Automating Critical Care EEG: Enhancing Accuracy, Efficiency, and Patient Outcomes in Critical Care

Comprehensive Summary

Contemporary neurocritical care settings are seeing a growing use of continuous EEG (cEEG) for data acquisition in the care of critically ill patients. The challenge of using cEEG for prolonged monitoring is the massive volume of data generated that requires expert clinical interpretation within a reasonable time frame. This is paired with the challenge of integrating cEEG findings with clinical data from the electronic medical record (EMR). This review discusses how emerging strategies including quantitative EEG (qEEG) and EMR harmonization can address these challenges. The qEEG reviews data collected by the cEEG and transforms it into statistical and visual displays that reduce overall cEEG review time. Other advantages of qEEG include detection of seizures and physiological deterioration, expanded usability by non-experts, and prognostic ability. Despite its benefits, the qEEG is recommended to be used alongside raw EEG data due to its limitations including variable sensitivity and high false-positive rates. The application of cEEG in critical care settings has revealed a potential relationship between findings of periodic and rhythmic patterns (RPPs) and neuronal injury. The use of automation and algorithms to analyze RPPs along with integrating clinical interpretations from the EMR using natural language processing models can inform our understanding of RPPs in seizure pathology. A prerequisite to successful integration of the growing advances in AI into the critical care setting requires the harmonization of various data points such as EEG, medication, and clinical documentation. Steps toward the harmonization of these domains include the use of order sets for medication, hybrid EMR note templates, and EEG repositories. Advances in the automation of EEGs paired with the harmonization of data points collected during patient care will allow for the meaningful processing of EEGs that will support clinical decision making in critical care settings.

Outcomes and Implications

The findings of this review highlight the potential that qEEG has to improve the review efficiency of raw EEG data which can improve the way we approach the care of critically ill patients. The review also exposes limitations of the qEEG implying that it does not yet have the capability to be used in place of analyzing raw EEG data. This review implies that harmonization of various datapoints involved in patient care are necessary for the integration of AI and automation into the critical care setting.