# Improving Diagnosis in Emergency and Acute Care: A Learning Laboratory (IDEA-LL) > **NIH AHRQ R18** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $621,414 ## Abstract 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 de... ## Key facts - **NIH application ID:** 9991883 - **Project number:** 5R18HS026622-03 - **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR - **Principal Investigator:** Prashant Mahajan - **Activity code:** R18 (R01, R21, SBIR, etc.) - **Funding institute:** AHRQ - **Fiscal year:** 2020 - **Award amount:** $621,414 - **Award type:** 5 - **Project period:** 2018-09-30 → 2022-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9991883 ## Citation > US National Institutes of Health, RePORTER application 9991883, Improving Diagnosis in Emergency and Acute Care: A Learning Laboratory (IDEA-LL) (5R18HS026622-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9991883. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*