What We Enable

Across Canada and around the world, healthcare systems and research organizations are striving to improve outcomes, reduce costs, and work more collaboratively. Now more than ever, data holds the key to jumpstart any of those goals, and MDClone has spent a decade designing new approaches to safely and efficiently leverage the massive investment we’ve made to collect data.

From frontline clinicians and students to policymakers and system leaders, we empower people to ask better questions, explore data more confidently, build new tools and technologies, and ultimately drive meaningful change. Whether you’re building a Learning Health System, launching cross-institutional research, or modernizing your analytics infrastructure, MDClone provides the foundation for progress.

Collaborative Research and Education

MDClone transforms the way health data is accessed and used in academic and clinical research. Whether fostering early-stage hypothesis generation or supporting REB-ready protocols, MDClone’s synthetic data solutions enable secure, privacy-preserving data exploration across institutions. In the classroom, students gain hands-on experience with high-fidelity synthetic data that mirrors real-world clinical complexity—without risking privacy. The ability to iteratively explore and refine data questions with the ADAMS platform in real time fosters deeper insight and creativity in both research and education, fueling new hypotheses and accelerating discovery. Across Canada, leading institutions like McGill and The Ottawa Hospital are using MDClone to accelerate research timelines, foster cross-disciplinary learning, and remove traditional barriers to collaboration.

Data-Driven Health System

Health systems face growing demand to improve outcomes, control costs, and enhance equity. MDClone enables organizations to become true Learning Health Systems, integrating data – structured and unstructured – across clinical, operational, and financial domains. Our ADAMS Platform and associated ADAMS Center methodology helps institutions dig into the data to frame and iterate on their most pressing questions, drive insights to action, and track measurable results. To support this transformation at scale, systems must equip frontline professionals with the data literacy needed to explore and interpret information, a key goal of the MDClone Academy. With the ability to empower hundreds of users across roles—from the bedside to the boardroom—MDClone supports a system-wide culture of continuous improvement and self-service analytics.

System-Level Research & Policy Collaboration

National and provincial health authorities require scalable, privacy-compliant tools to analyze outcomes across populations, systems, and regions. MDClone offers a turnkey infrastructure to enable secure data aggregation, cross-site collaboration, and real-world evidence generation—without the delays of data sharing agreements or the risks of exposing PHI. With the Connect Platform, Canadian public health agencies, policy makers, and research councils can run population-level studies, evaluate policy interventions, and inform funding models across jurisdictions, all while respecting local governance and maintaining public trust.

Privacy-Protected Data Access and Sharing

Canadian healthcare leaders must balance innovation with rigorous privacy standards. MDClone’s privacy-first approach, rooted in a decade of development of synthetic data solutions, allows for secure internal and cross-institutional collaboration without exposing PHI. Whether enabling interprovincial research, supporting external partnerships, or exploring unstructured data like physician notes, MDClone unlocks the full potential of data—compliantly and confidently. Built for PHIPA, PIPEDA, and Indigenous data sovereignty frameworks, MDClone supports a future-ready, AI-compatible infrastructure.

Systems Testing and AI Model Validation

In addition to analytics and research, MDClone’s synthetic data solutions enable the creation of secure, high-fidelity development environments for systems testing, application prototyping, and operational simulations—all without the use of real patient data. Organizations can validate workflows, build and test infrastructure, or onboard vendor systems without triggering compliance burdens. Synthetic data also supports AI development and integration by providing richly structured, bias-controllable datasets for model training, testing, and validation—empowering organizations to automate core physician or system functions in a trustworthy manner.