Accepted for/Published in: JMIR Human Factors
Date Submitted: Apr 15, 2024
Date Accepted: Dec 31, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Delphi Study on Requirement Analysis of AI Medical Software for EEG Seizure Monitoring in Digital Care pathway for Epilepsy: Healthcare Professionals’ Opinion
ABSTRACT
Background:
Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients' quality of life. Therefore, it is critical to track epileptic seizures, diagnose, and provide potential therapies to manage People or persons with epilepsy (PWE). To identify epileptic seizures EEG is helpful in the diagnosis, classification of the seizure type/epilepsy/epilepsy syndrome. Rarely ictal EEG can be recorded, most often interictal EEG, which can be abnormal or normal even in case of epilepsy. The current digital care pathway for epilepsy lacks the integration of AI-driven seizure detection, which could potentially enhance epilepsy treatment and management.
Objective:
this study aims to determine the system requirements for the creation and incorporation of the AI medical software for EEG seizure monitoring into the current digital care pathway for epilepsy. Users need assessment, system requirements elicitation, and prioritization are the primary areas of focus for this study.
Methods:
A four-round Delphi study using focus group discussion was used with experts from Oulu University Hospital to address the research questions and assess the proposed system requirements. Semi-structured interviews were carried out, and qualitative data analysis was done.
Results:
Based on the thematic analytics results, a guideline was provided for the integration of the proposed AI medical software for EEG seizure monitoring in digital care pathway for epilepsy.
Conclusions:
The study outcome presents a thorough strategy for improving the quality of care, the management of healthcare resources, and the use of AI and sensor technology in clinical settings. The results of this study assist the system developers in careful system design and development to fulfil the user need in digital care pathway for epilepsy.
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