Innovations in e-health

The theme of ISOQOL’s 19th Annual Conference in Budapest, Hungary, was The Journey of Quality of Life Research: A Path Towards Personalized Medicine. Innovations in e-health was one of four plenary panels. E-health is changing the landscape of clinical practice and health care, but the best way to leverage the many promised benefits of emerging e-health technologies is still not clear. The Innovations in e-health panel presented emerging changes in technologies and applications that will facilitate clinical decision making, improve quality and efficiency of care, engage individuals in clinical decision making, and empower them to adopt healthy behaviors. The purpose of this paper was to present emerging trends in e-health and considerations for successful adoption of new technologies, and an overview of each of the presentations in the e-health plenary. The presentations included a personal perspective on the use of technology for self-monitoring in Parkinson’s disease, an overview of online social networks and emerging technologies, and the collection of patient-reported outcomes through web-based systems in clinical practice. The common thread across all the talks was the application of e-health tools to empower individuals with chronic disease to be actively engaged in the management of their health. Considerations regarding data ownership and privacy, universal access to e-health, interactivity between different types of e-health technologies, and tailoring applications to individual needs were explored

PatientsLikeMe® Online Epilepsy Community: Patient characteristics and predictors of poor health-related quality of life

AbstractObjectiveThe online PatientsLikeMe® Epilepsy Community allows patients with epilepsy to record, monitor, and share their demographic, disease, and treatment characteristics, providing valuable insights into patient perceptions and understanding of epilepsy. The objective of this retrospective analysis was to characterize the profile of users and their disease and identify factors predictive of poor health-related quality of life (HRQoL), while assessing the platform's potential in providing patient-reported data for research purposes.MethodsData recorded (January 2010–November 2011) by Epilepsy Community members, with an epilepsy diagnosis and who reported >1 seizure, included the following: sociodemographic and disease characteristics, treatments, symptoms, side effects perceived as medication-related, seizure occurrence, and standardized questionnaires (Quality of Life in Epilepsy Inventory [QOLIE-31/P], EuroQoL 5-Dimensions Scale, 3 Levels [EQ-5D-3L], and Hospital Anxiety and Depression Scale [HADS]). Univariate and multivariate logistic regressions were conducted to identify predictors of poor HRQoL.ResultsDuring the study period, the Epilepsy Community comprised 3073 patients, of whom 71.5% were female, had a mean age of 37.8years, and had a mean epilepsy duration of 17.7years. The most frequently reported moderate/severe symptoms (n=2135) included memory problems (60.2%), problems concentrating (53.8%), and fatigue (50.0%). Medication-related side effects (n=639) included somnolence (23.2%), fatigue (17.2%), and memory impairment (13.8%). The QOLIE-31/P scores (n=1121) were significantly worse in patients who experienced a recent seizure. For QOLIE-31/P, highly predictive factors for poor HRQoL included the following: mild/moderate problems concentrating, depression, memory problems, treatment side effects, occurrence of tonic–clonic seizures, and epilepsy duration ≤1year. For EQ-5D-3L, highly predictive factors for poor HRQoL included the following: pain, depression, and comorbidities. Patients on newer AEDs were less likely to report poor HRQoL (QOLIE-31/P).SignificanceThese findings move further towards supporting the feasibility and usefulness of collecting real-world, anonymized data recorded by patients online. The data provide insights into factors impacting HRQoL, suggesting that a holistic treatment approach beyond seizure control should be considered in epilepsy

Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom

Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. Despite the apparent enthusiasm of researchers, funders, and the media, evidence is scarce for successful implementation of products, algorithms, and services arising that make a real difference to clinical care. This article collection provides concrete examples of how “big data” can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine—but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.Published versio