[Healthcare networks for people with rare diseases: integrating data and expertise]

Consultation Requests and Satisfaction with a Telehealth Network for Epilepsy: Longitudinal Analysis of the Epilepsy Network Hessen Evaluation

Background: Telemedicine improves access to specialized medical expertise, as required for paroxysmal disorders. The Epilepsy Network Hessen Evaluation (ENHE) is a pilot cross-sectoral teleconsultation network connecting primary neurologists and pediatricians with epilepsy centers in Hessen, a federal German state. Methods: We prospectively and longitudinally evaluated telehealthcare in the ENHE. Participating physicians rated each consultation for satisfaction and impact on further management. The survey was administered at each consultation and 3 months later. Results: We analyzed 129 consultations involving 114 adult and pediatric patients. Their mean age was 34 years (standard deviation: 26, range: 0.1-91 years), 48% were female, and 34% were children and adolescents. The most common consultation requests were co-evaluation of an electroencephalogram (electroencephalogram [EEG]; 76%) and therapeutic (33%) and differential diagnosis (24%) concerns. Physicians transmitted one paraclinical examination on average (range: 1-4), predominantly EEG (85%), followed by magnetic resonance imaging (17%) and written records (9%). Response rates were 72% for the initial and 67% for the follow-up survey. Across respondents, 99% (n = 92) were satisfied with the ENHE. Overall, 80% of the consultations contributed to the diagnosis, and 90% were considered helpful for treatment, influencing it in 71% of cases. Seizure frequency had decreased more often (96%) than increased (4%) at 3 months. The initial diagnosis was confirmed in 78% of patients. Discussion: In this pilot teleconsultation network for paroxysmal disorders, diagnostic and therapeutic advice was perceived as helpful. Clinical outcomes were largely positive, suggesting tele-epileptology is viable for paroxysmal (seizure) disorders.

Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study

BACKGROUND

The delay in diagnosis for rare disease (RD) patients is often longer than for patients with common diseases. Machine learning (ML) technologies have the potential to speed up and increase the precision of diagnosis in this population group. We aim to explore the expectations and experiences of the members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application.

METHODS

We used a mixed-methods approach with an online survey followed by a focus group discussion. Our study targeted primarily medical professionals but also other individuals affiliated with any of the 24 ERNs.

RESULTS

The online survey yielded 423 responses from ERN members. Participants reported a limited degree of knowledge of and experience with ML technologies. They considered improved diagnostic accuracy the most important potential benefit, closely followed by the synthesis of clinical information, and indicated the lack of training in these new technologies, which hinders adoption and implementation in routine care. Most respondents supported the option that ML should be an optional but recommended part of the diagnostic process for RDs. Most ERN members saw the use of ML limited to specialised units only in the next 5 years, where those technologies should be funded by public sources. Focus group discussions concluded that the potential of ML technologies is substantial and confirmed that the technologies will have an important impact on healthcare and RDs in particular. As ML technologies are not the core competency of health care professionals, participants deemed a close collaboration with developers necessary to ensure that results are valid and reliable. However, based on our results, we call for more research to understand other stakeholders' opinions and expectations, including the views of patient organisations.

CONCLUSIONS

We found enthusiasm to implement and apply ML technologies, especially diagnostic tools in the field of RDs, despite the perceived lack of experience. Early dialogue and collaboration between health care professionals, developers, industry, policymakers, and patient associations seem to be crucial to building trust, improving performance, and ultimately increasing the willingness to accept diagnostics based on ML technologies.