DADOS: Architecting a Scalable Architecture for Global Clinical Research
A cloud-based EDC platform that removes systemic friction from clinical research, scaling to over 150,000 patients across global networks.
Project results.
Patients Tracked
Massive longitudinal data capture across 200+ distinct clinical and research studies.
Real-Time Data
Instant scoring and reporting for surgeons at the point of care via iPad integration.
Project overview.
[Metric/Outcome]: Reduced research costs by replacing expensive proprietary vendors with a flexible, self-service model.
[Innovation]: Integrated a Machine Learning Module for real-time spine, hip, and knee outcome prediction.
[Scale]: Scaled to over 200 studies, 700 users, and 150,000 patients across multiple institutions.
Clinical research was historically paralyzed by "data friction". Researchers faced a fragmented landscape where outcome data was locked in disparate institutional silos, tools were not standardized, and entry into the field required reliance on costly, proprietary third-party software. This lack of integrated infrastructure delayed the transition from academic discovery to bedside clinical care.
Project execution.
We engineered DADOS as an open-platform solution to bridge the gap between translational research and clinical practice. By applying "surgery-to-system" architecture, I led the development of a framework that handles patient-reported outcomes (PROMs), clinical encounters, and biobanking specimens within a single, secure environment. We transitioned DADOS to a Software-as-a-Service (SaaS) model, utilizing cloud-hosted infrastructure to provide "on-demand" data capture for academic groups. The system includes an intuitive iPad interface for real-time patient entry, allowing clinicians to view longitudinal outcome reports instantly during patient visits.
Project results.
DADOS has become a cornerstone of the UHN research infrastructure, supporting major initiatives like the Longitudinal Evaluation of Arthritis Program (LEAP) and the Ontario Personal Support Worker (PSW) Registry. Its scalability is proven by its adoption at Mount Sinai Hospital and St. Michael’s Hospital. By integrating the NIH PROMIS CAT API, we have standardized high-quality outcome reporting, enabling data-driven medicine that is both clinically accurate and financially sustainable.
