To mark Affected person Security Consciousness Week, which runs March 8-14, ECRI and the Institute for Protected Treatment Practices (ISMP) printed a report that identifies the ten most crucial affected person security challenges anticipated to affect the healthcare trade in 2026, with “Navigating the AI Diagnostic Dilemma” topping the listing.
The nonprofit ECRI, one of many largest U.S.-based affected person security organizations, mentioned that the High 10 Affected person Security Considerations listing is knowledgeable by insights from senior executives from throughout the healthcare panorama—together with built-in well being techniques, kids’s hospitals, rural neighborhood well being facilities, and nationwide associations.
In selecting AI used for diagnostics as its high security concern, ECRI notes that AI remains 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 scientific 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 meant to resolve.”
Regardless of its potential to enhance diagnostic accuracy by automating knowledge retrieval, lowering cognitive load, decreasing cognitive biases, and offering clinicians with data 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 knowledge used to coach them. Such biases may end up in incorrect diagnoses and will exacerbate healthcare disparities.
• An absence of transparency associated to the info used to coach the AI mannequin and the event and testing of underlying algorithms may end up in diagnoses based mostly on outdated, inadequate, or incorrect data.
• Points with an AI system’s operation and efficiency may end up in hallucinations (i.e., incorrect, nonsensical, or nonexistent outputs) or system brittleness (i.e., AI’s incapacity to think about conditions that fall exterior of its coaching knowledge), each of which may contribute to misdiagnosis. That is particularly harmful as a result of AI techniques are sometimes skilled to
give solutions to each query—and customers could not understand the solutions are improper.
• Analysis has proven that over time, over-reliance on AI can erode individuals’s crucial pondering expertise. This has raised issues that clinicians who commonly depend on AI to assist diagnose sufferers will lose helpful diagnostic expertise, and that clinicians in coaching could 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 tasks for the governance, implementation, oversight, documentation, and monitoring of AI applied sciences.
ECRI additionally means that well being techniques be certain that employees are skilled on the right use of AI techniques, notably those who help in analysis, and inform clinicians of the techniques’ capabilities and limitations. They need to require employees to doc situations during which AI was used for diagnostic functions and the way it affected the scientific diagnostic course of.
The report recommends disclosing using AI to sufferers and procure knowledgeable consent earlier than utilizing generative AI in affected person analysis or importing affected person data to an AI system. Well being techniques ought to embrace opt-out clauses in consent agreements, ECRI recommends.
One other advice is to foster a simply tradition and encourage employees 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 severely and take steps to analyze and handle them, the report says.
Right here is the total ECRI High 10 Affected person Security Considerations matter listing for 2026:
1. Navigating the AI Diagnostic Dilemma
2. Diminished 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 Obstacles Inhibit Equitable Ache Administration for Ladies
7. Persistent Workforce Shortages Proceed to Burden Workers and Prohibit 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 Treatment Security Efforts
