Pharmacy robotic dispensing and planogram analysis using association rule mining with prescription data

Automation in pharmacies has achieved innovative levels of effectiveness and savings. In the present day, automated pharmacies are facing extremely large demands of prescription orders specifically at the central fill pharmacies that distribute drugs to retail pharmacies. As a result, improvements are necessary to the Robotic Prescription Dispensing System (RPDS) and RPDS planogram to increase the throughput of prescriptions. RPDS planogram defines where to allocate the dispensers inside the robotic unit and how to distribute them among the multiple robotic units. This research utilizes the pharmacy prescriptions database to extract useful knowledge to improve different strategies in pharmacy automation by using a data mining approach. In this study, a data mining tool is proposed to enhance pharmacy automation. Frequent Pattern Growth (FP-growth) approach, which is one of the algorithms of Association Rule Mining (ARM), is applied to an actual prescriptions database of a central fill pharmacy to study the associations within the prescribed drug regime. The FP-growth application in a prescriptions database is novel; thus, FP-growth is tested on both sequential mode, and parallel mode by using a distributed platform Hadoop and MapReduce paradigm. Two types of association rules are extracted: 1) associations among different drugs that involve their different dosage strengths and manufacturers; and 2) associations that include only information about different drug generic and brand names. The importance of the extracted association rules is evaluated by the use of different measures, including the support, confidence, lift and conviction. The discovered rules disclose strong associations among the purchased drugs that improve the allocation and distribution of dispensers among the robotic units, in addition to enhancements in other pharmacy managerial strategies. (C) 2016 Elsevier Ltd. All rights reserved.