WASTE ANALYSIS IN IN-PATIENT PHARMACEUTICAL DISPENSING SYSTEM BY LED GUIDE AND CONVEYOR BELT: AN APPLYING FROM DATA MINING TECHNIQUES - Bualoy, Peeratach; Detchit, Patawee; Chalortham, Nopphadol; Kitikannakorn, Nantawarn (Thai Bulletin of Pharmaceutical Sciences, 2025)
Maharaj Nakhon Chiang Mai University Hospital implemented a daily dose medication distribution system with an automated conveyor for safe and effective medication management. However, delays during peak hours and medication errors have been observed. To identify waste in the medication distribution process and analyze frequently co-prescribed medications using data mining and association rule techniques to suggest improvements. A quantitative analysis of prescription data from April 1, 2022, to March 31, 2023, used process flow mapping and WASTE analysis. Data mining and association rule discovery in RapidMiner Studio analyzed co-prescribed medications, identifying associations among pairs, triples, and quadruples. Key statistical measures, including support, confidence, and lift, were calculated. The study analyzed seven zones of medication cabinets, focusing on a conveyor belt that completes a rotation in 84 seconds and has five ejection stations. On average, 1,853 medication orders are processed daily, with 1,391 entering through the guided cabinets and conveyor. The study used Frequent Pattern Growth to identify 151 co-prescription rules and found high error rates, mainly under-prescribed quantities, in Zone EL2 (injectable medicine). It also showed all eight wastes of DOWNTIME, including defects in error reports and overproduction from pre-packaging excess medications. By managing these issues, we can reduce waste linked to wait times and unnecessary movement by staff refilling medications in short supply. This enables staff to focus on other important tasks. Additionally, transportation and extra processing waste can be recognized through unnecessary ejections. The study identified eight types of waste in the pharmaceutical dispensing system and emphasized the need for continuous quality improvement based on lean principles to reduce waste and enhance efficiency. By optimizing storage, workflow, and staffing, as well as recognizing commonly co-prescribed medications, the process can be better organized. Using data analysis is essential for managing medications more effectively and minimizing medication errors.