Optimizing Prescription Practices Using AI-Powered Drug Substitution Models to Reduce Unnecessary Healthcare Expenditures in Outpatient Settings

Abstract

Rising outpatient healthcare costs, particularly due to brand-name and non-optimized drug prescriptions, have created an urgent need for intelligent, cost-saving interventions. Traditional prescribing often overlooks equally effective, lower-cost drug alternatives, leading to significant financial inefficiencies without improving patient outcomes. This study proposes an AI-powered drug substitution model designed to optimize outpatient prescription practices by recommending clinically equivalent and more cost-effective alternatives. The primary goal is to reduce unnecessary healthcare expenditures while preserving treatment quality and physician autonomy. We developed a machine learning-based recommendation system using a comprehensive dataset that includes electronic health records, pharmacy claims, drug equivalency databases, and insurance formulary information. The model was trained to identify optimal drug substitutions based on diagnosis, prescription history, cost, and insurance coverage. Evaluation metrics included substitution precision, potential cost savings, and physician usability testing.