Machine learning model closely predicts patient waiting times for CT, MRI

"We noticed that most patients who were dissatisfied with the displayed waiting times were delayed for longer than predicted, so the need for more accurate models became imminent," Curtis et al. said. "We also wanted to predict not only waiting times for walk-in facilities, but also delays for the scheduled facilities." Stepping outside of existing research, Curtis and her co-authors zoned in on machine learning, an artificial intelligence modality that can reflect sophisticated trends otherwise difficult to capture with traditional regression approaches. Machine learning models can also resist noise, adapt to changing environments and run without human supervision, the researchers wrote, which fit the needs of a waiting room to a T. The team considered CT exams, MRI, ultrasound and radiography--only the last of which offered walk-in appointments--for the study. They evaluated 10 machine learning algorithms, including neural network, random forest, support vector machine, elastic net, multivariate adaptive regression splines, k-th nearest neighbor and linear regression, to find the algorithm that most closely predicted waiting times.