Medicare健康计划支付中不可靠事件报告的时间到事件估计
Time-to-Event Estimation with Unreliably Reported Events in Medicare Health Plan Payment
AI总结：
本文提出了一种时间到事件估计器，用于评估医疗保险中的新诊断编码和可能的虚报，并介绍了一个开源软件包，以提高与医疗保险报销行为相关的可重复方法开发。
摘要：
OBJECTIVE: 为了提出有助于评估医疗保险中新诊断编码和可能虚报的时间到事件估计器，并介绍一个开源软件包，以促进与医疗保险报销行为相关的更可重复的方法开发。 STUDY SETTING AND DESIGN: 对基于保险公司或提供者编码的模拟虚报进行观察性分析，这些编码可能受到医疗保险经办机构风险调整的激励。 DATA SOURCES AND ANALYTIC SAMPLE: 两年期间分别模拟了医疗保险经办机构人口和传统医疗保险人口的新健康状况编码数据，其中编码模式与每个计划中已知的做法一致。 PRINCIPAL FINDINGS: 我们提出了几种新的时间到事件估计器，用于估计医疗保险经办机构中的新编码强度和可能的虚报，包括考虑不可靠的报告。我们利用国家卫生研究院的All of Us研究在模拟数据中展示了估计器的性能，并开发了一个开源的R包来模拟纵向的现实标记虚报数据，这些数据之前对研究人员不可用。在模拟中，我们的新型估计器恢复了不同监控期内的虚报差异。低估对我们的新型估计器影响有限，而现有的估计器对低估更敏感。 CONCLUSIONS: 我们提出的估计器可以帮助研究人员和政策制定者跟踪新的编码行为（例如，可能受到风险调整公式更新的激励）并以更大规模进行跟踪，同时考虑多个现实数据因素。此外，我们提供的R包可用于改进编码强度和虚报方法的开发、可及性和可重复性评估。
英文摘要：
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.
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