Clinicians struggle to translate findings from discovery-based research to the care of individual patients. For example, selecting therapies based on venous thromboembolism (VTE) recurrence risk remains a largely unfulfilled goal. Those working in the field of thrombosis and hemostasis, and more generally in vascular care, have been saying for years that a sharper focus on the diagnosis and treatment of VTE is needed and that current efforts are not commensurate with the economic and health impacts of thromboembolism. We propose to address the needs above, in particular patient-specific risk assessment, diagnosis, and treatment for venous thromboembolism. We are leveraging ongoing improvements in the capability and resolution of noninvasive imaging (e.g., ultrasound technologies for examining the venous valve system in living patients) and image processing, aided by artificial intelligence/machine learning (AI/ML) and advanced mathematical regression. We employ Firebase Realtime Database, an API that synchronizes application data across multiple platforms (e.g., iOS, Android, and Web devices) by storing data on Firebase’s cloud for real-time analysis. The framework is designed to assist clinicians and other medical professionals in real-time for collaborative patient care. Our approach is designed to broaden the accessibility of the clinical analysis to a wider range of stakeholders, including those without specialist knowledge.