NEW OBSERVATIONAL DATA ANALYSIS METHODS FOR COMPARATIVE EFFECTIVENESS RESEARCH

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果的医疗干预措施，以指导患者接受最佳治疗，并将我们的医疗资金引导到他们将最有效率的地方。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，如果不同干预措施或不同医院的疾病严重程度显著不同，使用存活比例来比较住院急性心肌梗死(AMI)患者在治疗或住院期间的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，如疾病严重性和导致不同死亡可能性的合并症。风险调整的一个标准方法是使用“观察到预期”比率的测量，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，该回归模型以前适合于据说代表所有患者的患者数据的标准或参考总体。观察的结果是从患者的出院数据中获得的。风险调整的目标是确定与预期结果相比，干预措施(或提供者)的平均观察结果是更好、更差还是相同。我们建议开发和发布一个开源医疗排名(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、统计学和数学的方法，这些方法以前没有在CER和卫生服务研究中结合应用。HCR算法根据患者级别数据的直接比较同时对两个或更多干预措施或提供者进行排名。这种算法不需要有一个参考数据库来进行观察与预期的比较。这项提议是华盛顿大学医学院(WUSM)系的调查人员共同努力的结果。医学生物统计咨询中心和北京医疗保健中心临床卓越中心(CCE)。BJC网络中有11家医院，拥有一个全面的患者级临床和管理数据信息系统，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、研究和投票理论的新的数学方法，以便对卫生保健干预和提供者之间的结果和表现进行更准确的比较。这个项目的重要性在于，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，指导患者获得最佳治疗和护理提供者，并将医疗保健资金引导到最具成本效益的选择上。

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.