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

摘要 提高初级保健肝功能异常患者的肝病诊断率 通过预测性建模 减少诊断错误已经被医学研究所确定为国家的首要任务。诊断性 错误弥漫在所有的医疗保健中，平均每个人一生中都会经历一个重大错误。 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提供的质量和价值方面发挥关键作用 States.1 肝脏疾病，其并发症包括急性肝功能衰竭、肝硬变和肝癌，是主要的 美国和近几年的死因在年龄调整后的死亡率上有了显著的上升，而 心脏病和癌症的死亡率下降了2，尽管与肝脏相关的疾病的可预防性越来越高。 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 Rise.3 传统的诊断过程是从病史、体检和 实验室检测在检测早期肝病方面表现不佳。4、5相反，临床医生更加依赖 实验室研究，特别是肝功能测试(LFT)。6异常LFT是最常见的 医学上常见的发现。7，8目前，初级保健临床医生目前缺乏 始终如一地从这些异常中识别与肝脏相关的疾病。 初级保健的初步数据强调了问题的巨大范围；在来自欧洲的研究中，LFTs 在其他地方被发现有近五分之一的人异常。在我们的初步研究中，我们发现 在学术初级保健诊所就诊的患者中，有40%的人至少有一次LFT异常。此外， 这些异常的肝脏测试没有得到适当或充分的随访。这些数据和我们自己的 经验表明，初级保健医生(PCP)缺乏资源来可靠和准确地识别 在这些异常的LFT中诊断与肝脏相关的疾病。 在这项建议中，候选人和他的指导团队寻求利用跨专业的团队合作和 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 初级保健中与特定肝脏相关诊断相关的LFT异常的患者(目标1)。 此外，他们还将开发和验证预测模型，以确定患有异常LFTs的患者是否存在罹患 与肝脏有关的诊断(目标2)。最后，他们将创建一个决策支持工具应用程序来帮助初级保健医生 面对异常的LFTs，及时准确地诊断肝病(目标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).