RAPS Webcast: Rapid Sharing of International Clinical Data Ensuring Patient Privacy for Combating Pandemics and Other Diseases (on-demand)
This is an on-demand recording from May 2023Health emergency situations, such as the COVID-19 pandemic, require changes to long-term research procedures. Expedited information sharing is the key to rapid innovation, especially in the era of Big Data and AI.
Patient privacy and the need for untraceable data is a major concerns, which drove the EU to enact the General Data Protection Regulation (GDPR). Although important, it hampers clinical and scientific advances. GDPR has hindered health-care study data sharing. Is there a way to speed the process of international data sharing? Could we comply with GDPR while fostering rapid exchange of health data across the globe?
The webinar will look at the EU’s health-care ecosystem; explain recent strategic proposals based on state-of-the-art AI advances that speed up such processes; and outline a business proposal presented to a European challenge team that might pave the way for supporting international research and collaboration.
REGISTRATION FEE
Free
Learning Objectives
Audience Learning Level
Basic: Content is introductory. Basic educational activities are meant to establish a foundation of knowledge and/or competence.
Who Should Attend?
Regulatory intelligence professionals and researchers interested in international medical-data sharing.
Instructors:
SPEAKER BIO Ana Pilar Olmos is a regulatory consultant in medical devices and combination products. She focuses on MDR implementation, QMS remediation, clinical evaluation, and complaint handling at global pharmaceutical and medical-device companies. She has a biomedical engineering and AI background, Pilar Olmos. Participated in an entrepreneurship European challenge program that created an international database with synthetic clinical data that is statistically representative and privacy compliant. She co-founded Artificial Intelligence for Medicine.
Jorge García Condado is a Cambridge graduate engineer specializing in both Information and Computer Engineering and Bioengineering. He also holds an MRes in Information Health Engineering from the Universidad Carlos III de Madrid. He is a “La Caixa” doctoral fellow at Biocruces Bizkaia HRI. He researches machine learning, brain networks, and neurological disorders. He aims to help clinicians diagnose and distinguish mental and behavioral disorders by using AI.
