Background: Errors can happen during patient care, and some result in harm to the patient. Work place stress has been well established in dentistry, but its relation with errors in the delivery of patient care is less understood. The authors evaluated the relationship between burnout, work engagement, and self-reported dental errors among American dentists.
Methods: From May to August 2016, a national sample of American Dental Association member dentists were sent a validated, electronic survey assessing their levels of burnout, work engagement, and dental errors.
Results: Of the 391 responding dentists, 46.1% reported concern that they had made a dental error in the last 6 months, 12.1% of the dentists were informed by dental staff that they may have committed an error in the last 6 months, 16% were concerned that a malpractice lawsuit would be filed against them, and 3.6% were actively involved in a malpractice lawsuit. In the adjusted analysis, multivariate logistic regression showed that dentists with either high burnout risk were more likely to report concern over a perceived error within the last 6 months.
Conclusions: The results suggest that dental provider burnout is potentially a key predictor of reporting perceived dental errors. It is imperative that the dental profession continue to study the effects of work-related stress, develop professional practices that decrease burnout, and reduce errors.
Practical Implications: Efforts that minimize the potential for burnout may help reduce the occurrence of errors and improve the quality of care provided to dental patients.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/PTS.0000000000000673 | DOI Listing |
J Dent Sci
January 2025
Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
Background/purpose: This study analyzed the clinical and imaging features of lingual mandibular bone depression (LMBD) in the anterior mandible, aiming to prevent misdiagnosis and unnecessary surgical procedures.
Materials And Methods: The patients who visited a university dental hospital for painless radiolucency in the anterior mandible from January 2010 to December 2022 were retrospectively reviewed. Twelve cases of LMBD in the anterior mandible that are confirmed by biopsy or long-term follow-up were identified.
J Dent Sci
January 2025
Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan.
Background/purpose: Different types of scanners are gradually used to produce digital dental casts in the current dental practice. This study tested the accuracy of the three desktop scanners and two intraoral scanners and evaluated whether the desktop scanners had higher precision than the intraoral scanners.
Materials And Methods: This study used the three desktop and two intraoral scanners to scan a standard dental cast 5 times.
JBMR Plus
February 2025
Division of Biosciences, College of Dentistry, The Ohio State University, Columbus, OH, 43210, United States.
Hypophosphatasia (HPP) is an inherited error in metabolism resulting from loss-of-function variants in the gene, which encodes tissue-nonspecific alkaline phosphatase (TNAP). TNAP plays a crucial role in biomineralization of bones and teeth, in part by reducing levels of inorganic pyrophosphate (PP), an inhibitor of biomineralization. HPP onset in childhood contributes to rickets, including growth plate defects and impaired growth.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology, Jena University Hospital-Friedrich Schiller University, Am Klinikum 1, 07747, Jena, Germany.
Objectives: Forensic age estimation from orthopantomograms (OPGs) can be performed more quickly and accurately using convolutional neural networks (CNNs), making them an ideal extension to standard forensic age estimation methods. This study evaluates improvements in forensic age prediction for children, adolescents, and young adults by training a custom CNN from a previous study, using a larger, diverse dataset with a focus on dental growth features.
Methods: 21,814 OPGs from 13,766 individuals aged 1 to under 25 years were utilized.
Beijing Da Xue Xue Bao Yi Xue Ban
February 2025
Center for Digital Dentistry, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digi-tal Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China.
Objective: To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data, by utilizing a dynamic graph-based registration network model (maxillofacial dynamic graph registration network, MDGR-Net), and to provide a valuable reference for digital design and analysis in clinical dental applications.
Methods: Four hundred clinical patients without significant deformities were recruited from Peking University School of Stomatology from October 2018 to October 2022. Through data augmentation, a total of 2 000 three-dimensional maxillofacial datasets were generated for training and testing the MDGR-Net algorithm.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!