中的事件编码强度与可能的上报编码行为，包括对不可靠报告的考量。利用美国国立卫生研究院"全民健康"研究数据，我们展示了估计量在模拟数据中的性能，并开发了一个开源R包，用于模拟纵向、符合实际情况的带标签上报编码数据——此类数据此前研究者无法获取。在模拟中，我们的新型估计量成功恢复了监测周期内及不同周期间的上报编码差异。低报编码对新估计量的影响有限，而现有估计量对低报编码更为敏感。结论：所提出的估计量有助于研究者和政策制定者在考虑多项真实世界数据因素的前提下，更早、更大规模地追踪新型编码行为（例如风险调整公式更新可能引发的行为）。此外，我们提供的R包可用于改进编码强度与上报编码方法学的开发、可获取性及可重复评估。