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NEW OBSERVATIONAL DATA ANALYSIS METHODS FOR COMPARATIVE EFFECTIVENESS RESEARCH

用于比较有效性研究的新观察数据分析方法

基本信息

批准号：
8036735
负责人：
WILLIAM D. SHANNON
金额：
$ 150万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8036735
关键词：
Acute myocardial infarction Algorithms Area Caring Case-Mix Adjustments Cessation of life Characteristics Clinical Comorbidity Computer software Consult Data Data Analyses Databases Equilibrium Goals Graph Health Personnel Health Services Research Healthcare Hospitals Informatics Inpatients Intervention Joints Length of Stay Mathematics Measures Medicine Methods Modeling Operations Research Outcome Patient Discharge Patients Performance Programming Languages Provider Reporting Research Personnel Risk Risk Adjustment Sampling Severity of illness Standardization Statistical Methods System Test Result Testing Translational Research Universities Voting Washington base comparative effectiveness cost cost effective data format design effectiveness research innovation interest medical schools method development mortality novel open source patient population programs provider intervention public health relevance social statistics theories tool

项目摘要

DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) is designed to identify healthcare interventions having the best patient outcomes to direct patients to receive the best treatment and to direct our healthcare dollars to where they will be most productive. When comparing observational data to determine the best intervention, CER requires that we apply risk or case-mix adjustment methods before examining outcomes of care. For example, to compare survival in treatment or hospital for inpatient acute myocardial infarction (AMI) patients using the proportion surviving may be misleading if the severity of disease is significantly different across interventions or hospital. To make comparisons valid, risk adjustment must balance patient factors, such as disease severity and co-morbidities, which result in different likelihood of death. A standard approach to risk adjustment is to use measures of "observed-to-expected" rates, where expected outcome for patients are estimated by an existing, often unknown and proprietary, regression model previously fit to a standard or reference population of patient data said to be representative of all patients. The observed outcome is obtained from the patient's discharge data. The goal of the risk adjustment is to determine if an intervention (or provider) on average shows better, worse, or the same observed outcomes compared to expected outcomes. We propose to develop and release an open-source HealthCare Rankings (HCR) case-mix adjustment software package combining methods from observational data analysis, operations research, statistics, and mathematics that have not been applied in combination previously in CER and health services research. The HCR algorithm ranks two or more interventions or providers simultaneously based on direct comparison of patient-level data. This algorithm avoids the need to have a reference database for observed-to-expected comparisons. This proposal is a joint effort of investigators in the Washington University School of Medicine (WUSM) Dept. of Medicine's Biostatistical Consulting Center and the BJC HealthCare Center for Clinical Excellence (CCE). There are 11 hospitals in the BJC network with a comprehensive informatics system of patient level clinical and administrative data available for developing and validating the HCR algorithm. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop and validate novel mathematical methods from operations research and voting theory to perform more accurate comparisons of outcomes and performance among health care interventions and providers. The importance of this project is that if successful there will be new data analysis tools for directing patients to the best treatment and providers for their care based on their level of disease severity and other patient characteristics, and for directing health care dollars to the most cost effective options.

描述（由申请人提供）：比较有效性研究（CER）旨在确定具有最佳患者结局的医疗保健干预措施，以指导患者接受最佳治疗，并将我们的医疗保健资金用于最有效的地方。当比较观察数据以确定最佳干预措施时，CER要求我们在检查护理结果之前应用风险或病例组合调整方法。例如，如果疾病的严重程度在干预措施或医院之间存在显著差异，则使用存活率比例来比较急性心肌梗死（AMI）住院患者的治疗或住院生存率可能会产生误导。为了使比较有效，风险调整必须平衡患者因素，如疾病严重程度和合并症，这些因素导致不同的死亡可能性。风险调整的标准方法是使用“预期达标率”的测量，其中患者的预期结果是通过现有的、通常未知的和专有的回归模型来估计的，该回归模型先前拟合到标准或参考人群的患者数据，该患者数据被认为是所有患者的代表。从患者的出院数据中获得观察到的结局。风险调整的目标是确定干预（或提供者）平均显示与预期结果相比更好，更差或相同的观察结果。我们建议开发并发布一个开源的医疗保健排名（HCR）病例组合调整软件包，该软件包结合了观察数据分析，运筹学，统计学和数学方法，这些方法以前在CER和卫生服务研究中没有结合应用。HCR算法根据患者水平数据的直接比较，同时对两个或多个干预或提供者进行排名。该算法无需使用参考数据库来进行观察值与预期值的比较。这项建议是华盛顿大学医学院（WUSM）部门研究人员的共同努力。医学的生物统计咨询中心和BJC医疗保健中心的临床卓越（CCE）。BJC网络中有11家医院拥有患者级临床和管理数据的综合信息系统，可用于开发和验证HCR算法。公共卫生相关性：该项目的目标是开发和验证来自运筹学和投票理论的新数学方法，以更准确地比较卫生保健干预措施和提供者之间的结果和绩效。该项目的重要性在于，如果成功，将有新的数据分析工具，根据患者的疾病严重程度和其他患者特征，指导患者接受最佳治疗和提供者的护理，并将医疗保健资金用于最具成本效益的选择。