Leveraging Predictive Analytics and Redis-Backed Caching to Optimize Specialty Medication Fulfillment and Pharmacy Inventory Management
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P116Keywords:
Predictive Analytics, Redis Caching, Pharmacy Inventory Management, Specialty Medications, Machine LearningAbstract
Specialty medications have long shelf lives, high costs, and demand patterns that are often unpredictable, all of which makes specialty medication inventory management a big challenge for the pharmaceutical industry. This work explores the use of predictive analytics in combination with Redis backed caching systems, to streamline specialty medication dispensing and cost effective management of pharmacy inventory. The key aim is to conduct regression analysis for ML-based demand forecasting performance, evaluate real-time inventory systems with Redis caching performance, and evaluate gains in Order Fulfillment Rate performance measurement. This study explored quantitative research design using secondary data from pharmacy management systems and health care published databases. Specifically, the hypothesis was that integrated predictive analytics along with Redis caching has a huge impact on reducing stockouts and enhancing fulfillment efficiency. And tests showed that pharmacies that use these technologies had 23% lower inventory holding costs and 31% better order fulfillment accuracy. These predictive models achieved 87% accuracy in forecasting conclusions for specialty medications. Post discussion, implementation of Redis cache showed 78% of latency of database queries were vastly reduced, and this helped in making inventory real-time decisions. Integrating technology to improve pharmacy efficiency and clinical outcomes for specialty medications: An updated review of the literature Abstract Background The impact of technology integration into specialty pharmacy practice is documented in the literature; however, these studies are relatively scarce and often outdated
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