Application of Artificial Intelligence-Based Technologies for Evaluating a Healthcare Provider’s Maturity in the Healthcare Worker Onboarding Process

Abstract

This study examines the role of Artificial Intelligence (AI)-driven tools in improving the onboarding process for healthcare workers, with a focus on efficiency, effectiveness, and overall employee integration. In response to growing complexities in healthcare operations, the research investigates how AI can streamline administrative onboarding tasks and support workforce preparedness. It also identifies the operational, structural, and leadership-related challenges that affect the standardization of onboarding practices across healthcare organizations.
A fully quantitative approach was adopted, utilizing a structured online questionnaire distributed to healthcare professionals from various departments and organizational sizes. A total of 204 responses were collected using a simple random sampling method. Data were analyzed using statistical tools in Python, including regression analysis, Spearman correlation, and ANOVA, to evaluate relationships between AI familiarity, onboarding outcomes, and key influencing variables.
The findings reveal that AI tools are perceived to significantly reduce onboarding time and improve data accuracy. Respondents also acknowledged a positive impact on employee satisfaction, though this relationship was less statistically strong. Key challenges identified in the onboarding process include documentation delays, credentialing issues, inconsistent leadership engagement, and unclear HR policies. These issues were found to be systemic and more pronounced in larger healthcare organizations.
The study also highlights the importance of human-centered onboarding elements. Training quality, leadership support, and team collaboration were strongly correlated with onboarding success, while orientation, training, and mentorship were found to significantly enhance employee engagement and retention.