Evaluating Risk Factors Affecting Employee Overload in Healthcare Institutions Using Machine Learning Models: Predictions Based on Health Screening Indicators
學年 113
學期 1
發表日期 2025-01-22
作品名稱 Evaluating Risk Factors Affecting Employee Overload in Healthcare Institutions Using Machine Learning Models: Predictions Based on Health Screening Indicators
作品名稱（其他語言）
著者 Chen, Ming-shu; Yang, Wen-jen; Yang, Chih-te; Liu, Tzu-chi; Yang, Ching-tan; Lu, Chi-jie
作品所屬單位
出版者
會議名稱 30th International Symposium on Artificial Life and Robotics, 10th International Symposium on BioComplexity (AROB-ISBC 2025)
會議地點 Beppu, Japan
摘要 Taiwan’s National Health Insurance (NHI) system only utilizes 6-7% of the nation's GDP, yet it provides high healthcare quality and highly affordable services. However, healthcare staff are under considerable pressure, further exacerbating the strained workforce. This study applies six machine learning algorithms to explore important risk factors related to the excessive workload (overload) of healthcare institution staff, highlighting the significance of understanding these factors. Workload overload refers to the physiological state caused by long-term high stress, a serious 21st-century health issue. In Taiwan, the standard for recognizing overwork is based on workload during the six months and before the onset of illness, as outlined by the "Guidelines for Preventing Diseases Triggered by Abnormal Workload" from the Occupational Safety and Health Administration (OSHA) of the Ministry of Labor, Taiwan. These guidelines serve as a reference for defining working hours and overwork for both employers and employees. The Ministry of Labor mandates annual health checkups for workers across various industries in Taiwan, which includes an assessment of employee overload using the "Employee Overwork Assessment Scale.".
關鍵字 healthcare institutions;overload assessment;employee workload;machine learning;health screening
語言 en
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20250122~20250124
通訊作者
國別 JPN
公開徵稿
出版型式
出處