Application of Transformer Neural Networks for Data Cleaning in Emergency Room Logs: A Case Study from the Bordeaux University Hospital

Emergency Rooms (ERs) are at the center of various optimization research due to the growing number of visits in recent decades. The accurate logging of patient movement and time spent in ERs is essential for studying patient pathways and improving operations. Despite the innovative digital tracking system employed at the Bordeaux University Hospital, over 90% of these logs suffer from missing or incoherent values, primarily due to manual entry errors and software changes. This study explores the application of Transformer neural networks for cleaning ER logs at the Bordeaux University Hospital, addressing the challenge of inaccuracies in patient movement records accumulated over ten years. By leveraging a T5 Transformer model, we aim to demonstrate the model's ability to correct these errors by transforming unreliable datasets into valuable resources for optimizing ER operations. Our findings reveal that the Transformer model can achieve an overall accuracy of 95.79% in identifying and correcting faulty entries, showcasing the potential of advanced machine learning techniques in enhancing data integrity for healthcare applications. This research highlights the effectiveness of Transformer networks in data cleaning tasks and opens up opportunities for their application in various fields that are burdened with corrupt sequential data.