IntegriChain Artificial Intelligence/Machine Learning Innovation

IntegriChain Artificial Intelligence/Machine Learning Innovation

Employing advanced data science and analytics to help patients start therapy faster and stay on therapy longer

Our Data Science and Innovation teams employ a variety of leading-edge AI/ML methodologies to deliver insights that help save patient days of therapy. The teams utilize a number of AI/ML models – including Random Forest Classification, XGBoost Classification, Kalman Filter, K-Nearest Neighbor, K-Means Clustering, and ARIMA Modeling.

Below are just some of the innovations we’ve developed.


Patient Initiation Risk Scores

We use a variety of data sets to predict the likelihood that patients who are referred for Specialty medication will not be able to initiate on therapy due to patient journey roadblocks such as their insurance company denying coverage.

Patient Adherence Risk Scores

We use a variety of data sets to predict the likelihood that patients who are already on therapy for Specialty medication will discontinue therapy before their next refill.

Inventory Demand Projection

We employ disparate data sets to project future inventory demand for each channel and National Drug Code (NDC) combination.

Automated Pharmacy Selection for Pharmacy Call Programs

We produce statistically significant lists of pharmacies for the Pharmacy Call Programs that allow for intelligent, focused, and efficient outreach to pharmacies, maximizing the investments in these programs.

NDC/Brand Similarity Algorithms

We employee NDC and brand-level features–such as channel level sales distribution, therapeutic category, and WAC price–to determine similar NDCs/brands for various analytic projects.


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