Secure and Compliant Data Platform for AI Research in Healthcare
Discover the Health Data Nexus, a pioneering platform built with T-CAIREM that's revolutionizing AI in medicine. We engineered it to ensure secure, compliant, and scalable access to sensitive health data, empowering researchers and educators worldwide.
Overview
To advance AI research in medicine using real-world health data, the Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM) at the University of Toronto partnered with Upside to develop the Health Data Nexus (HDN) — a secure, cloud-based platform designed to streamline access to clinical datasets for researchers, educators, and data custodians.
Engineered to meet the highest privacy and ethical standards, HDN enables cross-institutional collaboration while ensuring full compliance with relevant privacy regulations. Built on Google Cloud Platform, it balances security, usability, and scalability to meet the needs of a wide range of users.
By May 2025, HDN had onboarded nine datasets, including multimodal data sourced from real medical records, representing over 15,000 patients. Each dataset underwent rigorous legal, privacy, and governance reviews to ensure full regulatory compliance. The platform has supported researchers from five countries and powered several national medical datathons, advancing data-driven healthcare research and education.
Project Background
T-CAIREM, a global leader in AI health research, initiated the Health Data Nexus to advance medical innovation through secure and responsible data sharing. The platform was designed to support compliant research using real-world health data, including patient records, medical imaging, and population-level datasets.
To bring this vision to life, Upside was selected by T-CAIREM as the development partner to build the scalable, flexible, and privacy-first platform that meets the needs of data providers, researchers, and educators.
The Challenge
The Challenge
Given the scale and sensitivity of real-world health data, Upside was tasked with building a robust cloud infrastructure capable of supporting AI-driven medical research while meeting strict security and compliance requirements.
Beyond infrastructure, the platform also had to simplify complex governance workflows—including time-consuming data access approvals—support differentiated access for researchers and educators, and accommodate a broad spectrum of health data formats including tabular data, imaging, and clinical text.
Key Challenges and Requirements
- Data Sensitivity & Compliance: Manage de-identified patient data (e.g., clinical records, CT scans) in full compliance with PHIPA, and TCPS 2. This required a formal Privacy Impact Assessment and Threat Risk Assessment.
- Security & Governance: Meet hospital-grade requirements for data protection, including isolated cloud-based environments with no download capability. Also address governance challenges such as REB approvals and access controls.
- Access & Approval Bottlenecks: Reduce administrative friction caused by lengthy data access approval workflows.
- Multimodal & Poorly Standardized Data: Support diverse datasets, including tabular (e.g., St. Michael’s GIM dataset), imaging (e.g., CSpine, 1,000+ CT scans), and text, while overcoming inconsistent formatting and metadata.
- Diverse Function Requirements: The platform needed to support a wide range of use cases: secure data storage for providers, AI analysis tools for researchers, simple and siloed access for professors and students, all managed through flexible role-based permissions and an intuitive interface.
Results and Impact
Results and Impact
Since launch, the Health Data Nexus has demonstrated tangible value across its three core pillars in data acquisition, research, and education, validating the collaborative work between T-CAIREM and Upside. These early results showcase the platform's scalability, usability, and relevance for the research community.
1. Nine Datasets Hosted
As of May 2025, the platform hosts nine datasets spanning tabular data, imaging, voice recordings, and population-level health information. These include the St. Michael’s Hospital General Internal Medicine (GIM) dataset (14,000 patients across 22,000 visits with comprehensive clinical data), the CSpine dataset with over 1,000 CT scans, COVID-19 inpatient data, the Canadian Heart Health Database, randomized trial files for LLM research, national epidemiology datasets, the Bridge2AI voice dataset, and the Sunnybrook Hospital Sleep Laboratory dataset.
These contributions showcase the platform's flexibility in hosting multimodal data and its potential as a national health data infrastructure.
2. Education and training:
HDN was used in classrooms and workshops as a teaching tool, giving university students hands-on experience with real-world medical data. Notable examples include:
- A biomedical engineering professor who has run student projects for over two years using datasets like GIM and Canada Heart Health to explore real-world data issues such as bias and class imbalance.
- The VADA Summer Schools workshop, a week-long program that used the HDN for student-led analytics projects.
Additionally, Upside’s work on credentialing, preloaded training, and dataset access workflows has made the HDN a plug-and-play solution for data-centric workshops and university courses.
3. Multiple International Datathons Supported:
Since its launch, the Health Data Nexus has supported 4 provincial and national datathons focused on health and medical research. These events gave researchers and students hands-on experience with real clinical datasets. Feedback from participants led to meaningful platform enhancements, including improved data storage workflows, the addition of shared billing for collaborative teams, and greater computing capacity through access to Google Vertex AI.
4. User Growth & Diversity:
The Health Data Nexus has attracted a diverse and expanding user base, primarily researchers, academics, and students from institutions around the world. As of 2025, the platform has been adopted in over 12 countries across four continents, amplifying its growing global impact in health data research.
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