Context: Emergency departments in the US are often crowded and have long wait times. A large variety of medical problems are treated in emergency departments from basic primary care to life threatening emergencies. Predicting how long someone may stay in the emergency department is a difficult task with many contributing variables that are difficult to track. Health informatics using machine learning methods, in particular deep learning neural networks, present a possible solution to this prediction. This information could then be used to allocate resources more efficiently and help with discharge/admit planning. Objective: Predict the length of stay for each patient encounter using electronic medical record (EMR) data and machine learning. Study Design: Create a prediction model of a patient’s length of stay in the emergency department using retrospective data from the EMR including event logs, demographic data, emergency department census, and medical data (demographic data, care areas, orders, and symptoms).
Setting: All encounters at a large urban community hospital’s emergency department between April 2018 and March 2019. Population Studied: All 83,483 patient encounters were included; there were no exclusions. Instrument: Python programming language using Keras and Tensorflow was used to develop the prediction model. Main Outcome Measure: Accuracy of the neural network model in predicting the length of stay in the emergency department for each patient encounter. Results: The neural network model predicted the length of stay at an accuracy of 90% for the following time categories of: 0 < 1 hours, 1 < 3 hours, 3 < 6 hours, 6 < 12 hours, and >= 12 hours. Conclusions: Machine learning using deep learning neural networks is a promising method for predicting length of stay in the emergency department and potentially in the hospital as a whole. Increasing the data available to the network and adjusting the algorithm could increase the accuracy of the model.