AINCRA: a readiness assessment for AI in nursing care projects based on a mixed-methods study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> A novel AI Nursing Care Readiness Assessment (AINCRA) tool was designed to support planning, execution, and evaluation of AINC projects. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> The resulting AINCRA encompasses five core dimensions: regulatory, processual, technical, social and ethical, and community building requirements and aspects. </sec> <sec> <title>CONCLUSIONS</title> 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. </sec> <sec> <title>CLINICALTRIAL</title> not applicable </sec>

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