Patient perceptions of wait time during outpatient office visits can affect patient satisfaction. Providing accurate information about wait times could improve patients’ satisfaction by reducing uncertainty. However, these are rarely known about efficient ways to predict wait time in the clinic. Supervised machine learning algorithms is a powerful tool for predictive modeling with large and complicated data sets. In this study, we tested machine learning models to predict wait times based on secondary EHR data in pediatric ophthalmology outpatient clinic. We compared several machine-learning algorithms, including random forest, elastic net, gradient boosting machine, support vector machine, and multiple linear regressions to find the most accurate model for prediction. The importance of the predictors was also identified via machine learning models. In the future, these models have the potential to combine with real-time EHR data to provide real time accurate estimates of patient wait time outpatient clinics.

Learning Objective: Learn the importance and possible solution for predicting patient wait time in pediatric ophthalmology outpatient clinic.

Authors:

Wei-Chun Lin (Presenter)
Oregon Health & Science University

Isaac Goldstein, Oregon Health & Science University
Michelle Hribar, Oregon Health & Science University
David Sanders, Oregon Health & Science University
Michael Chiang, Oregon Health & Science University

Presentation Materials:

• Abstract/Manuscript - 1