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

Abstract

Purpose/Objectives Broken appointments are an important cause of waste in health care. Patients who fail to attend incur costs to providers, deny trainees learning opportunities, and impact their own health as well as that of other patients who are waiting for care. Methods A total of 410,000 appointment records over 3 years were extracted from our electronic health record. We conducted exploratory data analysis and assessed correlations between appointment no‐shows and other attributes of the appointment and the patient. The University of Michigan Medical School's Committee on Human Research reviewed the study and deemed that no Institutional Review Board oversight was necessary for this quality improvement project that was, retrospectively, turned into a study with previously de‐identified data. Results The patient's previous attendance record is the single most significant correlation with attendance. We found that patients who said they are “scared” of dental visits were 62% as likely to attend as someone reporting “no problem.” Patients over 65 years of age have better attendance rates. There was a positive association between receiving email/text confirmation and attendance. A total of 94.9% of those emailed a reminder and 92.2% of those who were texted attended their appointment. Conclusion(s) We were able to identify relationships of several variables to failed and attended appointments that we were previously unknown to us. This knowledge enabled us to implement interventions to support better attendance at Dental Clinics at the University of Michigan, improving patient health, student training, and efficient use of resources.

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

“…Concerning patient's age, our results are similar to those reported by Weber et al in which patients’ age had an impact on the rate of missed appointments in DSC 23 . We showed that patients between 18 and 25 years of age exhibited the highest rate of failed appointments.…”

“…Concerning patient's age, our results are similar to those reported by Weber et al in which patients' age had an impact on the rate of missed appointments in DSC. 23 We showed that patients between 18 and 25 years of age exhibited the highest rate of failed appointments. Our results are also consistent with previous studies such as that by Cunha et al, which demonstrated that patients younger than 42 years old have higher missed appointments, as well as other studies that explained that young adults are potentially less likely to miss work or school days to attend a dental or medical appointment.…”

“…22 Yet, there is a limited number of studies that have investigated the use of ML in dental school clinics (DSC) to prevent patient no shows. 23,24 Therefore, the aim of this study was twofold. One, to perform ML modeling using data from the Midwestern University-Illinois Dental Institute (MWU-DI) electronic health records (EHR) that could identify patients with a likelihood of failing an appointment, while providing a user-friendly system that could alert clinicians and administrators of patients at high risk of missing an appointment.…”

“…Nowadays, machine learning (ML) systems are frequently applied to large databases to formulate accurate predictive models, 1 with encouraging results in decreasing the no‐show rate by 3.4% 22 . Yet, there is a limited number of studies that have investigated the use of ML in dental school clinics (DSC) to prevent patient no shows 23,24 …”

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

“…Dentistry was always at the forefront of health care regarding the development of new biomaterials and approaches that benefit patients on a large scale, such as implementing anesthesia methods for procedures (Choi and Kim 2021), as the example above; developing novel materials for dental implants (Ricci and Terracio 2011); and the public health benefit with fluoride and nonrestorative treatments to prevent or manage caries (Urquhart et al 2019). Nowadays, its creative approach has not ceased to expand to other medical and technology fields, including biosensors, e-health, learning health systems, and data sciences (Al Turkestani et al 2021; Weber et al 2021; Benoit et al 2022; Pundir et al 2022).…”