Missed acute myocardial infarction in the emergency department–standardizing measurement of misdiagnosis-related harms using the SPADE method

Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Adam L. Sharp ◽  
Aileen Baecker ◽  
Najlla Nassery ◽  
Stacy Park ◽  
Ahmed Hassoon ◽  
...  
Keyword(s):  
Emergency Department ◽  
Adverse Event ◽  
Chest Pain ◽  
Administrative Data ◽  
Diagnostic Performance ◽  
Event Rate ◽  
Public Health Problem ◽  
Diagnostic Error ◽  
Adverse Event Rate ◽  
Disease Pair

AbstractObjectivesDiagnostic error is a serious public health problem. Measuring diagnostic performance remains elusive. We sought to measure misdiagnosis-related harms following missed acute myocardial infarctions (AMI) in the emergency department (ED) using the symptom-disease pair analysis of diagnostic error (SPADE) method.MethodsRetrospective administrative data analysis (2009–2017) from a single, integrated health system using International Classification of Diseases (ICD) coded discharge diagnoses. We looked back 30 days from AMI hospitalizations for antecedent ED treat-and-release visits to identify symptoms linked to probable missed AMI (observed > expected). We then looked forward from these ED discharge diagnoses to identify symptom-disease pair misdiagnosis-related harms (AMI hospitalizations within 30-days, representing diagnostic adverse events).ResultsA total of 44,473 AMI hospitalizations were associated with 2,874 treat-and-release ED visits in the prior 30 days. The top plausibly-related ED discharge diagnoses were “chest pain” and “dyspnea” with excess treat-and-release visit rates of 9.8% (95% CI 8.5–11.2%) and 3.4% (95% CI 2.7–4.2%), respectively. These represented 574 probable missed AMIs resulting in hospitalization (adverse event rate per AMI 1.3%, 95% CI 1.2–1.4%). Looking forward, 325,088 chest pain or dyspnea ED discharges were followed by 508 AMI hospitalizations (adverse event rate per symptom discharge 0.2%, 95% CI 0.1–0.2%).ConclusionsThe SPADE method precisely quantifies misdiagnosis-related harms from missed AMIs using administrative data. This approach could facilitate future assessment of diagnostic performance across health systems. These results correspond to ∼10,000 potentially-preventable harms annually in the US. However, relatively low error and adverse event rates may pose challenges to reducing harms for this ED symptom-disease pair.

scholarly journals Microalbuminuria is associated with high adverse event rate following cardiac surgery

2011 ◽  
Vol 39 (6) ◽  
pp. 932-938 ◽  
Author(s):  
Martin Majlund Mikkelsen ◽  
Niels Holmark Andersen ◽  
Thomas Decker Christensen ◽  
Troels Krarup Hansen ◽  
Hans Eiskjaer ◽  
...  
Keyword(s):  
Adverse Event ◽  
Cardiac Surgery ◽  
Event Rate ◽  
Adverse Event Rate ◽  
High Adverse Event

scholarly journals LO016: Can we use administrative data to define an emergency department population at risk for pulmonary embolism? Development and validation of an algorithm to identify a research population

CJEM ◽  
2016 ◽  
Vol 18 (S1) ◽  
pp. S35-S35
Author(s):  
K. Burles ◽  
D. Wang ◽  
D. Grigat ◽  
E. Lang ◽  
J. Andruchow ◽  
...  
Keyword(s):  
Emergency Department ◽  
Pulmonary Embolism ◽  
Quality Improvement ◽  
Chest Pain ◽  
Administrative Data ◽  
Search Strategy ◽  
Search Algorithms ◽  
High Yield ◽  
Sensitivity Specificity ◽  
D Dimer

Introduction: Pulmonary embolism (PE) is a potentially life-threatening condition that is in the differential diagnosis of many emergency department (ED) presentations. However, no diagnostic code for suspected PE exists. Thus, identifying the population of patients undergoing PE workup from administrative data for use as a denominator in clinical research and quality improvement can be difficult. To overcome this, we used standardized triage complaint codes and investigations to develop search algorithms useful to identify patients undergoing PE workup from an administrative dataset. Our objective was to quantify the sensitivity, specificity, and case yield of these search algorithms in order to identify a superior search strategy. Methods: Hospital administrative data for adult patients (age ≥18 years), which included standardized triage complaint codes and ICD-10 diagnostic codes for PE, were obtained from four urban EDs between July 2013 to January 2015. Standardized triage complaint codes were evaluated for the proportion of patients diagnosed with PE. Combinations of high-yield presenting complaints, in combination with D-dimer testing or imaging orders, were evaluated for sensitivity, specificity, and predictive values for PE. Results: Of 479,937 patients presenting with 174 different complaints, 1,048 were diagnosed with PE. The best-performing search strategy was the combination of standardized CEDIS complaints of Cardiac Pain, Chest Pain (Cardiac Features), Chest Pain (Non-Cardiac Features), Shortness of Breath, Syncope/Pre-syncope, Hemoptysis, and Unilateral Swollen Limb/Pain, along with with D-dimer testing and/or CTPA, or V/Q scan. This combination captured 808 PE diagnoses for a sensitivity of 77.1% (95%CI 74.4-79.5%) and specificity of 86.8% (95%CI 86.7-86.6%). Conclusion: We identified a high-yield combination of presenting complaints and test ordering that can be used to define an ED population with suspected PE. This population of patients can be used as a denominator in research or quality improvement work that evaluates the utilization of diagnostic testing for PE.

scholarly journals Emergency Medical Services Provider Pediatric Adverse Event Rate Varies by Call Origin

2018 ◽  
Vol 34 (12) ◽  
pp. 862-865 ◽  
Author(s):  
David Jones ◽  
Matt Hansen ◽  
Josh Van Otterloo ◽  
Caitlin Dickinson ◽  
Jeanne-Marie Guise
Keyword(s):  
Adverse Event ◽  
Emergency Medical Services ◽  
Event Rate ◽  
Medical Services ◽  
Adverse Event Rate ◽  
Emergency Medical

scholarly journals Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data

2018 ◽  
Vol 27 (7) ◽  
pp. 557-566 ◽  
Author(s):  
Ava L Liberman ◽  
David E Newman-Toker
Keyword(s):  
Public Health ◽  
Big Data ◽  
Conceptual Framework ◽  
Diagnostic Performance ◽  
Diagnostic Errors ◽  
Diagnostic Error ◽  
Administrative Claims ◽  
Similar Data ◽  
Disease Pair ◽  
Pair Analysis

BackgroundThe public health burden associated with diagnostic errors is likely enormous, with some estimates suggesting millions of individuals are harmed each year in the USA, and presumably many more worldwide. According to the US National Academy of Medicine, improving diagnosis in healthcare is now considered ‘a moral, professional, and public health imperative.’ Unfortunately, well-established, valid and readily available operational measures of diagnostic performance and misdiagnosis-related harms are lacking, hampering progress. Existing methods often rely on judging errors through labour-intensive human reviews of medical records that are constrained by poor clinical documentation, low reliability and hindsight bias.MethodsKey gaps in operational measurement might be filled via thoughtful statistical analysis of existing large clinical, billing, administrative claims or similar data sets. In this manuscript, we describe a method to quantify and monitor diagnostic errors using an approach we call ‘Symptom-Disease Pair Analysis of Diagnostic Error’ (SPADE).ResultsWe first offer a conceptual framework for establishing valid symptom-disease pairs illustrated using the well-known diagnostic error dyad of dizziness-stroke. We then describe analytical methods for both look-back (case–control) and look-forward (cohort) measures of diagnostic error and misdiagnosis-related harms using ‘big data’. After discussing the strengths and limitations of the SPADE approach by comparing it to other strategies for detecting diagnostic errors, we identify the sources of validity and reliability that undergird our approach.ConclusionSPADE-derived metrics could eventually be used for operational diagnostic performance dashboards and national benchmarking. This approach has the potential to transform diagnostic quality and safety across a broad range of clinical problems and settings.

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