Andra Ionescu, Paris Carbone, Sebastiaan Meijer, Christos Koutras, Juliana Freire, Katja Hose, Jayanth Raghothama
Interoperability in healthcare remains difficult despite decades of standards and infrastructure. Although messaging protocols and standards enable systems to exchange data, they do not guarantee that clinical meaning is preserved across organisations, workflows, and cultural contexts. This persistent gap between where semantics are used in care and how they are exchanged continues to drive fragmentation, bespoke mappings, and costly manual integration.
This tutorial offers a primer and a forward-looking agenda by framing interoperability as three generations. We first revisit the pre-interoperability era of proprietary systems and point-to-point integrations. We then cover the standards-based second generation, highlighting what it solved (e.g., connectivity) and what it left unresolved (e.g., semantic alignment). Finally, we outline an emerging third generation towards true interoperability, and conclude with research directions for building interoperability in the AI era.
Date & Time: 02 September 2026, 15:45 - 17:15
Venue: Boston, MA, USA
Room: Otis, The Westin Boston Seaport District
KTH Royal Institute of Technology
Postdoctoral Researcher at KTH Royal Institute of Technology, in the Department of Biomedical Engineering and Health Systems. Her current work focuses on graph-based clinical data harmonisation and semantic interoperability challenges.
KTH Royal Institute of Technology
Associate Professor at KTH Royal Institute of Technology and director of the Data Systems Lab. He is working on scalable data processing systems, generative computing, data streaming, and graph databases. He is a co-recipient of the ACM SIGMOD Systems Award 2023 (Apache Flink).
KTH Royal Institute of Technology
Professor of Health Care Logistics at KTH Royal Institute of Technology. He is working on healthcare, health prevention and promotion systems, and is the coordinator of the European Digital Innovation Hub Health Data Sweden.
New York University
Postdoctoral Research Associate at the VIDA Center of NYU Tandon School of Engineering. His research addresses core data integration challenges, while contributing open-source tools accessible to both domain experts and developers.
New York University
Institute Professor at NYU's Tandon School of Engineering and co-directs the VIDA Center. Her research develops methods and systems that enable users to obtain trustworthy insights from data, spanning large-scale data analysis and integration, visualization, machine learning, provenance management, and web information discovery.
TU Wien
Professor at TU Wien, leading the Data Management and Knowledge-Driven AI Lab. She is working on intelligent data management, knowledge graphs, and applied machine learning with interdisciplinary collaboration in bioscience and health.
KTH Royal Institute of Technology
Associate Professor at KTH Royal Institute of Technology, in the Department of Biomedical Engineering and Health Systems. His research interests include complex systems, health informatics, policy design, and applied ML.