Situation: Several applications had been incubated from pharmacy claims data to support customer facing programs delivered to retail pharmacies. The business desired to scale up the number of these applications and increase the amount of data covered. However, existing processes provided significant challenges.
Challenges: Batch processing of data to support ad hoc applications meant more time cleaning and manipulating data then doing analytics. Turnaround times stretched out weeks vs. hours.Each application maintained independent data cleaning and processes, leading to data inconsistencies. Each application / technology stack needed to maintain compliance to stringent HIPAA and internal security protocols for handling protected health information.
Solution: Created a data jail for protected health information (PHI), and re-pointed applications to new de-identified, clean patient data sets. Reduced risks for holding protected data in multiple locations and locked down data access. Applications requiring protected health information were held apart with more robust logging standards (to isolate activity in case of breach)
Results: Increased breadth of data, frequency and number of data feeds to increase customer base for customer facing programs. Jump started new applications based on data, including several new customer facing analytics tools. Reduced time to value for these applications by maintaining centralized analytic data as a resource. Reduced processing redundancy between applications with a single source of claims data to support all applications. Created common de-duplication, field calculations and better overall data consistency.
pharmacyanalytics.com 
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