AI Prescription Reimbursement Analytics Tool
Budget: $250 – $750 CAD
I’m building an AI-driven platform that ingests large volumes of prescription claims and pharmacy-management feeds every day, then pinpoints payer reimbursement variations and hidden cost-saving opportunities. The engine must automatically map NDCs to GPI, AHFS, and GCN codes so brand-to-generic comparisons are seamless, letting us track substitution rates, pricing patterns, and payer behaviors in one view.
Here’s what I need delivered:
• A secure data pipeline that pulls historical and incremental claims plus pharmacy-system exports, normalises them, and refreshes analyses on a daily schedule.
• Algorithms or machine-learning models (Python, SQL, or similar—open to your recommendation) that surface reimbursement outliers, generic substitution gaps, and trend lines across payers.
• An interactive dashboard and exportable report set that translate those insights into action-ready metrics for pharmacy management teams.
• Full audit logging, HIPAA-compliant handling of PHI, and adherence to common pharmacy data standards.
Success looks like near real-time visibility into payer reimbursement variation and concise, shareable insights that directly influence purchasing and formulary decisions. If you’ve tackled healthcare data analytics before and can speak the language of GPI, AHFS, and GCN, I’d love to hear how you’d architect and deliver this solution.
Here’s what I need delivered:
• A secure data pipeline that pulls historical and incremental claims plus pharmacy-system exports, normalises them, and refreshes analyses on a daily schedule.
• Algorithms or machine-learning models (Python, SQL, or similar—open to your recommendation) that surface reimbursement outliers, generic substitution gaps, and trend lines across payers.
• An interactive dashboard and exportable report set that translate those insights into action-ready metrics for pharmacy management teams.
• Full audit logging, HIPAA-compliant handling of PHI, and adherence to common pharmacy data standards.
Success looks like near real-time visibility into payer reimbursement variation and concise, shareable insights that directly influence purchasing and formulary decisions. If you’ve tackled healthcare data analytics before and can speak the language of GPI, AHFS, and GCN, I’d love to hear how you’d architect and deliver this solution.