{"title":"A National Cross-Sectional Survey of Artificial Intelligence Patient Safety Reporting Infrastructure Available to US Nurses.","authors":"Gregory A Carter","doi":"10.1097/NCQ.0000000000001011","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Federal artificial intelligence (AI) safety governance depends on clinicians reporting AI-related patient safety concerns, yet national availability of nurse-facing reporting channels and related practice guidance is unknown.</p><p><strong>Purpose: </strong>To characterize formal AI safety reporting channels available to practicing US nurses and correlates of reporting.</p><p><strong>Methods: </strong>A national cross-sectional survey of 1189 practicing US nurses was conducted in June to July 2026.</p><p><strong>Results: </strong>Among 424 nurses who encountered an AI-related patient safety concern, 83 (19.6%) reported no formal channel for reporting it. Non-hospital nurses had higher odds of no channel than hospital-based nurses (odds ratio, 2.54; 95% confidence interval, 1.54-4.19). Where a channel existed, perceived severity predicted reporting (adjusted odds ratio, 2.47; 95% confidence interval, 1.77-3.45).</p><p><strong>Conclusions: </strong>One in 5 nurses with an AI-related patient safety concern lacked reporting infrastructure. Federal AI safety governance presupposes infrastructure that remains uneven across US care settings.</p>","PeriodicalId":16931,"journal":{"name":"Journal of nursing care quality","volume":" ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of nursing care quality","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/NCQ.0000000000001011","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Federal artificial intelligence (AI) safety governance depends on clinicians reporting AI-related patient safety concerns, yet national availability of nurse-facing reporting channels and related practice guidance is unknown.
Purpose: To characterize formal AI safety reporting channels available to practicing US nurses and correlates of reporting.
Methods: A national cross-sectional survey of 1189 practicing US nurses was conducted in June to July 2026.
Results: Among 424 nurses who encountered an AI-related patient safety concern, 83 (19.6%) reported no formal channel for reporting it. Non-hospital nurses had higher odds of no channel than hospital-based nurses (odds ratio, 2.54; 95% confidence interval, 1.54-4.19). Where a channel existed, perceived severity predicted reporting (adjusted odds ratio, 2.47; 95% confidence interval, 1.77-3.45).
Conclusions: One in 5 nurses with an AI-related patient safety concern lacked reporting infrastructure. Federal AI safety governance presupposes infrastructure that remains uneven across US care settings.
期刊介绍:
Journal of Nursing Care Quality (JNCQ) is a peer-reviewed journal that provides practicing nurses as well as nurses who have leadership roles in nursing care quality programs with useful information regarding the application of quality principles and concepts in the practice setting. The journal offers a forum for the scholarly discussion of “real world” implementation of quality activities.