Abstract: Machine learning models can assess historical insurance claims data and prescription fulfillment records to reduce paybacks to prescription payers in the form of direct and indirect remuneration fees. A pharmacy computer receives prescription data and communicates at least a portion of the prescription data to a pharmacy benefit manager. The pharmacy computer receives a prescription benefit response back at the computerized application from the prescription payer software application. The pharmacy computer is configured to develop machine learning request data, selected from the prescription data and prescription benefit response data, and send the machine learning request data to machine learning software. The machine learning software calculates payback forecast data for the prescription data.

Abstract: Machine learning models can assess historical insurance claims data and prescription fulfillment records to reduce paybacks to prescription payers in the form of direct and indirect remuneration fees. A pharmacy computer receives prescription data and communicates at least a portion of the prescription data to a pharmacy benefit manager. The pharmacy computer receives a prescription benefit response back at the computerized application from the prescription payer software application. The pharmacy computer is configured to develop machine learning request data, selected from the prescription data and prescription benefit response data, and send the machine learning request data to machine learning software. The machine learning software calculates payback forecast data for the prescription data.