A Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling

Abstract

Effective work scheduling for clinical training is essential for medical education, yet it remains challenging. Creating a clinical training schedule is a difficult task, due to the complexity of curriculum requirements, hospital demands, and student well-being. This study proposes the Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling (CCP-AI-MWS) to optimize clinical training schedules. The CCP-AI-MWS integrates the Collaborative Requirement Planning principle with Artificial Intelligence (AI). Two experiments have been conducted comparing CCP-AI-MWS with current practice. Results show that the newly developed protocol outperforms the current method. CCP-AI-MWS achieves a more equitable distribution of assignments, better accommodates student preferences, and reduces unnecessary workload, thus mitigating student burnout and improving satisfaction. Moreover, the CCP-AI-MWS exhibits adaptability to unexpected situations and minimizes disruptions to the current schedule. The findings present the potential of CCP-AI-MWS to transform scheduling practices in medical education, offering an efficient solution that could benefit medical schools worldwide.