Forecasting of Daily Outpatient Visits Based on Genetic Programming

Background: the forecasting of daily outpatient visits has significant practical implications in outpatient clinic operation management, not only contributing to guiding long-term resource planning and scheduling but also making tactical resolutions for short-term adjustments on special days such as holidays. we here in propose an effective genetic programming (gp)-based forecasting model to predict daily outpatient visits (ov) in a primary hospital. methods: in the gp-based model, the holiday-based distance outlier mining algorithm was used to determine the holiday effect. in addition, solar terms were applied as the smallest unit to more accurately determine the impact of a change in the climate on the outpatient volume. a segmental learning strategy also was used to predict the daily outpatient volume for the time series data. results: the gp-based prediction could more effectively extract depth information from a finite training sample size and achieve a better performance for predicting daily outpatient visits, with lower root mean square error (rmse) and higher coefficient of determination (r^2) values, than the seasonal autoregressive integrated moving average (sarima) model in the time range of holidays and the holiday effect. conclusion: gp-based model can achieve better prediction performance by overcoming the shortcomings of the sarima model. the results can be applied to support decision-making and planning of outpatient clinic resources, to help managers implement periodic scheduling of available resources on the basis of periodic features, and to perform proactive scheduling of additional resources.