Evaluating EEG Requisition Quality and Clinical Factors Influencing Outcomes: A Retrospective Audit
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Inappropriate diagnostic testing using electroencephalography (EEG) not only burdens the healthcare system but is especially concerning in Canada, where EEG demand is rising alongside a nationwide shortage of technologists. Understanding factors that influence yield, as well as patterns in requisition quality and completeness, may help improve both clinical utility and resource allocation. METHODS: We performed a single-center retrospective cohort study analyzing EEGs performed at Kingston Health Sciences Centre from March to August 2024. Demographic and clinical data were collected from paper EEG requisitions and digital reports. Diagnostic yield was assessed across clinical variables, physician specialty and patient setting using descriptive statistics, logistic regressions and odds ratios. RESULTS: EEGs detected abnormalities in ∼40% of 1077 EEGs, mostly non-epileptiform. Yield was higher among older adults (60+ years), inpatients in critical care, emergency patients, and those on anti-seizure medications or sedatives. Indications like "neuro-prognostication" and "concern for non-convulsive status epilepticus" had high yield (100%), whereas "syncope/presyncope" had poor yield (12.8%). Sleep-deprived EEGs did not significantly differ in yield compared to routine EEGs. Over half (56%) of the EEG requisition forms were incomplete. Requisition completeness varied by physician specialty: neurologists were found least likely to complete requisition forms compared to other specialties. CONCLUSION: By identifying clinical factors, physician specialties and settings associated with low diagnostic yield, interventions can be tailored to improve requisition practices and guide efficient resource allocation.
Institutions: Queen's University, Queens University
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,550
OpenAlex ID: W7171553632
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