Research on Drug Demands Forecasting in Medical Institutions Based on an ARIMA Model: A Case Study of Statins

OBJECTIVE  To construct an autoregressive integrated moving average(ARIMA) model for predicting statins demands, to provide medical institutions with a reliable tool to formulate drug procurement plans, especially for the scientific reporting of drug demands in centralized procurement.

METHODS  Monthly data on statins usage from January 2022 to June 2024 were collected from a tertiary hospital. The defined daily dose frequency(DDDs) was calculated, and an ARIMA model was constructed. The statins usage intensity data of the hospital from July to December 2024 were used for internal validation to assess the model’s predictive performance. Additionally, data from two other tertiary hospitals from January 2022 to December 2024 were collected for external validation of the model.

RESULTS  The monthly DDDs of statins in medical institutions exhibited seasonal periodicity and irregular fluctuations. A multiplicative seasonal ARIMA(1, 1, 0)(0, 1, 0)₁₂ model was constructed to fit the series, with a BIC value of 439.1. The residual series passed the Ljung-Box Q-test. Both internal and external validations indicated that the relative errors between the model's predicted values and the actual DDDs were acceptable, and the model had high prediction accuracy.

CONCLUSION  The ARIMA model constructed in this study demonstrated good performance in predicting statin usage and can provide a reference for drug procurement and scientific reporting of drug demands in medical institutions.