JMIR Publications

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 and 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 the case of epilepsy. The current digital care pathway for epilepsy lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management.

Objective:

This study aims to determine the requirements of integrating data-driven medical software into the digital care pathway for epilepsy to meet the project's goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasizes ensuring that the proposed system is realistic and achievable. Perspectives on the feasibility of data-driven medical software that meets the project’s goals and demonstrates practical feasibility regarding resource availability, time constraints, and technological capabilities.

Methods:

A four-round Delphi study utilizing focus group discussions was conducted with seven diverse panels of experts from Oulu University Hospital to address the research questions and evaluate the feasibility of data-driven medical software for monitoring individuals with epilepsy. This collaborative approach fostered a thorough understanding of the topic and considered the perspectives of various stakeholders. Additionally, a qualitative study was carried out using semi-structured interviews.

Results:

The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing healthcare resources, and using AI and sensor technology in clinical settings. The potential of AI and sensor technology to revolutionize healthcare is exciting. The study has identified practical strategies, such as real-time EEG seizure monitoring, predictive modeling for seizure occurrence, and data-driven analytics integration to enhance decision-making. These strategies are aimed at reducing diagnostic delays and providing personalized care. We are actively working on integrating these features into clinical workflows. However, further case studies and pilot implementations are planned for future studies. The results of this study will guide system developers in the meticulous design and development of systems that meet user needs in the 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.