Objectives:To identify guidelines on the clinical use of CBCT in dental and maxillofacial radiology, in particular selection criteria, to consider how they were produced, to appraise their quality objectively and to compare their recommendations.Methods:A literature search using MEDLINE (Ovid®) was undertaken prospectively from 1 January 2000 to identify published material classifiable as “guidelines” pertaining to the use of CBCT in dentistry. This was supplemented by searches on websites, an internet search engine, hand searching of theses and by information from personal contacts. Quality assessment of publications was performed using the AGREE II instrument. Publications were examined for areas of agreement and disagreement.Results:26 publications were identified, 11 of which were specifically written to give guidelines on the clinical use of CBCT and contained sections on selection criteria. The remainder were a heterogeneous mixture of publications that included guidelines relating to CBCT. Two had ...

The concept of patient safety in dentistry is in its infancy, with little knowledge about the effectiveness of tools or interventions developed to improve patient safety or to minimise the occurrence of adverse events. The aim of this qualitative systematic review was to search the academic and grey literature to identify and assess tools or interventions used in dental care settings to maintain or improve patient safety. All study designs were included from all dental care settings. Outcome measures were: patient safety, harm prevention, risk minimization, patient satisfaction and patient acceptability, professional acceptability, efficacy, cost-effectiveness and efficiency. Quality assessments were performed on the included studies based on CASP tools. Further analysis was undertaken to discover whether any of the tools had been trialled or verified by the authors, or by subsequent authors. Following abstract screening, and initial qualitative synthesis, nine studies were found to meet the inclusion criteria with 31 being excluded following initial analysis. Tools identified included: checklists (4 studies), reporting systems (3), the use of electronic notes (1) and trigger tools (1). Grey literature searching did not identify any further appropriate studies. In terms of study design, there were observational studies including audit cycles (5 studies), epidemiological studies (3) and prospective cluster randomised clinical trials (1). The quality of the studies varied and none of their outcomes were verified by other researchers. The tools identified have the potential to be used for measuring and improving patient safety in dentistry, with two surgical safety checklists demonstrating a reduction in erroneous dental extractions to nil following their introduction. Reporting systems provide epidemiological data, however, it is not known whether they lead to any improvement in patient safety. The one study on trigger tools demonstrates a 50 % positive predictive value for safety incidents. It is not clear as to what impact the introduction of electronic guidelines has on patient safety outcomes. This systematic review finds that the only interventions in dentistry that reduce or minimise adverse events are surgical safety checklists. We believe this to be the first systematic review in this field; it demonstrates the need for further research into patient safety in dentistry across several domains: epidemiological, conceptual understanding and patient and practitioner involvement.

Background: Unstructured patient feedback (UPF) allows patients to freely express their experiences without the constraints of predefined questions. The proliferation of online health care rating websites has created a vast source of UPF. Natural language processing (NLP) techniques, particularly sentiment analysis and topic modeling, are increasingly being used to analyze UPF in health care settings; however, the scope and clinical relevance of these technologies are unclear. Objective: This scoping review investigates how NLP techniques are being used to interpret UPF, with a focus on the health care settings in which this is used, the purposes for using these technologies, and any impacts reported on clinical practice. Methods: Searches of the MEDLINE, Embase, CINAHL, Cochrane Database of Reviews, and Google Scholar were conducted in February 2024. No date limits were applied. Eligibility criteria included English-language studies that used NLP techniques on UPF that pertained to an identifiable health care setting or providers. Studies were excluded if human actors solely performed coding or if NLP was applied to structured feedback or non–patient-generated content. Data were extracted and narratively synthesized regarding health care settings, NLP methods, and clinical applications. Results: From 4017 records, 52 studies met inclusion criteria. NLP was most commonly applied to UPF from secondary care settings (n=33) with fewer in primary (n=10) or community (n=5) care. Three NLP techniques were identified in the included studies: sentiment analysis (n=32), topic modeling (n=15), and text classification (n=7). Sentiment analysis was applied to explore associations between patient sentiment and health care provider characteristics, track emotional responses over time, and identify areas for improvement in health care delivery. Topic modeling, primarily using latent Dirichlet allocation algorithm, was used to uncover latent themes in patient feedback, compare patient experiences across different health care settings, and track changes in patient concerns over time. Text classification was used to categorize patient feedback into predefined topics. The association between NLP-derived insights and traditional health care quality metrics was limited, with few studies describing concrete clinical impacts resulting from their analyses. Conclusions: NLP has been applied to UPF across a number of contexts, primarily to identify features of health services or professionals that support good patient experience. The growth of research publications demonstrates an academic interest in these technologies, but there is little evidence these approaches are being used in clinical settings. Future research is required to assess how NLP may capture the nuance of health care interactions, align with existing quality metrics, and how it may be used to influence clinician behavior.