Semi-parametric Statistical Methods for Predicting High-cost VA Patients Using High-Dimensional Covariates

背景：不断增长的需求和医疗保健成本使得迫切需要开发新的统计方法， 准确预测高成本VA患者和与高成本VA相关的重要风险因素的方法 成本前瞻性预测高成本患者的能力是控制成本的重要一步。 未来的医疗费用。同样重要的是要确定疾病领域，大大有助于 高医疗保健费用和其他风险因素，决策者可以通过未来的干预措施来解决这些问题。 卫生保健成本数据的特点是偏度和异方差的高水平。 VA数据库中收集的大量变量提供了丰富的信息，但同时 时间，对统计分析和计算提出了巨大的挑战。行政和 来自VA数据库的电子病历数据通常包含缺失数据。新的统计 我们提出的程序旨在利用VA中丰富的数据库来分析成本数据。它 采用并开发了最先进的高维半参数统计程序来处理 VA数据集的复杂性。 目标：本项目旨在开发一个高成本预测（HCP）系统，该系统采用新的 分析大型VA数据库的高维半参数统计方法和算法 缺失值和删失发生率。HCP系统识别潜在的高成本患者， 提供了未来成本的预测区间，并提出了成本控制的重要风险因素清单。 该项目的成果将帮助退伍军人事务部的研究人员和政策制定者设计有效的干预措施 以那些潜在的高成本患者为目标，在不牺牲护理质量的情况下降低他们的成本。的 该项目将与VA分析和商业智能办公室（OABI）密切合作， 在VHA内接受初级保健的患者的成本数据。特别是，我们将确定一组 可改变的风险因素（MRF）对于改善护理和降低成本同时重要。 我们的工作填补了VA医疗保健成本数据分析的一个重要空白领域。通过组合 在现有的医疗评估需求评分（CAN）系统的HCP系统，我们将 这是朝着建立一个数据驱动的决策支持系统的最终目标取得的重要进展。 方法：该项目将开发一种新的半参数程序来预测高成本患者。 我们提出的方法结合了高维协变量和非线性协变量效应 并解决了死亡审查的挑战，这提高了准确性， 造型的灵活性。它不需要离散化成本，因此充分利用了信息 包含在成本数据中。它不需要任何参数分布假设。另一个主要 该项目的贡献是，我们提出了加权半参数分位数回归基于新的 变量选择程序，可以同时识别和估计重大风险因素， 高维数据在缺失值的存在。我们的方法会让病人 数据集，该数据集结合了通过决策支持系统提供的数据库中的所有可用成本数据 系统（DSS）国家摘录。我们将把管理成本会计系统（MCA， 以前称为决策支持系统或DSS），具有三个VA数据库，包括：VA患者 治疗文件（PTF）; VA门诊文件（OCF）; VA受益人识别和 记录子系统死亡文件。我们将比较新提出的方法与现有的 方法使用VA数据和模拟数据。

Background: The rising demands and health care costs make it urgent to develop new statistical methods to accurately predict high-costs VA patients and important risk factors associated with high costs. The ability to prospectively predict high-costs patients is an important step toward controlling future health care costs. It is also important to identify disease areas that contribute significantly to the high health care costs and other risk factors which policy makers can target by future intervention. Health care cost data are characterized by a high level of skewness and heteroscedastic variances. The large number of variables collected in the VA database provides rich information, but at the same time, imposes great challenges for statistical analysis and computation. The administrative and electronic medical record data from VA databases often contain missing data. The new statistical procedure we propose aims to take advantage of the rich databases in VA for analyzing costs data. It employs and develops state-of-art high-dimensional semiparametric statistical procedures to handle the complexity of VA data sets. Objectives: The project aims to develop a High Costs Prediction (HCP) system, which employs novel high-dimensional semiparametric statistical methods and algorithms to analyze large VA database with missing values and occurrence of censoring. The HCP system identifies potential high-costs patients, provides prediction intervals of future costs, and suggests a list of important risk factors for cost control. The outcomes of the project will help VA researchers and policy makers design effective interventions to target those potential high-cost patients and reduce their costs without sacrificing quality of care. The project will collaborate closely with VA Office of Analytics and Business Intelligence (OABI) to analyze costs data for patients receiving primary care within VHA. In particular, we will identify a set of modifiable risk factors (MRF) that are simultaneously important for improving care and reducing costs. Our proposed work fills in an important blank area of VA health care costs data analysis. By combining the HCP system with the existing Care Assessment Needs Scoring (CAN) system, we will make important progress toward the ultimate goal of building a data-driven decision support system. Methods: The project will develop a novel semiparametric procedure for predicting high costs patients. The approach we propose incorporates high-dimensional covariates and nonlinear covariate effects and addresses the challenge of censoring by death, which improves accuracy and increases the flexibility of modeling. It does not require discretizing the cost and hence fully uses the information contained in the cost data. It does not require any parametric distributional assumption. Another major contribution of this project is that we propose weighted semiparametric quantile regression based novel variable selection procedures which can simultaneously identify and estimate significant risk factors for high-dimensional data at the presence of missing values. Our approach will develop a patient level dataset that combines all available cost data from the databases provided through the Decision Support System (DSS) National Extracts. We will link data from the Managerial Cost Accounting System (MCA, formerly Decision Support System or DSS) with three VA databases including: the VA Patient Treatment File (PTF); the VA Outpatient Clinic File (OCF); and the VA Beneficiary Identification and Records Locator Subsystem death file. We will compare the newly proposed methods with existing methods using both the VA data and simulated data.