Predictive Analytics for Managing Drug and Alcohol Testing Risks

Abstract

Drug and alcohol testing programs are critical for ensuring workplace safety and compliance with legal standards. However, the current methodologies face significant challenges, including inefficiencies, high costs, and compliance risks. Predictive analytics offers a transformative approach to identifying and mitigating these risks through data-driven insights. This paper explores the integration of predictive analytics into drug and alcohol testing, focusing on risk prediction, model development, and deployment strategies. The research highlights key advancements in machine learning, data preprocessing, and ethical considerations to optimize testing protocols and enhance operational efficiency.