IntroductionCase‐based learning is widely used in health professions education to improve clinical learning, but little is known about how best to approach multiple cases in this active learning strategy. Our study explored dental student views of multiple case‐based learning in oral pathology.Materials and methodsQualitative description informed the study design. Data were collected through semi‐structured, individual interviews with twenty‐one third‐ and fourth‐year dental students who participated in multiple case‐based learning seminars. Data were analysed using inductive, manifest thematic analysis.ResultsThemes were identified at approach and case levels. Approach‐level themes included preparing students for clinical practice and board exams and maximising exposure (e.g., to lesions/conditions), knowledge application, and engagement within the time allotted for the learning session. Case‐level themes included using challenging but manageable cases, linking cases to lecture content, providing the necessary clinical information to solve the cases, and ensuring that cases were authentic and common with non‐typical presentations. Aspects of themes encompassed definitions of case characteristics, benefits, conditions of implementation, and recommendations for improvement.ConclusionCases should be considered individually, collectively, purposefully, and contextually in multiple case‐based learning. Evaluations of learning and behavioural outcome are needed to further establish the effectiveness of approaches and case characteristics in multiple case‐based learning.

Objective: Cases used in case-based learning should be realistic, relatively difficult, engaging, and educational to maximize clinical knowledge and skills. Data are needed to support the effectiveness of existing and new techniques to ensure these case attributes. The purpose of this study was to explore dental students' perceptions of the wildcard technique in case-based learning. This novel technique aims to ensure key case attributes by adding new information to the analysis of a case that challenges the initial diagnosis and/or treatment plan.Methods: Constructivism (paradigm) and interpretative description (approach) informed the study design. Participants were 21 third-and fourth-year dental students who took part in an oral pathology seminar in which the wildcard was employed. Data were collected through individual, semi-structured interviews that were digitally recorded and transcribed verbatim. Inductive, manifest thematic analysis was used to analyze the data. Several verification strategies were implemented to ensure rigor throughout data analysis.Results: Identified themes suggest that students perceived the wildcard as a new scenario that simulated clinical practice regarding settings, situations, conditions, and required skills. They also enjoyed the wildcard and found it effective in terms of knowledge acquisition, skills development, and engagement. Students valued and recommended wildcards that were challenging, authentic, and educational. Conclusions: Students positively valued the wildcard, which seems to ensure several case attributes. Learning and behavioral outcome evaluations are needed to further establish the effectiveness of the wildcard in case-based learning.

ObjectiveVarious health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.MethodsA secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).ResultsData from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).ConclusionOur study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.