MULTIPLE SCLEROSIS: Edited by Giancarlo ComiE-health and multiple sclerosisMatthews, Paul M.a; Block, Valerie J.b; Leocani, Letiziac,dAuthor Information aDepartment of Brain Sciences and UK Dementia Research Institute Centre, Imperial College, London, UK bDepartment of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, USA cUniversity Vita-Salute San Raffaele dNeurorehabilitation Department and Experimental Neurophysiology Unit, INSPE, Scientific Institute Hospital San Raffaele, Milan, Italy Correspondence to Paul M. Matthews, E502, Department of Brain Sciences, Imperial College London, Hammersmith Hospital, DuCane Road, London WC12 0NN, UK. Tel: +44 207 594 2612; e-mail: [email protected] Current Opinion in Neurology: June 2020 - Volume 33 - Issue 3 - p 271-276 doi: 10.1097/WCO.0000000000000823 Buy Metrics Abstract Purpose of review To outline recent applications of e-health data and digital tools for improving the care and management of healthcare for people with multiple sclerosis. Recent findings The digitization of most clinical data, along with developments in communication technologies, miniaturization of sensors and computational advances are enabling aggregation and clinically meaningful analyses of real-world data from patient registries, digital patient-reported outcomes and electronic health records (EHR). These data are allowing more confident descriptions of prognoses for multiple sclerosis patients and the long-term relative benefits and safety of disease-modifying treatments (DMT). Registries allow detailed, multiple sclerosis-specific data to be shared between clinicians more easily, provide data needed to improve the impact of DMT and, with EHR, characterize clinically relevant interactions between multiple sclerosis and other diseases. Wearable sensors provide continuous, long-term measures of performance dynamics in relevant ecological settings. In conjunction with telemedicine and online apps, they promise a major expansion of the scope for patients to manage aspects of their own care. Advances in disease understanding, decision support and self-management using these Big Data are being accelerated by machine learning and artificial intelligence. Summary Both health professionals and patients can employ e-health approaches and tools for development of a more patient-centred learning health system. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.