What You Should Know
- Global patient safety nonprofit ECRI expanded its Problem Reporting Network to explicitly capture, investigate, and triage errors, malfunctions, and near misses involving artificial intelligence (AI) tools and AI-enabled medical devices used in patient care.
- Addresses a critical clinical oversight deficit: while AI is deployed across diagnostic imaging, clinical decision support, and ambient scribes, the industry lacks a centralized, healthcare-specific mechanism to track incorrect AI outputs or quantify how often they reach patients.
- In an ECRI survey of 124 hospital quality, safety, risk, and compliance leaders, 31% reported encountering an incorrect or misleading AI output over the past year, and 9% confirmed an AI error reached a patient or impacted a care decision (35% were unsure).
- Ambient scribes (37%) were the most frequently encountered AI tools among respondents, followed by EHR-embedded clinical decision support (31%) and clinical LLM assistants/chatbots (31%).
ECRI Expands Problem Reporting Network to Track Clinical AI Errors and Near Misses
Healthcare systems are rushing to deploy artificial intelligence across imaging, ambient documentation, and electronic health record decision support. Yet despite the rapid rollout, the industry still lacks a centralized, standardized mechanism to track when algorithms generate incorrect recommendations or how often those errors reach actual patients.
To close that safety blind spot, global patient safety organization ECRI has expanded its Problem Reporting Network to explicitly capture, investigate, and analyze errors, malfunctions, and near-misses tied to clinical AI applications and AI-enabled medical devices.
The initiative issues a national call to clinicians, health systems, and risk management leaders to report any incident where an AI-driven tool introduced clinical risk, made a factual hallucination, or contributed to a medical error.
A Growing Footprint with Limited Post-Market Surveillance
While clinical software vendors have introduced hundreds of AI features into hospital environments, post-market safety data remains fragmented.
In a recent ECRI survey of 124 hospital quality, risk, and safety executives, nearly one-third (31%) reported encountering an AI output they believed was incorrect or misleading over the past year. Another 35% were unsure whether errors had occurred, highlighting how difficult algorithmic mistakes can be to detect in fast-paced clinical environments. Notably, 9% of respondents confirmed that an AI-generated error had directly reached a patient or influenced a care decision.
Ambient scribes represented the most commonly encountered AI tool in the survey (37%), followed closely by EHR-embedded clinical decision support systems (31%) and clinical large language model (LLM) chatbots (31%).
How the Reporting Network Operates
ECRI’s Problem Reporting Network, which has operated as a confidential medical technology incident channel since 1972, will apply its existing clinical engineering review process to AI submissions:
- Independent Investigation: Each submitted incident is triaged and investigated by ECRI’s in-house biomedical engineers and clinical specialists, who follow up directly with the reporting organization.
- Hazard Advisories: If an algorithm or device design flaw poses broader risk across the health sector, ECRI issues formal hazard alerts with actionable remediation steps to manufacturers, hospital leaders, and regulatory bodies like the FDA.
- PSO Complement: The AI reporting pathway operates alongside the broader ECRI and ISMP Patient Safety Organization (PSO), which has analyzed more than 8 million general safety event reports nationwide.
What Leadership Is Saying
“ECRI has persistently emphasized the risk of adopting AI with insufficient scrutiny,” said Scott Lucas, PhD, ECRI’s Vice President of Devices, Therapeutics, and Technology. “Although we appreciate AI’s tremendous potential, we don’t yet have a clear picture of its downstream impact in healthcare. Without a robust reporting dataset and analysis, the industry cannot sufficiently improve the design and integration of AI tools and devices. We must look to evidence and data to understand the evolving risks and associated system factors, to enable the use of the safest, most effective technologies.”
