IMPLEMENTATION OF APRIORI ALGORITHM IN PREDICTING THE NEED FOR DISPOSABLE MEDICAL MATERIALS

Abstract

This research addresses a persistent challenge encountered by healthcare institutions: the management of suboptimal inventories of disposable medical supplies (BMHP). Variability in the demand for BMHP frequently leads to either shortages or surplus stock, adversely impacting patient care quality and resulting in resource wastage. In July, it was noted that 200 folded gauze and 109 ENT tampons were not sterilized by the central sterilization unit due to a lack of demand from other departments. This situation illustrates the occurrence of waste, as the remaining BMHP could have been utilized effectively; however, the time spent on manufacturing could have been better allocated to enhance service delivery if managed appropriately. To address this issue, the study employs a data-driven methodology utilizing the Apriori algorithm, selected for its proven capability to uncover purchasing patterns of BMHP within extensive and intricate transaction datasets. The research process initiates with the gathering of BMHP transaction data over a specified timeframe. The resulting rules will elucidate the interrelationships among various types of BMHP, serving as a foundation for forecasting future requirements. Consequently, healthcare institutions will be better equipped to plan and manage their BMHP inventories with greater effectiveness and efficiency. It is anticipated that the findings of this study will significantly enhance the quality of healthcare services by ensuring adequate availability of BMHP while striving to minimize waste to zero percent.