De-implementation of inappropriate thyroid ultrasound

项目摘要/摘要 甲状腺超声的不适当使用是导致甲状腺癌过度诊断和 过度治疗，这涉及到高风险的程序和长期治疗，导致医疗， 心理社会和病人的经济困难。高分化甲状腺癌的年累计成本 据估计，美国的医疗保健规模将超过15亿美元，预计到2030年将达到35亿美元， 在甲状腺癌诊断5年后，每个患者的潜在成本为50,000美元。甲状腺癌是一种 是美国增长最快的癌症，但死亡率仍然很低。大约25%的新病例是 可归因于发现了即使留下来也不太可能造成伤害的小甲状腺癌 没有得到诊断和治疗。小甲状腺癌诊断的最大驱动力是在无症状的情况下使用TUTS 人，这是一种临床指南不鼓励的做法。尽管有建议，但TUTUS的普遍性 反对它表明有必要采取积极的战略来消除它。消除符合以下条件的做法的过程 不以证据为基础被称为去执行化。到目前为止，还没有研究提供可复制和有用的 卫生系统确定其TUTS做法的方法，还没有系统地评价 驱动它的多层面因素，以至于我们缺乏关于有针对性的、可接受的和可行的去产能的关键信息 实施战略。如果没有它们，过度使用将持续存在。为了填补这一空白，我们将利用 多学科团队，在计算机表型、机器学习和混合方面拥有丰富的经验 方法研究。我们还将使用两个独特的数据库：罗切斯特流行病学项目，一个医疗 记录链接系统，捕获来自27个县的全部人口的医疗保健信息 明尼苏达州和威斯康星州以及以患者为中心的临床研究网络(PCORnet)共享 将数据组织成标准结构的通用数据模型。有三个目标。目标1：使用代表 和两个PCORnet站点，以开发一种可复制的计算机表型来识别接受TUTS的患者。目标2： 使用4个PCORnet，以确定患者、临床医生和实践因素与TUTS相关 具有代表性的医疗实践样本。目标3：使用混合方法，了解因素并确定 患者、临床医生和卫生系统可接受的取消实施IATS的潜在策略 利益相关者。该提案响应了NOT-CA-20-021的目标，以探讨取消实施 在癌症护理的整个过程中，临床实践效率低下或价值低下。在本研究结束时，我们将 我开发并验证了一种计算机表型，以识别不同环境中的TUT，以及一份清单 可接受的策略可能会减少TUTES。这些发现将被广泛传播，并将为 研究方法-部署在不同的卫生系统中，并针对患者、临床医生和组织- 测试此处确定的取消实施战略的有效性。

PROJECT SUMMARY/ABSTRACT Inappropriate use of thyroid ultrasound (iTUS) is an important driver of thyroid cancer overdiagnosis and overtreatment, which involves high-risk procedures and long-term therapeutics that cause medical, psychosocial, and financial hardships for patients. Cumulative annual cost of well-differentiated thyroid cancer care in the U.S. has been estimated to exceed $1.5 billion and is projected to reach $3.5 billion by 2030, and the potential cost after 5 years of thyroid cancer diagnosis is $50,000 per patient. Thyroid cancer is one of the fastest-growing cancers in the U.S, but mortality remains very low. Approximately 25% of new cases are attributable to the identification of small thyroid cancers that are unlikely to cause harm if they were left undiagnosed and untreated. The biggest driver of small thyroid cancer diagnosis is iTUS use in asymptomatic people, a practice discouraged by clinical guidelines. The pervasiveness of iTUS despite recommendations against it suggests the need for active strategies to eliminate it. The process of eliminating practices that are not evidence-based is known as de-implementation. To date, no studies have provided a replicable and useful way for health systems to identify their iTUS practices, and there has been no systematic evaluation of multilevel factors driving it, such that we lack key information about targeted, acceptable, and feasible de- implementation strategies. Without them, overuse will persist. To fill this gap, we will leverage a multidisciplinary team with vast experience in computer phenotyping expertise, machine learning, and mixed method research. We will also use two unique databases: the Rochester Epidemiology Project, a medical record-linkage system that captures health care information from the entire population of 27 counties in Minnesota and Wisconsin, and the Patient-Centered Clinical Research Network (PCORnet) that shares a common data model to organize data into a standard structure. There are three aims. Aim 1: Using the REP and two PCORnet sites, to develop a replicable computer phenotype to identify patients receiving iTUS. Aim 2: Using 4 PCORnet sities, to identify patient, clinician, and practice factors associated with iTUS in a representative sample of healthcare practices. Aim 3: Using mixed methods, to understand factors and identify potential strategies for iTUS de-implementation acceptable to the patient, clinician, and health system stakeholders. This proposal is responsive to the objectives of NOT-CA-20-021 to explore de-implementation of ineffective or low-value clinical practices along the cancer care continuum. At the end of this study, we will have developed and validated a computer phenotype to identify iTUS across diverse settings, as well as a list of acceptable strategies likely to decrease iTUS. These findings will be broadly disseminable and will pave the way for studies—deployed in diverse health systems and targeting patients, clincians, and organizations—that test the effectiveness of the de-implementation strategies identified here.