Improving the Diagnosis of Liver Disease in Primary Care Patients with Abnormal Liver Function

摘要 改善肝功能检查异常的初级保健患者的肝病诊断 通过预测建模 减少诊断错误已被医学研究所确定为国家的首要任务。诊断 错误遍布整个医疗保健领域，平均每个人在一生中都会经历一次重大错误。 因此，改进诊断过程和减少诊断错误不仅对所有人都非常合适， 患者，但将在优化美国医疗保健服务的质量和价值方面发挥关键作用。 国家1 肝脏疾病，并发症包括急性肝衰竭，肝硬化和肝癌， 近年来，美国的年龄调整死亡率大幅攀升， 心脏病和癌症的死亡率已经下降。2尽管肝脏相关疾病的可预防性增加， 通过早期识别和治疗，慢性和终末期肝病的死亡人数继续增加， 上升3 传统的诊断过程是综合从病史、体格检查和 实验室检测在早期肝病的检测中表现不佳。4，5相反，临床医生更严重地依赖于 实验室研究，特别是肝功能检查（LFT）。6肝功能检查异常是最常见的 目前，初级保健临床医生目前缺乏能力， 一致地从这些异常中识别肝脏相关疾病。9 -12 初级保健的初步数据强调了这个问题的巨大范围;在欧洲的研究中， 在其他地方发现，近五分之一的人有异常。13，14在我们的初步研究中， 到40%的患者在学术初级保健诊所至少有一个异常LFT。此外，本发明还 对这些异常的肝脏测试进行不适当或不充分的跟踪。这些数据和我们自己的 经验表明，初级保健医生（PCP）缺乏可靠识别和准确识别 在这些异常LFT中诊断肝脏相关疾病。 在本提案中，候选人及其导师团队寻求利用跨专业团队合作， 信息技术，以减少诊断错误。他们将确定临床和人口统计学变量， 在初级保健中与特定肝脏相关诊断相关的异常LFT患者（目的1）。 此外，他们还将开发和验证一种预测模型，以识别具有异常LFT的患者， 肝脏相关诊断（目标2）。最后，他们将创建一个决策支持工具应用程序，以帮助PCP 面对异常LFT，以及时准确地诊断肝病（目的3）。

Abstract Improving the Diagnosis of Liver Disease in Primary Care Patients with Abnormal Liver Function Tests Through Predictive Modeling Reducing diagnostic error has been identified by the Institute of Medicine as a top national priority. Diagnostic errors pervade all of healthcare, with the average individual experiencing one major error during their lifetime. Therefore, improving the diagnostic process and reducing diagnostic error is not only highly appropriate for all patients, but will play a crucial role in optimizing the quality and value of healthcare delivery in the United States.1 Liver disease, with complications including acute liver failure, cirrhosis, and liver cancer ranks as a leading cause of death in America and over recent years has had a significant climb in age-adjusted mortality, while death rates from heart disease and cancer have fallen.2 Despite the increasing preventability of liver-related conditions through early recognition and treatment, the toll of chronic and end stage liver disease continues to rise.3 The traditional diagnostic process, a synthesis of information gathered from history, physical exam, and laboratory testing, performs poorly in the detection of early liver disease.4,5 Instead, clinicians rely more heavily on laboratory studies, and liver function tests (LFTs) in particular.6 Abnormal LFTs are among the most frequently encountered findings in medicine.7,8 Currently, primary care clinicians currently lack the ability to consistently identify liver-related disease from these abnormalities.9-12 Preliminary data in primary care emphasize the immense scope of the problem; in studies from Europe, LFTs have been found elsewhere to be abnormal in nearly 1 in 5 people.13,14 In our preliminary studies, we have up to 40% of patients seen in an academic primary care clinic possessed at least one abnormal LFT. Further, these abnormal liver tests are inappropriately or inadequately followed-up. These data and our own experience indicate that primary care physicians (PCPs) lack the resources to reliably identify and accurately diagnose liver-related diseases amongst these many abnormal LFTs. In this proposal, the candidate and his mentorship team seek to harness inter-professional teamwork and information technology to reduce diagnostic error. They will identify clinical and demographic variables of patients with abnormal LFTs associated with specific liver-related diagnoses in primary care (Aim 1). Additionally, they will develop and validate a predictive model to identify patients with abnormal LFTs at risk for liver-related diagnoses (Aim 2). Lastly, they will create a decision support tool application to aid PCPs confronted with abnormal LFTs to promptly and accurately diagnose liver disease (Aim 3).