Objective To accurately predict the outpatient and emergency visits of a district-level public hospital based on autoregressive integrated moving average (ARIMA) model, providing important basis for hospital budget planning and operational decisions. Methods The monthly outpatient and emergency visits of a public hospital in Shuangliu District, Chengdu City from January 2012 to November 2023 were collected, and R 4.3.1 software was used to establish an ARIMA model based on the data from January 2012 to December 2022. The outpatient and emergency visits from January to November 2023 were predicted and validated. Results Except for January and March 2023, every monthly number of predicted outpatient and emergency visits for 2023 matched the actual one relatively well. The average absolute percentage error for January to November 2023 was 8.504%. The actual total number of outpatient and emergency visits from January to November 2023 was 1441960, and the predicted value was 1417130 with a relative error of –1.722%. Conclusions ARIMA model can predict the outpatient and emergency visits of district-level hospitals relatively well. However, factors such as the high incidence of COVID-19 may affect the accuracy of short-term prediction.
ObjectiveTo understand the current status of healthcare human resources (HHR) in a large public hospital, predict the HHR demand aligned with the development of the hospital, and provide reference basis and feasible solutions for HHR planning for high-quality development of the large public hospital. MethodsBased on grey model and auto regressive integrated moving average model, a variance reciprocal method for weight allocation was applied to set up the combination forecasting model. Different types of HHR demand of the large public hospital from 2024 to 2026 were predicted and the accuracies of the three different model predictions were compared. ResultsThe numbers of total personnel, health technical personnel, physicians, nurses, and technicians predicted by the combination forecasting model for 2026 were 17654, 13041, 4389, 6198, and 2264, respectively. The corresponding average annual growth rates from 2024 to 2026 were 5.54%, 5.55%, 5.37%, 4.27%, and 5.60%, respectively. Compared with the two single forecasting models, the combination forecasting model had the smallest average absolute errors, mean squared errors, and mean absolute percentage errors for predicting the numbers of total personnel, nurses, and technicians. It also had the smallest average absolute error and mean absolute percentage error for predicting the number of health technical personnel, and the smallest average absolute error for predicting the number of physicians. ConclusionsCompared with the single forecasting model, the combination forecasting model shows fewer system errors and better predictive results. The demand for total personnel, health technical personnel, physicians, nurses, and technicians of this large public hospital will continue to increase, so planning and reserving staff in advance is a key to high-quality development of the hospital.