Introduction:To date, many emergency department (ED)-based quality improvement studies and interventions for acute stroke patients have focused on expediting timesensitive treatments, particularly reducing door-to-needle time. However, prior to treatment, a diagnosis of stroke must be reached. The ED-based stroke diagnostic process has been understudied despite its importance in assuring high-quality and safe care. Methods:We used a learning collaborative to conduct a failure modes, effects, and criticality analysis (FMECA) of the acute stroke diagnostic process at three health systems in Chicago, IL. Our FMECA was designed to prospectively identify, characterize, and rank order failures in the systems and processes of care that offer opportunities for redesign to improve stroke diagnostic accuracy. Multidisciplinary teams involved in stroke care at five different sites participated in moderated sessions to create an acute stroke diagnostic process map as well as identify failures and existing safeguards. For each failure, a risk priority number and criticality score were calculated.Failures were then ranked, with the highest scores representing the most critical failures to be targeted for redesign.Results: A total of 28 steps were identified in the acute stroke diagnostic process.Iterative steps in the process include information gathering, clinical examination, interpretation of diagnostic test results, and reassessment. We found that failure to use existing screening scales to identify patients with large-vessel occlusions early on in their ED course ranked highest. Failure to obtain an accurate history of the index event, failure to suspect acute stroke in triage, and failure to use established stroke screening tools at ED arrival to identify potential stroke patients were also highly ranked. Conclusions:Our study results highlight the critical importance of upstream steps in the acute stroke diagnostic process, particularly the use of existing tools to identify stroke patients who may be eligible for time-sensitive treatments.
Introduction: Diagnostic error occurs in approximately 10% of acute stroke (AS) presentations. The diagnostic process includes history, physical examination, and test performance and interpretation. However, critical information for diagnosis is contained in unstructured clinical notes. Hypothesis: We hypothesized that natural language processing (NLP) can identify features in unstructured clinical notes associated with potential diagnostic error during ED “catch and release” (CR) encounters prior to AS admissions. Methods: Using a retrospective case-control design and ICD-10 codes, we identified index emergency department (ED) admissions with a diagnosis of first-time stroke (cases) and age and sex-matched gastroenteritis (controls) who had an ED CR encounter in prior 30 days. Notes were processed using cTAKES to identify concept unique identifiers (CUI) among clinical narratives from the CR encounters. Regression analysis was utilized to determine CUI terms from the CR encounter that were associated with stroke cases compared to controls. These CUI terms were grouped by clinical experts into 3 aspects of the diagnostic process: history (e.g., risk factors, medications, symptoms), neurologic examination (e.g., mental status exam, cranial nerves, pronator drift), and tests (e.g., labs, CT, MRI). Results: In an analytic cohort of 319 stroke cases and 319 gastroenteritis controls, a non-cerebrovascular neurologic diagnosis at the CR encounter was noted in 20.2% of cases versus 6.0% in controls (P<0.01). We identified 120 terms at the CR encounter associated with stroke (OR >2.0 and p<0.05). Grouped by themes, tests accounted for 50 (41.7%), examination for 37 (30.1%), and history for 33 (27.5%) terms. Terms related to neurologic examination had the highest median OR (median OR 6.7, IQR 2.7-11.5) followed by history (median OR 3.8, IQR 3.2-4.9) and tests (median OR 3.5, IQR 2.8-4.6). Conclusions: Neurologic presentations to the ED preceded 20% of stroke cases suggesting some of these may represent missed diagnoses for minor stroke and TIA. NLP may be a useful surveillance approach to identify neurologic symptoms, deficits, and tests present at CR encounters and trigger interventions to reduce diagnostic error prior to stroke.
Introduction: Acute stroke (AS) is a high-harm, high-cost condition that affects nearly 800,000 people/year in the US. Proven, time-sensitive treatments can reduce disability. However, to deliver an AS treatment, a timely and accurate diagnosis is first needed. Yet, diagnostic error is the most common type of error in AS, occurring in ~10% of AS patients, and higher in patients with mild or atypical presentations. Hypothesis: The identification, characterization, and ranking of failures of the AS diagnostic process, by clinicians who provide AS care in the ED, is an essential step prior to designing feasible, robust, and effective solutions to reduce AS diagnostic error. Methods: A Learning Collaborative (LC) of clinicians involved in the AS diagnostic process at 3 health systems in Chicago, IL participated in a Failure Modes, Effects, and Criticality Analysis (FMECA) to identify the steps in the AS diagnostic process, failures of each step and their underlying causes, and to characterize each failure’s frequency (F), impact (I) on making a timely and accurate AS diagnosis, and any existing safeguards (S), using standardized scores. A risk priority index (RPN=FxIxS) and a criticality number (CN=FxI) was calculated for each failure and rank ordered. Results: In a series (N=7) of 60-90 minute virtual sessions, the LC, comprised of Emergency Medicine (N=9), Neurology (N=10), and Radiology (N=1) clinicians of all professions (MD, RN, Technician) and levels (resident, attending, coordinator), created an AS diagnostic process map and risk table. The process map included 27 specific steps. The highest risk steps were failure to use a severe stroke/large vessel occlusion scale (RPN=432; CN=72); inability to establish patients’ last known well (RPN=384; CN=48); failure to use an AS screening scale (RPN=384; CN=54); lack of a “witness” of the event to confirm information (RPN=378; CN=42); and failure to recognize potential stroke and activate a stroke code at triage (RPN=288; CN=48). Conclusion: This study, for the 1 st time, reveals specific targets, particularly in the early phase of the AS diagnostic process, for which solutions should be designed (e.g., standardized process to use a severe stroke/LVO tool) to reduce AS diagnostic error.
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