Using business intelligence and data visualization to understand the characteristics of failed appointments in dental school clinics

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.

The Forefront of Dentistry-Promising Tech-Innovations and New Treatments

KNOWLEDGE TRANSFER STATEMENT

This article discusses innovations in technology and treatments that have enormous potential to revolutionize our dental care, including novel concepts in electronic health records, communication between dentists and patients, biologics around diagnosis and treatment, digital dentistry, and, finally, the real-time optimization of information technology. The early implementation and validation of these innovations can drive down their costs and provide better dental and medical services to all members of our society.