Objectives and Research Topics/Areas

Modern healthcare systems are generating massive amount of health data that coupled with exponential advances in technology and computing power have the potential for advancing medical research and improving patient care. The ability to leverage these data also add significant privacy issues, requiring new approaches to investigate potential risks, safeguard patient privacy, and maintain data usability. This interdisciplinary workshop aims at addressing these challenges by bringing together researchers and practitioners to deliberate on technical and non-technical issues, and investigate uncharted challenges and solutions in the domains of Data Privacy and Data Analysis in Healthcare systems. Numerous privacy solutions have been proposed over the years but applications in real healthcare systems are limited. Concerns regarding data fidelity, potential bias in predictive results, interpretability, and usability are among the limiting factors. To address these challenges, this workshop aims at collecting the most innovative state-of-the-art techniques, exploring new solutions, and fostering collaboration across domains to support the development of effective privacy-protecting health data analysis methods. Through quality research papers and talks, the workshop will make a significant contribution toward the development of novel information systems and computational methods to support a responsible use of health data in emerging technologies.

Topics include, but are not limited to:

1. Privacy and Data Sharing: investigating the challenges of maximizing data usability while safeguarding patient privacy in healthcare data/EHR analytics, precision medicine, and data integration

2. Trustworthy AI systems: examining the potential impacts of emerging AI models in biomedical applications, such as privacy risks and predictive bias; applications of AI in healthcare

3. Decentralized Data Analysis: exploring emerging techniques for leveraging data across multiple sites including but not limited to blockchain-based technology, federated learning and swarm learning

4. Health Data Analysis Infrastructure: including but not limited to methodologies and tools, visualization analytics, health management/repositories, and real-world case studies

Paper submission instructions

This workshop will only accept papers that have not been previously published. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines ( Click here).

Accepted paper formats (references are counted within the page limits):

Please click here to Submit your paper!

Accepted papers will be published in the IEEE ICHI 2026 proceedings (on IEEE Xplore – Indexed on Scopus and many other platforms). For further information, please see IEEE ICHI 2026 web page @ https://zhang-informatics.github.io/ICHI2026/.

At least one author of each accepted paper to the workshop must register and present the paper at the conference.

Review procedure

The reviewing process will be single-blind.

All submitted papers will be reviewed by 3 international program committees.

Important Dates

Workshop Organizers

General Chairs

Matteo Mantovani Matteo Mantovani, Ph.D.
Department of Computer Science, University of Verona, Italy
matteo.mantovani@univr.it
Luca Bonomi Luca Bonomi, Ph.D.
Department of Biomedical Informatics, Vanderbilt University Medical Center (VUMC), TN, USA
luca.bonomi@vumc.org

Technical Program Committee

T.B.A.

Program

T.B.A.

Past edition

Link - 2nd edition - In conjunction with the 2025 IEEE International Conference on Healthcare Informatics (IEEE ICHI 2025) - 3rd June 2024 - Orlando (FL) - USA

Link - 1st edition - In conjunction with the 2024 IEEE International Conference on Healthcare Informatics (IEEE ICHI 2024) - 3rd June 2024 - Orlando (FL) - USA