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

研究总结/摘要： 诊断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。在急诊科（ED）工作的临床医生特别容易受到 在混乱的环境中，由于时间紧迫的决策而导致的诊断错误。约1.41亿 每年艾德都会去美国。保守估计，成人中5%的诊断错误转化为~ 7 艾德的诊断错误有100万例，其中近一半可能对患者造成伤害。诊断错误 由不同患者之间复杂的相互作用（健康知识、主诉、复杂性等）引起， 提供者/护理团队（提供者的认知负荷、信息收集/综合等）和系统（健康 信息技术、拥挤、干扰等）因素为了减少艾德的诊断错误，我们必须 使用说明诊断过程中人-系统相互作用动态的方法。 我们的目标是创建“改善急诊和急性护理诊断-学习实验室”（IDEA- LL），这是一个使用可操作的、以患者为中心的数据进行诊断安全监测和干预的新项目 从护理和电子健康记录（EHR）的前线获得。IDEA-LL将使用多学科 设计、实施和评估干预措施的方法，以提高诊断安全性。调查 团队，由一个独特的医生，工程师的合作伙伴关系，将形成一个跨学科的环境，临床医生， 护士，病人，工程师，信息学家和设计师作为学习实验室的一个组成部分， 在学术和社区ED中解决儿科和成人急诊护理。 在目标1（识别）中，了解诊断决策和识别潜力的详细过程 导致诊断错误的因素我们提出了使用混合方法-扎根理论的迭代过程， 即结合定性（参与者观察，深入参与者访谈）和挖掘历史 数据我们将在两个学术和两个社区教育机构进行直接的实地观察， 诊断过程。我们将通过与艾德临床医生的利益相关者访谈来补充观察结果， 患者在诊断过程中获得对脆弱性的看法和看法。我们将补充 前瞻性观察，通过回顾性分析医疗记录， 与控制记录进行比较，以评估潜在的影响变量。在目标2中（设计和 开发），使用共识方法，我们将开发一个全面的名单，病人，提供者/护理团队 和系统一级的促成因素，并确定干预措施进行研究。排名潜力后 干预措施，使用以人为本的设计原则与输入的人为因素工程师，我们将隔离 患者、提供者/护理团队和以系统为中心的干预，用于迭代测试和部署，以提高疗效 在四个急诊室做测试在目标3（实施和影响）中，我们将测试 在4个急诊室使用混合方法进行干预，即定量和定性措施。

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.