Health IT policy & regulation

Mengying Zhu, Bingjie Luo +11

How should hospitals calibrate oversight of AI tools embedded in EHR workflows, imaging, triage, documentation, and operations? The authors conducted a narrative review and framework synthesis drawing on peer-reviewed evidence, reporting guidelines, regulatory and policy sources, implementation studies, and applied governance case reports. The resulting framework has four components: a use-case inventory tagged by decision influence and workflow coupling; a six-domain risk taxonomy spanning clinical safety, privacy and data security, ethics and fairness, transparency, system stability, and compliance; a four-tier risk scheme keyed to harm, automation, reversibility, and coupling; and a governance architecture assigning roles to a committee, clinical owners, risk-control functions, and independent assurance. A lifecycle pathway runs from initiation and local validation through shadow mode, controlled go-live, monitoring, change control, and retirement. No effect sizes are reported; this is a conceptual framework, not an evaluation.

Lena Petersson, Luís Irgang +2

For what purposes do physicians use unauthorized, non-conformity-assessed AI tools at work? This cross-sectional survey of physicians in Swedish health care organizations (N=357; response rate ~64%), fielded through a verified online panel between December 2023 and January 2024, applied qualitative content analysis to free-text responses, interpreted through the sociology of professions and paradox theory. Reported uses fell into four categories: clinical work and decision-making (second opinions, differential diagnoses, rare cases), administrative work (patient communication, documentation), research and professional development, and technological curiosity. Physicians framed such use as compensating for gaps in institutional systems and reducing workload. The abstract reports no effect sizes or usage prevalence.

Anna Spagnolli, Cecilia Tolomini +3

Do the transparency needs of healthcare AI users actually map onto the Instructions for Use (IFU) document that the EU AI Act (Directive 2024/1689) requires providers to give deployers? This cross-sectional online survey, administered via Qualtrics to four deployer groups \u2122 managers (N = 238), healthcare professionals (N = 115), patients (N = 229), and IT workers (N = 230) \u2122 asked participants to rate the relevance of a set of transparency needs and identify which IFU section would address each. Priorities differed across user types, and participants had difficulty locating some transparency information within the IFU structure; the abstract reports no effect sizes or magnitudes. The authors derive recommendations for locally meaningful IFUs.

Elizabeth M Stone, Rachel Topazian

How much do members of the public support regulating AI-delivered mental health advice? This Health Affairs Scholar paper takes up that question, but no abstract was available at the time of writing, so the study design, sample, and findings cannot be characterized here. Readers interested in public opinion on guardrails for consumer-facing chatbots and other AI tools offering psychological support should consult the full text for the survey methods, population sampled, and reported levels of support for specific regulatory approaches.