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Improve Statistical Methods for Profiling of Healthcare Providers

改进医疗保健提供者概况分析的统计方法

基本信息

批准号：
10595024
负责人：
Zhi Kevin He
金额：
$ 32.95万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10595024
关键词：
Address Advanced Development Algorithms Budgets COVID-19 COVID-19 pandemic Caring Certification Cessation of life Computer software Computing Methodologies Data Data Set Databases Detection Dialysis procedure Dimensions Discrimination End stage renal failure Event Evolution Family Goals Government Grant Health Personnel Health Policy Healthcare Healthcare Systems Hospitalization Measures Medicare Methodology Methods Modeling Monitor Outcome Patient-Focused Outcomes Patients Performance Physicians Play Prevalence Privatization Procedures Provider Publications Quality Control Quality of Care Recurrence Research Research Personnel Risk Risk Adjustment Role Sample Size Saran Speed Statistical Data Interpretation Statistical Methods Stream Subgroup Suspensions Techniques Testing Time Transplant Surgeon United States Centers for Medicare and Medicaid Services Update Variant Writing clinical application comorbidity comorbidity Index coronavirus disease cost data registry data streams design discrete time health assessment high dimensionality improved innovation interest large-scale database mortality multiple data sources novel open source pandemic disease parallel computer routine provider theories transplant centers

项目摘要

PROJECT SUMMARY Healthcare provider profiling is of nationwide importance. In order to identify extreme (poor or excellent) performance and to intervene as necessary, outcomes of patients associated with specific healthcare providers are routinely monitored by both government and private payers. This monitoring can help patients make more informed decisions, and can also aid consumers, stakeholders, and payers in identifying providers where improvement may be needed, and even closing or fining those with extremely poor outcomes. Our endeavor is motivated by the study of end-stage renal disease (ESRD), which represents 7.2% of the entire Medicare budget and is of interest due to its heavy burden on patients, families, and the healthcare system. Existing profiling approaches for analyzing large-scale ESRD registry data assume the risk adjustment is perfect and the between-provider variation is entirely due to the quality of care, which is often invalid. As a result, these methods disproportionately identify larger providers, although they need not be “extreme.'' To address this problem, Aim 1 develops an individualized empirical null approach for profiling healthcare providers to account for the unexplained between-provider variation due to imperfect risk adjustment. The national dialysis data contains more than 3,000 comorbidities from over 2,000,000 patients who are treated from more than 7,000 facilities. The goal is to select important comorbidity indexes for risk adjustment of provider profiling. However, the use of large-scale databases introduces computational difficulties, particularly when the event of interest is recurrent, and the numbers of sample size and the dimension of parameters are large. Traditional methods that perform well for moderate sample sizes and low-dimensional data do not scale to such massive data. Another challenging aspect of the national dialysis dataset is that patient information is updated sequentially. How to integrate streaming recurrent event data adds another level of difficulty. In view of these difficulties, Aim 2 proposes a nested divide-and-conquer-based boosting procedure for high-dimensional variable selection with large-scale clustered recurrent event data. The proposed procedure is further combined with a model updating procedure based on the time-dependent Kullback-Leibler discrimination information to integrate streaming recurrent event data. Finally, the COVID-19 pandemic has dramatically changed how healthcare care is delivered, and statisticians have an important role to play in supporting providers and patients through this evolution. Aim 3 proposes a latent illness-death model to account for temporal and geospatial variation of COVID prevalence in the provider profiling. This analysis is needed to evaluate provider performance more accurately, to help physicians focus on groups of patients with excess risk, and to aid providers in determining corrective actions to improve their performance. The research in Aim 4 is to develop publicly available software to enable the utilization of the proposed approaches.

项目总结医疗保健提供者概况在全国范围内都很重要。为了识别极端的(差的或优秀的) 与特定医疗保健提供者相关的患者的表现并在必要时进行干预经常受到政府和私人付款人的监督。这种监测可以帮助患者获得更多明智的决策，还可以帮助消费者、利益相关者和支付者识别供应商可能需要改进，甚至关闭或罚款那些结果非常差的公司。我们的努力是由终末期肾病(ESRD)的研究推动的，ESRD占全球整个联邦医疗保险预算，由于其对患者、家庭和医疗保健的沉重负担而引起人们的兴趣系统。现有用于分析大规模ESRD注册表数据的分析方法假定风险调整是完美的，提供者之间的差异完全是由于护理质量，这往往是无效的。作为一名结果，这些方法不成比例地识别出较大的供应商，尽管它们不必是“极端的”。至为了解决这个问题，Aim 1开发了一种个性化的经验归零方法，用于分析医疗保健提供商需要解释由于风险调整不完善而导致的提供商之间无法解释的差异。国家透析数据包含来自200多万名患者的3,000多例合并症从7000多家医疗机构接受治疗。目标是选择重要的共病指标进行风险调整提供商概况分析。然而，大规模数据库的使用带来了计算困难，特别是当感兴趣的事件是重复发生的，并且样本大小的数目和维度参数很大。适用于中等样本量和低维数据的传统方法数据不会扩展到如此海量的数据。国家透析数据集的另一个挑战方面是患者信息按顺序更新。如何集成流式循环事件数据又增加了一项难度级别。针对这些困难，目标2提出了一种基于嵌套分而治之的提升利用大规模聚集性经常性事件数据进行高维变量选择的程序。这个所提出的过程进一步与基于时间依赖的模型更新过程相结合 Kullback-Leibler判别信息，以整合流重复事件数据。最后，新冠肺炎疫情极大地改变了医疗保健的提供方式，统计学家们在通过这一演变支持提供者和患者方面发挥重要作用。目标3提出了一个潜伏期疾病-死亡模型解释提供者中COVID流行率的时间和地理空间变化侧写。需要这种分析来更准确地评估提供者的表现，以帮助医生专注于关于风险过高的患者群体，并帮助提供者确定纠正措施以改善其性能。目标4中的研究是开发公开可用的软件，以便能够利用拟议的接近了。