The Covid-19 pandemic situation and cancer burden trend lead the changes in the chemotherapy drugs demand. Chemotherapy can have several cycles of administration that take months. Moreover, delays can occur if the patient's physical condition is not ready to undergo the process of administration, and the data will be complete after several months. The availability of new patients' arrival data is faster than the demand data. The approach to integrating this arrival data into the economic order quantity (EOQ) model is the focus of this study. The demand rate is estimated using new patient arrivals data and the constant transition probabilities. Model development using infinite geometric series and validated by simulation using a spreadsheet. The results show the effectiveness of new patients' arrivals data replacing the demand data with the resulting sensitivity equivalence. Inventory decisions are sensitive when new patients' arrival rates are close to zero.