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It will appear shortly on 10.2196/84148
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AINCRA: a readiness assessment for AI in nursing care projects based on a mixed-methods study
ABSTRACT
Background:
Integrating Artificial Intelligence (AI) systems into nursing care often encounters obstacles stemming from unmet requirements and insufficient engagement with well-documented socio-technical pitfalls. Readiness models offer a systematic way to evaluate project preparedness and to build the capabilities needed for successful AI in nursing care (AINC) research, development and implementation.
Objective:
A novel AI Nursing Care Readiness Assessment (AINCRA) tool was designed to support planning, execution, and evaluation of AINC projects.
Methods:
A sequential exploratory mixed-methods bottom-up approach to maturity model development identified key AI readiness dimensions and attributes. The initial AINCRA version is grounded on insights from expert workshops, an online survey, and a nominal group consensus process. A systematic literature review further triangulated AI readiness attributes. Lastly, a think aloud interview study and focus group discussions involving experts from diverse disciplines validated the attributes.
Results:
The resulting AINCRA encompasses five core dimensions: regulatory, processual, technical, social and ethical, and community building requirements and aspects.
Conclusions:
Across five maturity levels, 69 AINC readiness attributes enable practitioners from AI research and development, clinical partners and nursing and health scientists to plan, evaluate and enhance AI projects across their lifecycle, thereby supporting effective AI integration in nursing care. Clinical Trial: not applicable
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.