VAMOS Collaborative: An Open Source Platform for Trustworthy AI

Why It Matters

Continuous algorithm oversight protects patient safety and equity, reducing costly model failures. The open‑source, multi‑institutional approach accelerates adoption of best‑practice standards across the sector.

Key Takeaways

• VAMOS provides real‑time AI performance dashboards for health systems.
• Open‑source collaborative includes UCSF, OHSU, Mass General Brigham.
• Platform tracks accuracy, drift, fairness, and can pause algorithms.
• Network enables cross‑site learning on bias and model failures.
• HL7 and Trustworthy AI Network help shape standards.

Pulse Analysis

The rise of machine‑learning tools in clinical care has outpaced traditional safety checks, prompting experts to borrow concepts from drug safety. Embi coined "algorithmovigilance" to describe systematic, post‑deployment monitoring akin to pharmacovigilance, emphasizing that AI models must be continuously evaluated for unintended consequences, especially in diverse patient populations. This mindset is gaining traction as hospitals confront model drift, bias, and regulatory scrutiny, making real‑time oversight a prerequisite for sustainable AI adoption.

VAMOS operationalizes algorithmovigilance through a configurable dashboard that aggregates key performance indicators such as accuracy, precision, data drift, and equity metrics. When a metric breaches predefined bounds, the system can trigger alerts, initiate model retraining, or even pause the algorithm to prevent harm. Because the codebase is open source, health systems can customize the platform to their data pipelines and integrate it with existing electronic health record workflows. Early adopters are already using VAMOS to monitor readmission prediction models like VUMC's "Cornelius," gaining granular visibility into model behavior across patient subgroups.

The collaborative network behind VAMOS amplifies its impact by creating a shared learning ecosystem. Institutions such as UCSF, OHSU, and Mass General Brigham contribute real‑world performance data, enabling cross‑site detection of bias—e.g., a model underperforming for Hispanic patients at one site can be flagged and corrected globally. Partnerships with standards bodies like HL7 and the Trustworthy AI Network aim to codify metrics and reporting formats, paving the way for industry‑wide compliance frameworks. For health systems, this translates into reduced liability, improved patient outcomes, and a clearer path to regulatory approval, positioning VAMOS as a cornerstone of trustworthy AI at scale.