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

• 批准号: 10443230
• 负责人: Zhi Kevin He
• 金额: $ 32.99万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 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/10443230
• 关键词: Address; Advanced Development; Algorithms; Budgets; COVID-19; COVID-19 pandemic; Caring; 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; Lead; 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; base; clinical application; comorbidity; comorbidity Index; coronavirus disease; cost; data registry; data streams; design; discrete time; high dimensionality; improved; innovation; interest; large-scale database; mortality; multiple data sources; novel; open source; pandemic disease; parallel computer; patient oriented; routine provider; theories; transplant centers

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.

项目摘要 医疗保健提供者的档案具有全国性的重要性。为了确定极端（差或优） 性能和必要时进行干预，与特定医疗保健提供者相关的患者的结局 由政府和私人付款人进行例行监测。这种监测可以帮助患者 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。 我们的奋进是出于对终末期肾病（ESRD）的研究，ESRD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， 系统分析大规模ESRD登记研究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，这往往是无效的。作为 结果，这些方法不成比例地识别较大的供应商，尽管它们不必是“极端的”。''到 为了解决这个问题，目标1开发了一种个性化的经验零方法，用于分析医疗保健 供应商解释由于不完善的风险调整而导致的供应商之间无法解释的差异。 国家透析数据包含来自2，000，000多名患者的3，000多种合并症， 超过7,000家医疗机构提供治疗。其目的是选择重要的协方差指标进行风险调整 提供者配置文件。然而，大规模数据库的使用引入了计算困难， 特别是当感兴趣的事件是经常性的，和样本量的数量和维度， 参数很大。传统方法在中等样本量和低维情况下表现良好 数据无法扩展到如此庞大的数据。国家透析数据集的另一个挑战是， 患者信息被顺序地更新。如何整合流式循环事件数据增加了另一个 难度级别。鉴于这些困难，Aim 2提出了一种嵌套的基于分治的Boosting 大规模聚集性复发事件数据的高维变量选择过程。的 所提出的程序进一步结合模型更新程序的基础上，时间依赖 Kullback-Leibler判别信息，以整合流式复发事件数据。 最后，COVID-19大流行极大地改变了医疗保健的提供方式， 在这一演变过程中，在支持提供者和患者方面发挥着重要作用。目标3提出了一个 潜在疾病-死亡模型，以解释提供者中COVID流行率的时间和地理空间变化 侧写需要进行这种分析，以更准确地评估提供者的绩效，帮助医生专注于 对具有过度风险的患者群体，并帮助提供者确定纠正措施，以改善其 性能 目标4的研究是开发公开可用的软件，以便利用拟议的 接近。