Background Expansion of clinical guidance tools is crucial to identify patients at risk of requiring an opioid refill after outpatient surgery. Objective The objective of this study was to develop machine learning algorithms incorporating pain and opioid features to predict the need for outpatient opioid refills following ambulatory surgery. Methods Neural networks, regression, random forest, and a support vector machine were used to evaluate the data set. For each model, oversampling and undersampling techniques were implemented to balance the data set. Hyperparameter tuning based on k-fold cross-validation was performed, and feature importance was ranked based on a Shapley Additive Explanations (SHAP) explainer model. To assess performance, we calculated the average area under the receiver operating characteristics curve (AUC), F1-score, sensitivity, and specificity for each model. Results There were 1333 patients, of whom 144 (10.8%) refilled their opioid prescription within 2 weeks after outpatient surgery. The average AUC calculated from k-fold cross-validation was 0.71 for the neural network model. When the model was validated on the test set, the AUC was 0.75. The features with the highest impact on model output were performance of a regional nerve block, postanesthesia care unit maximum pain score, postanesthesia care unit median pain score, active smoking history, and total perioperative opioid consumption. Conclusions Applying machine learning algorithms allows providers to better predict outcomes that require specialized health care resources such as transitional pain clinics. This model can aid as a clinical decision support for early identification of at-risk patients who may benefit from transitional pain clinic care perioperatively in ambulatory surgery.

UNSTRUCTURED Expansion of clinical guidance tools is crucial to identify patients at risk of requiring an opioid refill after outpatient surgery. The objective of this study was to develop machine-learning algorithms incorporating pain and opioid features to predict need for outpatient opioid refills following ambulatory surgery. Neural networks, regression, random forest, and a support vector machine were used to evaluate the dataset. For each model, over and undersampling techniques were implemented to balance the dataset. Hyperparameter tuning based on k-fold cross validation was performed and feature importance was ranked based on a SHapley Additive exPlanations (SHAP) explainer model. To assess performance, we calculated the average area under the receiver operating characteristics curve (AUC), sensitivity and specificity for each model. There were 1,333 patients, of which 10.8% refilled their opioid prescription within two weeks after outpatient surgery. Of all models, neural networks performed the best. The average AUC calculated from k-fold cross-validation was 0.71. When the dataset was split into 70% training and 30% test set, the AUC was 0.75. The features with the highest impact on model output were performance of a regional nerve block, post-anesthesia care unit (PACU) maximum pain score, PACU median pain score, active smoking history, and total perioperative opioid consumption. Applying machine learning algorithms allows providers to better predict outcomes that require specialized healthcare resources. This model serves as a guide for predictive features that can aid as clinical decision support for early identification of at-risk patients that may benefit from transitional pain clinic care perioperatively in ambulatory surgery.