A personalized periodontitis risk based on nonimage electronic dental records by machine learning.

Objective: This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).

Methods: By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing.

A scoping review of patient safety checklists in dentistry.

Objectives: Not much is known about safety checklists use in dentistry. We aim to examine, assess, and provide a comprehensive understanding of the current knowledge concerning the use of checklists to improve patient safety in dentistry.

Methods: We conducted a comprehensive literature search using Medline and Embase for studies that use or describe the development of dental patient safety checklists.

The Nature of Adverse Events in Dentistry.

Objectives: Learning from clinical data on the subject of safety with regards to patient care in dentistry is still largely in its infancy. Current evidence does not provide epidemiological estimates on adverse events (AEs) associated with dental care. The goal of the dental practice study was to quantify and describe the nature and severity of harm experienced in association with dental care, and to assess for disparities in the prevalence of AEs.

The burden of diagnostic error in dentistry: A study on periodontal disease misclassification.

Background: Periodontal disease constitutes a widely prevalent category of non-communicable diseases and ranks among the top 10 causes of disability worldwide. Little however is known about diagnostic errors in dentistry. In this work, by retrospectively deploying an electronic health record (EHR)-based trigger tool, followed by gold standard manual review, we provide epidemiological estimates on the rate of diagnostic misclassification in dentistry through a periodontal use case.

Learning from data in dentistry: Summary of the third annual OpenWide conference.

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.