ECRI Expands Patient Safety Network to Track AI-Driven Clinical Errors
The healthcare safety organization is targeting systemic algorithmic failures to prevent scalable patient harm.
ECRI has expanded its existing Problem Reporting Network to specifically track and analyze errors caused by artificial intelligence in patient care. The move aims to identify systemic AI failures and provide critical mitigation guidance to healthcare providers.
By integrating AI-specific adverse events into its reporting infrastructure, ECRI intends to capture real-world data on how algorithmic tools fail in clinical settings. This initiative focuses on identifying patterns of failure that could lead to patient harm, allowing the organization to issue safety alerts and technical guidance to institutions utilizing these technologies.
The Visibility Gap in AI Safety
As AI integration grows within diagnostics and clinical decision support, the industry faces an increasing risk of "algorithmic harm." Traditional patient safety reporting systems were designed for human error or mechanical failure and often lack the technical nuance or specific categories required to document AI-driven mistakes. This creates a visibility gap where the root cause of a clinical error—a flawed algorithm—may be overlooked or miscategorized.
ECRI has already flagged these concerns in its 2025/2026 health technology hazard reports, which identified AI-enabled technology and AI chatbots as top risks to patient safety. These reports underscore the urgency of creating a dedicated pipeline for reporting AI malfunctions before they result in widespread clinical failures.
The Risk of Scalable Harm
Unlike traditional medical errors, which are often isolated to a single practitioner or facility, AI errors are systemic and scalable. A single flawed algorithm deployed across multiple health systems can potentially affect thousands of patients simultaneously, regardless of the institution's local quality controls.
A centralized reporting network allows for the identification of these broad patterns. By aggregating data from various providers, ECRI can detect when a specific AI tool is consistently producing incorrect outputs or biased results, enabling a rapid response that can prevent harm across the wider healthcare ecosystem.
Monitoring the Algorithmic Frontier
Moving forward, the industry will be watching how this data translates into concrete safety standards for AI procurement and deployment. While the reporting network provides the necessary data, the next challenge remains the speed at which providers can implement corrections to proprietary software owned by third-party vendors.
Healthcare organizations are now encouraged to report "Clinical AI Problems" directly through the Problem Reporting Network to help build a comprehensive database of algorithmic failures. The effectiveness of this initiative will depend on the willingness of providers to report failures in tools that are often marketed as infallible.