ENHANCING PHARMACY OPERATIONS THRU PREDICTIVE ANALYTICS AND MACHINE LEARNING

Abstract

The traditional pharmacy operations are inefficient, laborious, and subject to 
human error.  Drug dispensing errors, order filling delays, inaccurate stock 
management, data, unauthorized entry, and pharmacist workloads are all results of the 
issues.  Most of these issues have been lessened by pharmacy automation technologies, 
such as automated dispensing robots and micro-fulfillment centers, which lower 
medication errors.  Real-time stock management systems maximize stock levels while 
minimizing waste and out-of-stock scenarios.  Accurate documentation and patient 
confidentiality are improved by computerized data collection and interface with 
electronic health records.  Pharmacists can offer more clinical and patient-centered 
services, increasing patient satisfaction and results, by being freed from repetitive 
manual labor.  Because of the increased operational effectiveness, cost savings, and 
regulatory compliance, pharmacies are now better equipped to grow their services in a 
safe and sustainable manner.