Ming-Tse Tsai, MD

ED overcrowding is a significant issue in modern medicine across many countries, which not only threatens patient safety but also burns out providers' passion. Among several proposed indicators, ED LOS is the most common one of measurement. Being able to predict ED LOS at a patient’s presentation provides valuable information to all the stakeholders in ED, including patients, providers, and managers. In this study, a predictive model was built, as well as the powerful predictors were identified, via a machine learning method by leveraging the real-world data collected in a medical center in Taiwan. The results benefit in informing future modeling and shed a light to the path towards tackling this complex multifactorial phenomenon.