Artificial Intelligence (AI) and machine learning are increasingly used to identify patterns in healthcare data that can support earlier detection of high-risk conditions. Opioid misuse remains a major public health challenge, placing a significant burden on healthcare systems and communities. This study explores how machine learning models can be applied to electronic health record (EHR) data to identify patients at elevated risk for opioid misuse before clinical escalation occurs.
Using publicly available healthcare datasets and simulated patient risk indicators derived from prescription patterns, demographic variables, and prior clinical encounters, predictive models were evaluated for their ability to classify high-risk patient profiles. Results suggest that machine learning approaches can identify risk signals earlier than traditional screening methods, enabling timely intervention and referral to recovery support services.
However, the use of Artificial Intelligence (AI) in clinical environments raises ethical and regulatory concerns, including data privacy, algorithmic bias, and the need for clinician oversight. These findings highlight the importance of transparent model design and strong governance frameworks to ensure responsible and equitable use of AI in healthcare.