METHODS TO MODEL CAUSE AND EFFECT FROM CLINICAL DATA

描述(改编自申请人的摘要)： 对临床行为与患者之间因果关系的认识 结果对于提高质量和 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 在理论上是一种理想的因果关系检验，但在实践中它们可能是不可行的或 不切实际。可获得的观察性临床数据量很大 大于实验数据量，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，利用观测数据来增强我们的 对临床行为与患者关系的认识 结果。大多数当前的观测技术都依赖于这样一个假设 对行动和结果的所有重要混杂因素进行衡量，并 受控于。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，它假设 不同的临床医生组看到具有代表性的特定样本 病人群体。临床医生可以根据不同的情况进行分组 标准，包括过去的资源使用情况。如果接受这种治疗的患者 临床医生群体根据他们的资源使用情况或 他们的病人的结果，那么他们的差异是由于实践 变种。在这种情况下，我们可以分析 这些群体特有的临床行为。通过假设所有 重要的临床行为已经被测量，我们也可以限制在 一般的行为对临床结果的因果影响。 我们建议研究新的分析建模方法，使用 社区获得性肺炎、心肌炎患者的回顾性分析 脑梗塞、哮喘、慢性阻塞性肺疾病、充血性心脏 衰竭、蜂窝织炎和短暂性脑缺血发作。我们的研究将集中在 最初是根据出现这些情况的患者的记录 急诊部。我们将使用信息系统中的患者数据 在匹兹堡大学医学中心(UPMC)。我们计划测试一下 通过引导观测场来假定具有代表性的群体样本 在UPMC的临床环境研究。 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.