High inpatient opioid exposure is associated with increased risk of persistent opioid use. Early identification of high-risk patients may improve opioid stewardship. We developed machine learning models to predict high opioid exposure during hospitalization using electronic health record data from MIMIC-IV. We conducted a retrospective study of 223,452 unique first hospital admissions in MIMIC-IV. The outcome was high opioid exposure, defined as the top decile among opioid-exposed admissions (MME/day ≥ 225), representing 2.65% of all admissions. Structured early-admission features included demographics, admission characteristics, laboratory utilization and abnormality summaries, and 24-hour procedural indicators. Discharge-note data were incorporated using ClinicalBERT embeddings and interpretable bigram features. Models were trained using an 80/10/10 split and evaluated with temporal validation on the most recent 10% of admissions. Performance was assessed using ROC-AUC and PR-AUC with 95% confidence intervals. Among structured-only models, XGBoost achieved the best test performance (ROC-AUC 0.932 [0.924-0.940]; PR-AUC 0.223 [0.193-0.262]). The combined structured and notes model improved precision-recall performance (ROC-AUC 0.932 [0.920-0.943]; PR-AUC 0.276 [0.229-0.331]). Temporal evaluation showed similar discrimination (ROC-AUC 0.929; PR-AUC 0.223). High-risk bigrams included procedural terms such as “external fixation” and “cervical discectomy.” Integration of structured and text-derived features improved risk stratification compared to structured data alone. Interpretable bigram signals reflected procedural complexity and orthopedic pathology, reinforcing the clinical plausibility of model predictions. Multimodal EHR-based models accurately predict high inpatient opioid exposure and may support targeted opioid stewardship during hospitalization.

AUTHOR SUMMARY Opioid medications are commonly used in hospitals to treat pain, but some patients receive very high doses, which may increase their risk of long-term opioid use, with negative side effects such as opioid addiction. Identifying patients at risk early during their hospital stay could help physicians make safer prescribing decisions. In this study, we used electronic health record data from over 220,000 hospital admissions to develop computer models that estimate the likelihood that a patient will receive high levels of opioids. We focused on information available within the first 24 hours of admission, including basic patient characteristics, laboratory testing patterns, and procedures. We also used information from clinical notes to capture additional context about patient care.

We found that these models were able to accurately identify patients at higher risk, and that combining structured data with information from clinical notes improved performance. Importantly, the patterns identified by the models, such as certain surgical procedures, were consistent with clinical expectations. Our findings suggest that routinely collected hospital data can be used to support earlier identification of patients at risk for high opioid exposure. This approach could help guide more targeted and cautious opioid prescribing practices in inpatient settings.

The authors have declared no competing interest.

The author(s) received no specific funding for this work.

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This study was exempt from additional institutional review board approval. All authors completed the required data use training and complied with the PhysioNet data use agreement.

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The data used in this study is available from the MIMIC-IV database, which is hosted on PhysioNet (https://physionet.org/).

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