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.

项目概要医疗保健提供者概况分析对于识别极端（差或优秀）具有全国性的重要性。表现并根据需要进行干预，与特定医疗保健提供者相关的患者的结果政府和私人付款人都会定期进行监控，这种监控可以帮助患者赚更多钱。明智的决策，还可以帮助消费者、利益相关者和付款人确定供应商可能需要改进，甚至关闭或罚款那些结果极差的公司。我们的努力源于对终末期肾病 (ESRD) 的研究，该疾病占 7.2% 整个医疗保险预算，由于其对患者、家庭和医疗保健的沉重负担而受到关注用于分析大规模 ESRD 注册数据的现有分析方法假设风险调整。是完美的，提供者之间的差异完全是由于护理质量造成的，这通常是无效的。结果，这些方法不成比例地识别出较大的提供商，尽管它们不一定是“极端的”。为了解决这个问题，目标 1 开发了一种个性化的经验无效方法来分析医疗保健供应商要考虑由于不完善的风险调整而导致的无法解释的供应商之间的差异。全国透析数据包含超过 2,000,000 名患者的 3,000 多种合并症从 7,000 多家机构进行治疗，目标是选择重要的合并症指标进行风险调整。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重复出现时，样本数量和维度参数很大。对于中等样本量和低维，传统方法表现良好。国家透析数据集的另一个具有挑战性的方面是，数据无法扩展到如此海量的数据。如何整合复发事件数据又增加了患者信息的顺序更新。鉴于这些困难，目标 2 提出了一种基于嵌套分而治之的提升。使用大规模聚类循环事件数据进行高维变量选择的过程。所提出的程序进一步与基于时间相关的模型更新程序相结合 Kullback-Leibler 判别信息可集成流式循环事件数据。最后，COVID-19 大流行极大地改变了医疗保健的提供方式，统计学家目标 3 提出了在支持提供者和患者方面发挥重要作用。潜在疾病-死亡模型，用于解释提供者中新冠病毒流行率的时间和地理空间变化需要进行这种分析来更准确地评估提供者的表现，以帮助医生集中精力。风险过高的患者群体，并帮助提供者确定纠正措施以改善他们的表现。目标 4 的研究是开发公开可用的软件，以便能够利用所提出的接近。