University of Florida researchers are developing a new artificial intelligence tool that will help clinicians identify patients at high risk for opioid use disorder and overdose.
The tool will use data from patients’ electronic medical records to guide clinicians in safely and effectively prescribing opioid medications. The project is supported by a five-year, $3.2 million grant from the National Institute on Drug Abuse (NIDA) and aims to reduce the rise in opioid overdose and opioid use disorder in the United States.
For UF researchers, identifying high-risk patients begins by leveraging ongoing NIDA-funded work and an analysis of healthcare claims data using a type of AI called machine learning. This NIDA grant will use electronic health records or integrated healthcare data to help clinicians identify patients most susceptible to opioid use disorder and overdose. The data analysis requires advanced AI technology, which UF provides researchers through its HiPerGator AI supercomputer.
Researchers estimates the new algorithm will accurately identify between 70-90% of high-risk patients. The algorithm will exclude the large majority of prescription opioid users with negligible opioid use disorder or overdose risk, while evaluating the benefits and risk tradeoffs of prescription opioid use for high-risk patients.
The second part of the NIDA grant involves designing and developing a clinical decision support tool that integrates AI-based risk scores to warn clinicians about high-risk patients. The tool will be integrated into patients’ electronic health records and provide clinicians with early warnings and risk mitigation strategies.
