Improving Diagnosis in Emergency and Acute Care: A Learning Laboratory (IDEA-LL)

项目摘要/摘要： 诊断性决策是一个涉及不确定性的高度复杂的认知过程，这使得它 容易出错的容易出错的在急诊科工作的临床医生尤其容易受到 由于在混乱的环境中进行时间紧迫的决策而导致的诊断错误。大约有1.41亿人 艾德每年到美国访问一次。保守估计，成人中5%的诊断错误相当于~7% 急诊室的诊断错误有数百万例，其中近一半可能会对患者造成伤害。诊断错误 由于不同患者之间的复杂相互作用(健康素养、提出抱怨、复杂性等)， 提供者/关怀团队(提供者的认知负荷、信息收集/合成等)和系统(运行状况 信息技术、拥挤、干扰等)各种因素。为了减少急诊室的诊断错误，我们必须 使用说明诊断过程中人与系统交互的动态的方法。 我们的目标是创建“改善急诊和急性护理的诊断-学习实验室”(IDEA- 11)，这是一种使用可操作的、以患者为中心的数据进行诊断安全监控和干预的新程序 从保健第一线和电子健康记录(EHR)获得。Idea-LL将使用多学科 设计、实施和评估干预措施以提高诊断安全性的方法。调查性的 团队由独特的医生-工程师合作伙伴关系领导，将形成一个临床医生的跨学科环境， 护士、病人、工程师、信息学家和设计师作为学习实验室不可或缺的一个方面 在学术和社区急诊室解决儿科和成人急救问题。 在目标1(识别)中，了解诊断决策和识别潜力的详细过程 导致诊断误差的因素我们提出了一种使用混合方法的迭代过程--扎根理论， 即将定性(参与者观察、深入参与者访谈)和历史挖掘相结合 数据。我们将使用在两个学术和两个社区EDS的直接现场观测来绘制整个 诊断过程。我们将补充利益相关者对急诊临床医生的采访结果，并 患者在诊断过程中获得对脆弱性的观点和看法。我们将补充 通过对触发阳性的医疗记录进行回顾性分析进行前瞻性观察 与控制记录进行比较，以评估潜在的影响变量。目标2(设计和 开发)，使用协商一致的方法，我们将制定一份患者、提供者/护理团队的全面清单 和系统层面的贡献因素，并确定需要研究的干预措施。对潜力进行排名后 干预，使用以人为中心的设计原则和人为因素工程师的输入，我们将分离 患者、提供者/护理团队和以系统为重点的干预，用于迭代测试和部署以实现疗效 在四个急诊室测试。在目标3(实施和影响)中，我们将测试 使用混合方法，即定量和定性措施，对4个急诊室进行干预。

ROJECT SUMMARY/ABSTRACT: Diagnostic decision-making is a highly complex cognitive process involving uncertainty, which makes it susceptible to errors. Clinicians working in emergency departments (EDs) are particularly vulnerable to making diagnostic errors because of time-pressured decision-making in chaotic environments. There are ~ 141 million annual ED visits in the US. A conservative estimate of a 5% diagnostic errors in adults translates into ~ 7 million cases of diagnostic errors in the ED, with nearly half with potential for patient harm. Diagnostic errors result from a complex interplay between various patient (health literacy, presenting complaint, complexity, etc.), provider/care-team (cognitive load on providers, information gathering/synthesis, etc.) and systems (health information technology, crowding, interruptions, etc.) factors. To reduce diagnostic errors in the ED, we must use methods that illustrate the dynamics of human-system interaction during diagnostic process. Our goal is to create “Improving Diagnosis in Emergency and Acute care - Learning Laboratory” (IDEA- LL), a novel program for diagnostic safety surveillance and intervention using actionable, patient-centered data obtained from both frontlines of care and electronic health records (EHRs). IDEA-LL will use multidisciplinary approaches to design, implement and evaluate interventions to improve diagnostic safety. The investigative team, led by a unique physician-engineer partnership, will form a transdisciplinary environment of clinicians, nurses, patients, engineers, informaticians and designers as an integral aspect of the learning laboratory to address both pediatric and adult emergency care in academic and community EDs. In Aim 1 (identify), to understand the detailed process of diagnostic decision-making and identifying potential factors that lead to diagnostic errors we propose an iterative process using mixed methods-grounded theory, i.e. combining qualitative (participant observations, in-depth participant interviews) and mining of historical data. We will use direct in-situ observations at two academic and two community EDs to map the entire diagnostic process. We will supplement the observations by stakeholder interviews with ED clinicians and patients to obtain perspectives and perception on vulnerabilities in the diagnostic process. We will supplement prospective observation by conducting a retrospective analysis of medical records that were trigger positive to compare with control records to assess potentially contributing variables. In Aim 2 (design and development), using consensus methods we will develop a comprehensive list of patient, provider/care-team and system level contributory factors and identify interventions to be studied. After ranking potential interventions, using human-centered design principles with input from human-factors engineers, we will isolate patient, provider/care-team and system-focused intervention for iterative testing and deployment for efficacy testing at the four EDs. In Aim 3 (implementation and impact), we will test for effectiveness and impact of the interventions at the 4 EDs using mixed methods i.e. quantitative and qualitative measures.