METHODS TO MODEL CAUSE AND EFFECT FROM CLINICAL DATA

描述（改编自申请人摘要）： 了解临床行动与患者之间的因果关系 成果对于提高质量和 医疗保健的成本效益。 随机对照临床试验 （随机对照试验）提供了我们所拥有的最可靠的方法来建立和 量化临床因果关系。 虽然我们可以使用RCT来提供 在理论上是因果关系的理想检验，但在实践中，它们可能是不可行的， 不切实际 可用的临床观察数据量很大， 大于实验数据的数量，而观测数据是 越来越多地被记录在医院信息系统中。 我们需要 可靠的方法，利用观测数据，以增强我们的 了解临床行动与患者之间的关系 结果。 目前大多数观测技术都依赖于这样一个假设， 测量了行动和结果的所有重要混杂因素， 控制。 这个假设很难检验，因此， 由此得出的因果结论的有效性仍然存在疑问。 我们开发了一种新的基于计算机的分析方法，该方法假设： 不同的临床医生组看到特定的代表性样本， 患者人群。 临床医生可以根据各种 标准，包括过去的资源使用。 如果病人接受这样的治疗， 临床医生群体根据他们的资源使用或 他们的病人的结果，那么他们的差异是由于实践 变化. 在这种情况下，我们可以分析具体的变化， 临床行动的特点群体。 通过假定所有 重要的临床行动已经衡量，我们也可以限制在 一般情况下，这些措施对临床结局的因果影响。 我们建议研究新的分析建模方法， 社区获得性肺炎、心肌梗死患者的回顾性数据 梗塞，哮喘，慢性阻塞性肺疾病，充血性心脏 衰竭、蜂窝组织炎和短暂性脑缺血发作。 我们的研究将集中在 最初在患有这些疾病的患者的记录上， 急诊室。 我们将使用来自信息系统的患者数据 匹兹堡大学医学中心（UPMC） 我们计划测试 观测场代表性群体样本假设 在UPMC的临床环境中进行研究。 如果所提出的方法能够可靠地估计 临床医生对患者结果的行动，它将提供一个重要的新工具， 协助提高临床护理的质量和成本效益。

DESCRIPTION ( Adapted from the applicant's abstract): Knowledge of the causal relationships between clinical actions and patient outcomes is of paramount importance in improving the quality and cost-effectiveness of health care. Randomized controlled clinical trials (RCTs) provide the most reliable method we have for establishing and quantifying clinical causal relationships. While we can use RCTs to provide in theory an ideal test of causality, in practice they may be infeasible or impractical. The amount of available observational clinical data is much greater than the amount of experimental data, and observational data is being increasingly recorded in hospital information systems. We need reliable methods that make use of observational data to augment our understanding of the relationships between clinical actions and patient outcomes. Most current observational techniques rely on an assumption that all significant confounders of actions and outcomes are measured and controlled for. This assumption is difficult to test, and thus, the validity of the resulting causal conclusions remain in question. We have developed a new computer-based analytic method which assumes that different clinician groups see a representative sample of a particular patient population. Clinicians may be grouped according to a variety of criteria, including past resource use. If the patients treated by such clinician groups differ significantly according to their resource use or to the outcomes of their patients, then their differences are due to practice variations. In that case, we can analyze the specific variations in clinical actions that characterize the groups. By assuming that all significant clinical actions have been measured, we also can constrain in general the causal influence of the actions on clinical outcomes. We propose to investigate the new analytic modeling method using retrospective data on patients with community-acquired pneumonia, myocardial infarction, asthma, chronic obstructive pulmonary disease, congestive heart failure, cellulitis, and transient ischemic attacks. Our study will focus initially on the records of patients who present with these conditions to the Emergency Department. We will use patient data from information Systems at the University of Pittsburgh Medical Center (UPMC). We plan to test the assumption of representative group samples by conducting observational field studies in clinical environments at UPMC. If the proposed method is able to estimate reliably the influence of clinician actions on patient outcomes, it will provide a major new tool to assist in improving the quality and cost-effectiveness of clinical care.