Nurses' perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study.

Introduction: The purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption.

Design: Qualitative descriptive study.

Methods: Nurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke.

Results: Four major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools.

Conclusion: This study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy.

Clinical relevance: This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support.