Personalizing pharmacy care to improve patient outcomes
WalgreensDatabricks SQL · Delta Lake
Walgreens migrated off legacy Teradata and Hadoop infrastructure to the Databricks Data + AI Platform to build an Information, Data, and Insights (IDI) platform processing 40,000 data events per second, forecasting medication demand and delivering near real-time patient insights to pharmacists across nearly 9,000 locations serving 825 million prescriptions annually. This saved millions of dollars in supply chain costs and increased store productivity by 20%.
Overview
Walgreens migrated off legacy Teradata and Hadoop infrastructure to the Databricks Data + AI Platform to build an Information, Data, and Insights (IDI) platform processing 40,000 data events per second, forecasting medication demand and delivering near real-time patient insights to pharmacists across nearly 9,000 locations serving 825 million prescriptions annually. This saved millions of dollars in supply chain costs and increased store productivity by 20%.
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Inspect the highlighted sourceThe challenge
Walgreens legacy on-premises solution was built on Teradata and Hadoop technologies that were both costly and unable to scale. Models were built on old data, they could not be updated in a timely fashion, and were difficult to deploy. There was about a 48-hour turnaround time to make the data from store operations usable, and limited insights into the supply chain resulted in both online and in-store order issues and miscalculated inventory levels.
The solution
Using the Databricks Data + AI Platform, Walgreens created an intelligent data platform that is able to forecast demand and redistribute medications across its network of nearly 9,000 pharmacies, while delivering near real-time insights and recommendations for pharmacists. The platform notifies pharmacists of potential drug interactions, changes in drug profiles, and other notes to discuss with patients during prescription refills.
Reported business value
Walgreens has optimized their supply chain by right-sizing inventory levels to save millions of dollars. Store stocking requirements are now determined by data, along with modern demand planners in updated pharmacy systems. These tools have increased productivity by 20%.
Sources
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