Yuan, Doyle

Abstract Objective To measure the relative levels of signal and noise in expert diagnosis of epilepsy. Methods Twenty multinational epileptologists independently reviewed 50 vignettes of adult and pediatric patients presenting with suspected seizure(s) on two separate occasions with a ≥30‐day washout period. Experts provided a diagnosis of epilepsy or non‐epilepsy based on clinical information and, if requested, routine EEG and neuroimaging data. Cases had an established clinical diagnosis of epilepsy or non‐epilepsy based on capture of habitual paroxysmal events on video‐EEG or long‐term clinical follow‐up. Experts' judgments were analyzed to decompose variability into different sources: signal (objective differences between cases), level noise (experts' bias toward over/under‐diagnosis), pattern noise (experts' idiosyncratic reactions to specific case features), and occasion noise (inconsistency across occasions). Results The probability of an expert making a different diagnosis for a given case on two different occasions was 16%. The probability of two different experts making a different diagnosis for the same case was 26%. Signal (case “difficulty”) accounted for 66–69% of total variation, with 31–34% attributable to noise. Level noise was the largest contributor in the absence of EEG/neuroimaging results (23%), while pattern noise dominated when test results were available (24%). Occasion noise contributed relatively little (1%) but was still sufficient to cause diagnostic reversals in 16–22% between occasions. Significance The degree of noise in expert diagnosis of epilepsy is substantial, stemming primarily from physicians' idiosyncratic interpretations of case features and variable dispositions toward over‐ or under‐diagnosis. Strategies to improve reliability are needed, including standardized data collection protocols and structured decision algorithms. For “difficult cases,” where expert reliability and accuracy are lowest, our findings support current clinical practice which favors early referral for video‐EEG monitoring over reliance on diagnostic anchoring. This diagnostic pathway may become more accessible with advances in EEG technology (e.g., wearable devices) and artificial intelligence.

Purpose: Delivering optimal care to patients with seizures and epilepsy requires all EEGs to be interpreted accurately and reliably. This study investigated neurology professionals' opinions on the ideal standards for EEG in clinical care. Methods: We developed an anonymous e-survey targeting practicing and trainee neurologists focused on participants' demographics, clinical practice characteristics, and views on optimal EEG standards of care—including whether an EEG certification test is needed and whether postresidency/fellowship training in EEG/epilepsy is necessary for neurologists who interpret outpatient/routine EEGs in practice. The survey was hosted by the Neurology Clinical Practice—Practice Current, and it was distributed online through the American Academy of Neurology, American Epilepsy Society, American Clinical Neurophysiology Society, and International League Against Epilepsy, and through social media. Results: Two hundred eighty-three responses were included: 119 from EEG/epilepsy-trained neurologists, 83 from non-EEG/epilepsy-trained neurologists, 75 from trainees, and 6 from advanced care providers. Most participants (78%) agreed that “an objective certification test of ability to interpret EEGs is needed for all those who interpret EEGs in clinical practice.” Most participants (71%) believed that outpatient/routine EEGs may be read only by neurologists with EEG/epilepsy training; this opinion was more prevalent among EEG/epilepsy-trained (83%) versus non-EEG/epilepsy-trained neurologists (55%). Conclusions: Our neurology community should discuss the need to develop and implement a certification test of ability for all neurologists who wish to interpret EEGs in clinical practice. In addition, it is imperative to improve in-residency EEG education to ensure that neurology graduates achieve EEG competence before entering the workforce.