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In AJR. American journal of roentgenology

OBJECTIVE. Outpatient appointment no-shows are a common problem. Artificial intelligence predictive analytics can potentially facilitate targeted interventions to improve efficiency. We describe a quality improvement project that uses machine learning techniques to predict and reduce outpatient MRI appointment no-shows. MATERIALS AND METHODS. Anonymized records from 32,957 outpatient MRI appointments between 2016 and 2018 were acquired for model training and validation along with a holdout test set of 1080 records from January 2019. The overall no-show rate was 17.4%. A predictive model developed with XGBoost, a decision tree-based ensemble machine learning algorithm that uses a gradient boosting framework, was deployed after various machine learning algorithms were evaluated. The simple intervention measure of using telephone call reminders for patients with the top 25% highest risk of an appointment no-show as predicted by the model was implemented over 6 months. RESULTS. The ROC AUC for the predictive model was 0.746 with an optimized F1 score of 0.708; at this threshold, the precision and recall were 0.606 and 0.852, respectively. The AUC for the holdout test set was 0.738 with an optimized F1 score of 0.721; at this threshold, the precision and recall were 0.605 and 0.893, respectively. The no-show rate 6 months after deployment of the predictive model was 15.9% compared with 19.3% in the preceding 12-month preintervention period, corresponding to a 17.2% improvement from the baseline no-show rate (p < 0.0001). The no-show rates of contactable and noncontactable patients in the group at high risk of appointment no-shows as predicted by the model were 17.5% and 40.3%, respectively (p < 0.0001). CONCLUSION. Machine learning predictive analytics perform moderately well in predicting complex problems involving human behavior using a modest amount of data with basic feature engineering, and they can be incorporated into routine workflow to improve health care delivery.

Chong Le Roy, Tsai Koh Tzan, Lee Lee Lian, Foo Seck Guan, Chang Piek Chim


MRI, XGBoost, artificial intelligence, machine learning, no-show