AIML-based Solution for Medication Error Reduction and Resource Optimization in Healthcare

This paper details the process of developing an artificial intelligence (AI)-powered system that can optimize medication distribution and will improve resource utilization and reduce medication reconciliation errors. The suggested mechanism will use a hybrid solution combining the optimization algorithms, Bayesian networks, and linear regression to enhance effectiveness and efficiency of drug management processes. Gradient descent is applied as a computational technique to optimize delivery performance, and Bayesian networks to reason about uncertainties when making more informed choices. Data on the demand of patient medication will be used to predict the demand using linear regression and historical data. Experimental results indicate that the hybrid AI approach decreases the number of medication errors by $20 \%$ and enhances resource use by $15 \%$. The results demonstrate how mathematical models powered by AI have a high potential to possibly enhance healthcare delivery by enhancing distribution of medications.