Leveraging machine learning to create user-friendly models to mitigate appointment failure at dental school clinics.

PURPOSE/OBJECTIVES: This study had a twofold outcome. The first aim was to develop an efficient, machine learning (ML) model using data from a dental school clinic (DSC) electronic health record (EHR). This model identified patients with a high likelihood of failing an appointment and provided a user-friendly system with a rating score that would alert clinicians and administrators of patients at high risk of no-show appointments. The second aim was to identify key factors with ML modeling that contributed to patient no-show appointments. METHODS: Using de-identified data from a DSC EHR, eight ML algorithms were evaluated: simple decision tree, bagging regressor classifier, random forest classifier, gradient boosted regression, AdaBoost regression, XGBoost regression, neural network, and logistic regression classifier. The performance of each model was assessed using a confusion matrix with different threshold level of probability; precision, recall and predicted accuracy on each threshold; receiver-operating characteristic curve (ROC) and area under curve (AUC); as well as F1 score. RESULTS: The ML models agreed on the threshold of probability score at 0.20-0.25 with Bagging classifier as the model that performed best with a F1 score of 0.41 and AUC of 0.76. Results showed a strong correlation between appointment failure and appointment confirmation, patient's age, number of visits before the appointment, total number of prior failed appointments, appointment lead time, as well as the patient's total number of medical alerts. CONCLUSIONS: Altogether, the implementation of this user-friendly ML model can improve DSC workflow, benefiting dental students learning outcomes and optimizing personalized patient care.

Effect of a Pilot Preclinical Incentive Program on Dental Students' Performance on a Clinical Competency Exam.

The aim of this study was to investigate the effect of a pilot preclinical incentive program on dental students' performance on a clinical competency (mock board) exam at Midwestern University College of Dental Medicine-Arizona. To assess the effect of preclinical grade incentives in a program called SUCCEED, scores from a clinical competency exam administered during the fall quarter of the fourth year were compared between the graduating classes of 2014 and 2015 (pre-SUCCEED curriculum) and the graduating class of 2016 (post-SUCCEED curriculum). The study hypothesized that the class participating in the SUCCEED program, with its incentives for greater preclinical preparation and practice, would score higher than the other classes on the exams. The results showed that the endodontics and periodontics pass rates and test scores from the Class of 2016 were higher than those from the Classes of 2014 and 2015; the prosthodontics pass rates were similar; and the operative dentistry pass rates and test scores were lower than the Classes of 2014 and 2015. While the results of two of the four subsections of the competency exam showed an improvement in clinical performance for the Class of 2016, the operative dentistry test scores for that class were less than expected. Based on the increased number of operative dentistry procedures performed in preclinical simulation and the clinic, the authors conclude that the competency exam should not be the only measure to evaluate success of the SUCCEED program.