Forecasting Chronic Drug Demand Based on BPJS Kesehatan Claims Data Using the ARIMA Model in Gunungkidul Regency, Special Region of Yogyakarta, Indonesia

Abstrak

Chronic diseases require continuous pharmacotherapy and generate sustained demand for essential medicines, particularly within universal health coverage systems. In Indonesia, pharmaceutical utilization under the National Health Insurance program is documented through administrative claims data, which provide an important basis for demand analysis and planning. This study aims to forecast chronic drug demand in Gunungkidul Regency, Special Region of Yogyakarta, using health insurance claims data and a time-series forecasting approach. A retrospective analysis was conducted using weekly aggregated claims data. Drug utilization patterns were examined, and demand forecasting was performed using the Autoregressive Integrated Moving Average model following standard time-series procedures. Forecast accuracy was assessed by comparing predicted values with observed utilization. The results indicate that the model effectively captures weekly demand patterns and short-term fluctuations, producing forecasts that closely align with actual utilization trends. These findings demonstrate that time-series forecasting based on claims data can provide reliable estimates of chronic drug demand. The study highlights the potential value of integrating forecasting models into pharmaceutical inventory planning to support timely drug availability and improve logistics efficiency within regional health insurance implementation.