Afshar M, Sharma B, Dligach D, Oguss M, Brown R, Chhabra N, Thompson HM, Markossian T, Joyce C, Churpek MM, & Karnik NS (2022). Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. The Lancet (British Edition), 4(6), e426–e435. https://doi.org/10.1016/S2589-7500(22)00041-3
SMART-AI is a substance misuse algorithm to support referral to treatment using artificial intelligence. The tool is a machine learning classifier tool for identifying alcohol misuse, opioid misuse, and non-opioid drug misuse using clinical notes collected in the electronic health records. Using of patients (N=16,917) during the first 24 hours of hospitalization, the prospective primary analysis consisted of temporal validation done to examine misuse classification and the association to outcomes and treatment referrals. Results from manual screening identified 3.5% of patients had any type of substance misuse and 11% of these patients had more than one type of misuse. SMART-AI showed good calibration and validity, with a false negative rate of 0.18-0.19 and a false positive rate of 0.03 between non-Hispanic Black and non-Hispanic White subgroups. The results also show prediction performance can change over time or in differing patient settings, where prevalence of substance misuse varies. There were also significant changes during the COVID-19 pandemic which required the algorithm to be recalibrated. Overall, this study demonstrated that clinical notes from the electronic health record during initial hospitalization can be used to identify substance misuse accurately with the help of artificial intelligence and may be used to potentially improve screening rates.