Johanna Annunen

BACKGROUND Digital care pathways are increasingly used to support chronic disease management, yet empirical evidence of their impact on treatment burden remains scarce. OBJECTIVE This study examines how a digital care pathway for epilepsy (DCPE) influences treatment burden and what are its effects on daily lives of persons with epilepsy METHODS A qualitative single-case study was conducted in a Finnish healthcare setting. Semistructured interviews were carried out with persons with epilepsy (n=18), caregivers(n=3), and healthcare professionals (n=7). Data were analyzed deductively using the Burden of Treatment Theory, focusing on care-related work, challenges, and impacts on well-being. RESULTS DCPE was perceived as convenient, easy to use, timesaving, and reducing treatment burden for most users. It was particularly suitable for routine communication once care relationship had been established, while face-to-face interactions remained important for newly diagnosed or in treatment change. DCPE supported routine medication management well. However, it did not address broader psychosocial impacts of epilepsy, and further development needs were identified. CONCLUSIONS DCPE can reduce treatment burden and improve access to care for most users, particularly in routine care. However, hybrid care models remain essential, as face-toface support was preferred in certain phases of care pathway. Future development should focus on the diverse needs of people with epilepsy and on addressing the psychosocial aspects of living with epilepsy.

In this paper, we present a scenario-based method for identifying stakeholders’ insights for sustainable digital healthcare by leveraging future digital healthcare scenarios and changing social sustainability drivers. This study aims to propose a scenario-based stakeholder insight method to support the development of inclusive and socially sustainable digital healthcare futures. The first eleven change drivers associated with social sustainability were extracted from the digital healthcare literature and presented to various stakeholders to rank their impact and predictability regarding the social sustainability of digital healthcare. Moreover, the proposed method is also designed to extract insights from stakeholders regarding the implementation of different future scenarios, (i) the consequences of various future scenarios, (ii) the risks and challenges of achieving the future scenarios, and (iii) recommendations for policymakers. The study’s stakeholders include medical doctors, digital healthcare researchers, developers, and digital health-related businesses. The results present the most decisive, impactful, and predictable scenario as “connected care for all,” an inclusive digital healthcare system accessible to all without digital exclusion. Moreover, results show the most impactful and uncertain change driver, as rated by stakeholders. The proposed method can be used to understand stakeholders’ social sustainability requirements, leading to a sustainable digital healthcare future.

[This corrects the article DOI: 10.2196/59558.].

UNSTRUCTURED 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 them, and provide potential therapies to manage people with epilepsy. Electroencephalography (EEG) is helpful in the diagnosis and classification of the seizure type, epilepsy, or epilepsy syndrome. Ictal EEG is rarely recorded, whereas interictal EEG is more often recorded, and the results can be abnormal or normal even in the case of epilepsy. The current digital care pathway for epilepsy (DCPE) lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management. This study aimed to determine the requirements for integrating data-driven medical software into the DCPE to meet the project’s goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasized 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 are presented. A 4-round Delphi study using focus group discussions was conducted with 7 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. In addition, a qualitative study was carried out using semistructured interviews Drawing from the findings of the thematic analytics, a detailed set of guidelines was created to facilitate the seamless integration of the proposed data-driven medical software for EEG seizure monitoring into the DCPE. These guidelines encompass system requirements, data collection and analysis, and user training, offering a comprehensive road map for the effective implementation of the software. The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing health care resources, and using artificial intelligence and sensor technology in clinical settings. The potential of artificial intelligence and sensor technology to revolutionize health care is exciting. The study 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 were 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 DCPE.

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 them, and provide potential therapies to manage people with epilepsy. Electroencephalography (EEG) is helpful in the diagnosis and classification of the seizure type, epilepsy, or epilepsy syndrome. Ictal EEG is rarely recorded, whereas interictal EEG is more often recorded, and the results can be abnormal or normal even in the case of epilepsy. The current digital care pathway for epilepsy (DCPE) lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management. Objective: This study aimed to determine the requirements for integrating data-driven medical software into the DCPE to meet the project’s goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasized 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 are presented. Methods: A 4-round Delphi study using focus group discussions was conducted with 7 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. In addition, a qualitative study was carried out using semistructured interviews. Results: Drawing from the findings of the thematic analytics, a detailed set of guidelines was created to facilitate the seamless integration of the proposed data-driven medical software for EEG seizure monitoring into the DCPE. These guidelines encompass system requirements, data collection and analysis, and user training, offering a comprehensive road map for the effective implementation of the software. Conclusions: The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing health care resources, and using artificial intelligence and sensor technology in clinical settings. The potential of artificial intelligence and sensor technology to revolutionize health care is exciting. The study 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 were 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 DCPE.

Epilepsy is a chronic neurological disorder, requiring long-term treatment. The workload and impact of treatment causes a significant burden to patients. Digital care pathways may have potential for reducing treatment burden, but there also may be concerns of additional burden caused by digital healthcare. The aim of this study is to investigate the role of digital care pathway on treatment burden for patients with epilepsy. This was a single case study with the digital care pathway for epilepsy in the Wellbeing Services County of North Ostrobothnia (Pohde), in Finland, as a unit of analysis. The data was collected by observing an expert meeting of three clinicians. The meeting focused on five pre-defined domains of treatment burden: Medication burden, Time and travel burden, Financial burden, Social and emotional burden, and Healthcare access burden. The data was analyzed qualitatively and organized based on the pre-defined categories. The results suggest that the digital care pathway supports patients with treatment burden for all the pre-defined domains. Reported benefits include reduced travel, options for remote appointments, providing informational support and easier ways to contact healthcare professionals (HCPs). The main concerns clinicians had was could the use of digital care pathway cause rushed treatment decisions, difficulties of building trust and seeking support from HCPs, and difficulties of using the digital systems. A new theme emerged from the data, Diverse burdens, highlighting the variety of patients with epilepsy with differing needs for treatment.

The healthcare systems across the world are transitioning towards sustainable development of digital health solutions such as digital care pathway (DCP) to meet the growing needs of healthcare services. DCP is a digital health solution to provide facilities like making online appointments, ease of access to connect with a healthcare professional (HCP), sharing symptoms and tracking progress of the disease. In this paper, a team of researchers and healthcare professionals (HCP) examine and propose the sustainability goals for Digital Care Pathway for Epilepsy (DCPE) project from the economic, social, and environmental perspectives. The project is a DCPE for children, adolescents, and adult patients with epilepsy. The research methodology is a single case study approach based on the DCPE in Northern Ostrobothnia Wellbeing County (Pohde), in Finland as a unit analysis. Furthermore, the data was collected using feedback from a co-design session with four HCPs all working as clinicians in epilepsy care at Oulu University Hospital. The analysis from the co-design session resulted in various factors to achieve sustainability goals like reduction in travel to hospital, cost efficient, time saving, ease of access to connect with HCPs, and lower CO₂ emission. However, there are several challenges such as adaptation to the online treatment options, proper management, and scheduling of online appointments, and building trust in epilepsy patients for remote treatment. Despite the challenges, HCPs confirm that the DCPE can be useful for treating patients with epilepsy through remote consultation along with the traditional ways.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). 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 them, and provide potential therapies to manage people with epilepsy. Electroencephalography (EEG) is helpful in the diagnosis and classification of the seizure type, epilepsy, or epilepsy syndrome. Ictal EEG is rarely recorded, whereas interictal EEG is more often recorded, and the results can be abnormal or normal even in the case of epilepsy. The current digital care pathway for epilepsy (DCPE) lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management. Objective: This study aimed to determine the requirements for integrating data-driven medical software into the DCPE to meet the project’s goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasized 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 are presented. Methods: A 4-round Delphi study using focus group discussions was conducted with 7 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. In addition, a qualitative study was carried out using semistructured interviews. Results: Drawing from the findings of the thematic analytics, a detailed set of guidelines was created to facilitate the seamless integration of the proposed data-driven medical software for EEG seizure monitoring into the DCPE. These guidelines encompass system requirements, data collection and analysis, and user training, offering a comprehensive road map for the effective implementation of the software. Conclusions: The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing health care resources, and using artificial intelligence and sensor technology in clinical settings. The potential of artificial intelligence and sensor technology to revolutionize health care is exciting. The study 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 were 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 DCPE.