Predicting Wait Times in Pediatric Ophthalmology Outpatient Clinic Using Machine Learning

Wei Chun Lin, Isaac H. Goldstein, Michelle R. Hribar, David S. Sanders, Michael F. Chiang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1121-1128
Number of pages8
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
StatePublished - 2019
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)


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