Exploring the Association Between AI-Derived Competency Scores and Nurse Retention

Vollständiger Abstract

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Background: This study explored the associations between artificial intelligence (AI)-derived competency scores and nurse retention outcomes among newly employed nurses. Evidence regarding the relationship between AI-based competency assessments, nurse performance, and long-term retention remains limited. Methods: Data were collected from 156 newly employed nurses at hospitals in South Korea between March 2021 and August 2023. Retention status (retained vs. resigned) was specified as the dependent variable, while AI-derived competency scores served as independent variables in binary logistic regression analyses. Result: Self-reflection was significantly associated with retention (p = 0.032), whereas honesty (p = 0.051) and values (p = 0.072) did not achieve statistical significance and were therefore not associated with retention. Behavioral response was significantly associated with early turnover (p = 0.035), whereas relationship (p = 0.073) and understanding of others (p = 0.141) were not significantly associated with turnover. Conclusions: These findings provide preliminary evidence that selected AI-derived competency scores are associated with nurse retention outcomes. However, the findings should be interpreted cautiously because this study examined statistical associations rather than predictive performance, and further validation studies are required before AI-assisted competency assessments can be considered standalone decision-support tools for nurse recruitment.