An efficient work schedule for medical interns at Chris Hani Baragwanath Academic Hospital

Abstract

Medical internship is a period associated with a high risk of fatigue and burnout mainly due to long working hours, negatively impacting the interns’ wellbeing, learning experience, and quality of patient care (Gunasingam et al., 2015). The Health Professionals Council of South Africa (HPCSA) serves as the regulatory body overseeing internship training and sets regulations for training institutions, such as Chris Hani Baragwanath Academic Hospital (CHBAH), to comply with. The objective of this study is to develop an efficient work schedule for CHBAH interns compliant with HPCSA regulations, ultimately contributing to reduced burnout, enhanced intern well-being, improved learning experience and potentially improved patient care quality. This study demonstrated that the working hours on the actual work schedules for CHBAH interns during the General Medicine, Obstetrics and Gynaecology, and General surgery rotations were indeed non-compliant with HPCSA regulations and that additional interns will be required during those rotations to ensure compliance. To develop an efficient work schedule for CHBAH interns compliant with HPCSA regulations; ChatGPT 4.0, which is Generative Artificial Intelligence (AI), was used in this study and the model was initially trained to fully account for the HPCSA regulations. Following the training of ChatGPT 4.0, the ideal generic intern work schedule was created and coded, with the aim to develop an efficient intern schedule for CHBAH interns for each rotation, adhering to both the HPCSA regulations and the department’s unique work structure. By developing an efficient work schedule compliant with HPCSA regulations, interns at CHBAH will experience less risk of fatigue and burnout, and therefore enhanced intern well-being, improved learning experience and potentially improved patient care quality.