To mark Affected person Security Consciousness Week, which runs March 8-14, ECRI and the Institute for Secure Medicine Practices (ISMP) printed a report that identifies the ten most crucial affected person security challenges anticipated to impression the healthcare business in 2026, with “Navigating the AI Diagnostic Dilemma” topping the checklist.
The nonprofit ECRI, one of many largest U.S.-based affected person security organizations, stated that the High 10 Affected person Security Issues checklist is knowledgeable by insights from senior executives from throughout the healthcare panorama—together with built-in well being techniques, kids’s hospitals, rural group well being facilities, and nationwide associations.
In selecting AI used for diagnostics as its high security concern, ECRI notes that AI continues to be an evolving know-how that raises points associated to reliability, transparency, privateness, legal responsibility, and ethics, and “customers shouldn’t deal with it as a alternative for medical experience. Inserting an excessive amount of belief in an AI mannequin to diagnose sufferers with out factoring in clinician experience can result in misdiagnosis— the very drawback AI was supposed to resolve.”
Regardless of its potential to enhance diagnostic accuracy by automating information retrieval, lowering cognitive load, decreasing cognitive biases, and offering clinicians with info to assist information their selections, ECRI notes that in some circumstances AI can contribute to diagnostic errors. The report gave some examples:
• AI fashions can perpetuate biases current within the information used to coach them. Such biases can lead to incorrect diagnoses and should exacerbate healthcare disparities.
• A scarcity of transparency associated to the information used to coach the AI mannequin and the event and testing of underlying algorithms can lead to diagnoses based mostly on outdated, inadequate, or incorrect info.
• Points with an AI system’s operation and efficiency can lead to hallucinations (i.e., incorrect, nonsensical, or nonexistent outputs) or system brittleness (i.e., AI’s incapability to contemplate conditions that fall outdoors of its coaching information), each of which may contribute to misdiagnosis. That is particularly harmful as a result of AI techniques are sometimes educated to
give solutions to each query—and customers might not notice the solutions are improper.
• Analysis has proven that over time, over-reliance on AI can erode folks’s vital considering expertise. This has raised issues that clinicians who usually depend on AI to assist diagnose sufferers will lose precious diagnostic expertise, and that clinicians in coaching might fail to develop these expertise solely.
The report recommends that well being techniques set up AI utilization insurance policies, tips, and procedures for workers that define clear roles and obligations for the governance, implementation, oversight, documentation, and monitoring of AI applied sciences.
ECRI additionally means that well being techniques make sure that workers are educated on the correct use of AI techniques, significantly people who help in prognosis, and inform clinicians of the techniques’ capabilities and limitations. They need to require workers to doc situations through which AI was used for diagnostic functions and the way it affected the medical diagnostic course of.
The report recommends disclosing using AI to sufferers and acquire knowledgeable consent earlier than utilizing generative AI in affected person prognosis or importing affected person info to an AI system. Well being techniques ought to embrace opt-out clauses in consent agreements, ECRI recommends.
One other suggestion is to foster a simply tradition and encourage workers to talk up if points with AI-based applied sciences happen. Well being techniques ought to take issues associated to the operation and use of AI techniques critically and take steps to research and tackle them, the report says.
Right here is the total ECRI High 10 Affected person Security Issues subject checklist for 2026:
1. Navigating the AI Diagnostic Dilemma
2. Decreased Entry to Rural Healthcare Will increase Well being Dangers and Disparities
3. Rising Charges of Preventable Acute Ailments in Communities and Healthcare Settings
4. Results of Federal Funding Cuts on Healthcare Operations and Affected person Security
5. Lack of Recognition and Reporting of Hurt Occasions
6. Structural and Systemic Boundaries Inhibit Equitable Ache Administration for Ladies
7. Persistent Workforce Shortages Proceed to Burden Workers and Limit Entry to Care
8. The Impression on System Enchancment When a Tradition of Blame Hinders Studying
9. Emergency Division Boarding Contributes to Worse Affected person Outcomes
10. Persistent Gaps in Producer Packaging and Labeling Design Proceed to Undermine Medicine Security Efforts
