Advanced Analytics- Pharmaceutical Retail
Using Intuceo’s proprietary analytic and predictive technology, a solution was developed for a large supermarket chain to help mitigate patient non-compliance.
- To know which customers to engage
- A model that could account for the risk of non-compliance per patient and medication.
- To predict whether a particular customer would likely comply with a particular prescription.
- To ultimately intervene at the right time with the right patient at the right pharmacy.
At Intuceo’s core are five patent-pending analytic and predictive engines:
- Data SharpTM pre-processing wizard – rapidly binned and cleaned a sample dataset having 18 initial attributes and aggregated the attributes by patient-medication combination. It then identified the subset of 10 most important attributes that explained the target attribute, which represented the desired outcome
- Quick InsightsTM preliminary insight validator – generated high-level insights that validated certain client assumptions and invalidated others—for example, the assumption that patients taking medications for non-life-threatening chronic conditions were most at risk of non-compliance was proven false.
- Hidden InsightsTM deep insight extractor – extracted valuable hidden insights were presented to the client as simple if/then rules, which were “translated” into easy-to-understand statements that even non-specialists could understand.
- Bold VistasTM predictive modeler – built predictive models that could be integrated into the client’s existing BI system for real-time analysis of incoming CRM data.
- Identified which patients were more or less likely to comply with which prescribed medications.
- Enabled the client to plan interventions with pharmacy customers at risk of non-compliance.
- Empower the client to generate ongoing knowledge from its data wealth that would continue to drive business decisions.
Hundreds of insights were generated that would not have been discovered using off-the-shelf tools, and the project was completed in a fraction of the time it would have taken data scientists to crunch the numbers using traditional analytics.
The iCube team left the client with an understanding of which factors were driving patient non-compliance at its pharmacies, why that non-compliance was happening, and how to mitigate the issue for the health of its customers and the company balance sheet.