Abstract
BackgroundAccurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed.MethodsThe ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo.ResultsFor TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively.ConclusionsThe hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.
Highlights
Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation
The selected test cases cover time series data of stationary, with uptrend or downtrend, with both periodic and trend, etc. and the prediction results of the hybrid model are compared with the traditional auto-regressive integrated moving average (ARIMA) model to evaluate prediction accuracy and applicable range
In this work, an integration of a traditional ARIMA model and a self-adaptive filtering model is proposed to forecast the demand of medical service
Summary
Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The medical resources allocation is increasingly concerned by the management of healthcare service provider since it is directly related to the timely delivery of medical services. Good understanding on the medical service demand calls for analysis on the current and historical amount of the medical treatment delivered, and relies on accurate predicting of the trend in the near future. Such trends provide invaluable information for needs assessment, resource planning, facilities evaluation and policy formulations. A reliable health demand forecasting (e.g. the outpatient visits in different departments of a hospital) can create alerts for the management of patients’ overflows and scientifically allocate critical medical resources so as to reduce the costs in supplies and staff redundancy
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