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

改善急诊和急症护理的诊断：学习实验室 (IDEA-LL)

• 批准号: 10230984
• 负责人: Prashant Mahajan
• 金额: $ 62.14万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10230984
• 关键词: Improving; Diagnosis; Emergency; Acute; Care

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.

摘要/摘要： 诊断决策是一个高度复杂的认知过程，涉及不确定性，这使得 容易受到错误。在急诊部门（ED）工作的临床医生特别容易受到制作 由于混乱环境中的决策时间，诊断错误。约有1.41亿 在美国的年度ED访问。成人5％诊断错误的保守估计转化为〜7 急诊室中有数百万例诊断错误，几乎有一半的患者伤害。诊断错误 由各种患者之间的复杂相互作用（健康素养，提出抱怨，复杂性等）引起的， 提供者/护理团队（提供者的认知负载，信息收集/合成等）和系统（健康 信息技术，拥挤，中断等）因素。为了减少ED中的诊断错误，我们必须 使用说明诊断过程中人类系统相互作用动态的方法。 我们的目标是创建“改善紧急和急性护理中的诊断 - 学习实验室”（想法 - LL），一种使用可操作的，以患者为中心的数据的新型诊断安全监视和干预的计划 从护理和电子健康记录（EHRS）的前线获得。 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 由独特的物理工程伙伴关系领导的团队将形成临床医生的跨学科环境， 护士，患者，工程师，有限型的人和设计师是学习实验室不可或缺的方面 在学术和社区EDS中介绍小儿和成人急诊室。 在目标1（识别）中，了解诊断决策的详细过程并确定潜力 导致诊断错误的因素我们提出了使用混合方法的理论的迭代过程， 即结合定性（参与者观察，深入参与访谈）和历史挖掘 数据。我们将在两个学术和两个社区EDS上使用直接的原位观测来绘制整个 诊断过程。我们将补充利益相关者对ED临床医生的访谈和 患者获得诊断过程中脆弱性的观点和看法。我们将补充 通过对病历进行回顾性分析，这是触发阳性的，前瞻性观察 与控制记录进行比较，以评估潜在的贡献变量。在AIM 2（设计和 开发），使用共识方法，我们将制定全面的患者清单，提供者/护理团队 和系统级别的贡献因素，并确定要研究的干预措施。在排名潜力之后 干预措施，使用以人为因素工程师的意见为中心的设计原理，我们将隔离 患者，提供者/护理团队和以系统为中心的干预措施，以轻松进行迭代测试和部署 在四个EDS进行测试。在AIM 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，在4 eds处进行干预。

项目成果

Understanding diagnostic processes in emergency departments: a mixed methods case study protocol.

• DOI: 10.1136/bmjopen-2020-044194
• 发表时间: 2021-09-24
• 期刊: BMJ open
• 影响因子: 2.9
• 作者: Daniel M;Park S;Seifert CM;Chandanabhumma PP;Fetters MD;Wilson E;Singh H;Pasupathy K;Mahajan P
• 通讯作者: Mahajan P

Incorporating RTLS-Based Spatiotemporal Information in Studying Physical Activities of Clinical Staff.

将基于 RTLS 的时空信息纳入研究临床人员的身体活动。

• DOI: 10.1109/embc46164.2021.9630597
• 发表时间: 2021
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Enayati,Moein;Farahani,NasibehZanjirani;Chaudhry,AlishaP;Kapoor,Anoushka;Arunachalam,Shivaram;Walker,LauraE;Nestler,David;Pasupathy,KalyanS
• 通讯作者: Pasupathy,KalyanS

Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study.

• DOI: 10.2196/24642
• 发表时间: 2021-06-14
• 期刊: JMIR research protocols
• 影响因子: 1.7
• 作者: Enayati M;Sir M;Zhang X;Parker SJ;Duffy E;Singh H;Mahajan P;Pasupathy KS
• 通讯作者: Pasupathy KS