Background: Subscription-based models are increasingly being explored as a potential solution to enhance patient access to preventive dental care, reduce reliance on traditional insurance and offer a more predictable financial … Background: Subscription-based models are increasingly being explored as a potential solution to enhance patient access to preventive dental care, reduce reliance on traditional insurance and offer a more predictable financial …

Background: The integration of Quick Response (QR) codes into healthcare offers a modern solution for efficient and cost-effective information dissemination. Given Saudi Arabia’s high smartphone penetration rate, QR codes hold significant potential for enhancing preventive oral healthcare. Objective: This study aims to assess the effectiveness of QR codes in providing preventive oral health information leaflets (PILs), focusing on patient awareness, ease of use, and preferences compared to traditional paper leaflets. Methods: A questionnaire-based survey was conducted among 118 participants, predominantly middle-aged and well-educated. The study assessed familiarity with QR codes, their actual usage, and patient preferences for accessing healthcare information via mobile technology or paper PILs. Chi-square tests were used to analyze the associations between demographics and preferences. Results: The study found universal awareness of QR codes among participants, with 44.1% reporting prior usage. A majority (91.5%) preferred accessing healthcare information via mobile devices, with 80.5% favoring QR codes over leaflets for their convenience and accessibility. No significant differences were observed in preferences across gender or educational levels, although women were more likely to require assistance. Participants highlighted the ease of sharing healthcare information through QR codes (87.3%). Conclusion: QR codes are widely recognized as effective tools for patient education, particularly in preventive oral healthcare. Their ease of use, accessibility, and strong preference over traditional methods underscore their potential in healthcare communication. Further research is needed to address barriers to broader adoption and assess long-term impacts.

Background: Dental insurance claims data are vital for research in oral health, epidemiology, and policy. However, issues like data quality, coding standards, validity, interoperability, and analytical approaches hinder their use. This review outlines these challenges. Methods: Following PRISMA-ScR and Arksey-O’Malley, we searched Web of Science, Scopus, and PubMed through April 2026 for peer-reviewed studies on dental insurance data issues. Two reviewers screened and extracted data, identifying key challenges and implications. Results: Out of 563 records, 389 remained after deduplication; 45 studies met criteria. Data sources included Medicaid, Medicare, insurers, and national systems from various countries. Six main challenges emerged: (1) coding errors and lack of standardization; (2) data validity and quality concerns; (3) interoperability and linkage barriers; (4) fraud detection issues; (5) analytical limitations; (6) policy insights on disparities. Validation showed variable accuracy, with diagnosis codes more reliable than procedure codes. Conclusions: Challenges limit data use in research and policy. Standardized coding, validation, interoperability, transparency, and causal inference are essential for leveraging these data to improve oral health research and policies.

Body mass index (BMI) is gaining recognition as a factor influencing surgical healing, but its precise effect on post-extraction complications is still not fully understood. This study investigated the association between BMI category and a comprehensive spectrum of post-extraction outcomes. This prospective observational study was conducted on 247 patients stratified into four BMI groups: underweight (n = 52), healthy weight (n = 68), overweight(n = 73), and obese (n = 54). Outcome variables included post-operative pain measured by the Visual Analog Scale (VAS) at 24 h, 72 h, one week, and two weeks; facial swelling at 72 h; a wound healing score ranging from 1 (poor) to 5 (excellent); Oral Health-Related Quality of Life (OHIP-14); dry socket; and post-extraction infection. Statistical analysis included Shapiro-Wilk normality testing, Kruskal-Wallis with Dunn post-hoc (Bonferroni correction), Chi-square, Spearman rank correlation, binary logistic regression, and multilinear regression, performed using SPSS v. 31.0. software. All continuous outcomes differed significantly across BMI groups (Kruskal-Wallis tests, all p < 0.001). The largest effect was observed for wound healing score (H = 144.564 ,η² = 0.583): the healthy weight group achieved predominantly excellent healing (72.1% of score 5) while overweight and obese patients showed predominantly poor-to-fair healing (87.7% and 87.1% scores 1-2, respectively). BMI was the strongest Spearman correlate of wound healing (ρ = -0.564, p < 0.001) and a significant independent predictor in logistic regression for dry socket( OR = 1.306; 95% CI :1.195-1.427, p < 0.001) and infection(OR = 1.534; 95% CI: 1.187-1.984 ; p = 0.01). Multiple linear regression confirmed BMI as the primary predictor of wound healing score (B =-0.118, p < 0.001; R² = 0.339) and OHIP-14 impairment (B =- 0.117 p < 0.01; R^{2 =} 0.280). Elevated BMI is a significant, independent predictor of adverse post-extraction. Healthcare providers should include BMI in pre-extraction risk assessments and consider BMI-specific post-operative management protocols to optimize patient outcomes.

Background: Clear post-extraction sessions are vital for good patient outcomes. Traditional methods, such as verbal and printed information, have historically been prevalent; however, digital health care methods for analysis and communication are increasingly being adopted. Still, evidence comparing preferences for digital versus traditional instruction, especially for routine post-extraction care, remains limited. Objectives: This study aimed to evaluate preferences between digital methods (QR code-based videos) and traditional methods (verbal and printed leaflets) for delivering post-extraction care instructions among dental patients and students. It also assesses perceptions of communication quality, confidence, and patient interaction associated with each method. Methods: A cross-sectional study involved 200 dental students and 200 patients undergoing routine tooth extractions. The sample size was determined based on a 5% margin of error at a 95% confidence level for proportion estimation. Participants experienced all three instructional delivery methods—verbal, printed leaflet, and QR code-based video—for post-extraction care and completed validated questionnaires. The study assessed preferences for each delivery method, communication quality, confidence in following or providing instructions, ease of access, perceived usefulness, and impact on patient-provider interaction. Data were analyzed using descriptive statistics, the Mann–Whitney U test for group comparisons, and Spearman correlation for relationships among ordinal data. Results: Among all participants, 67.5% chose QR codes as the preferred method for improving communication (p < 0.001). Among dental students, 49% favored QR codes as the best method for postoperative instructions, and 50.5% indicated that QR codes boosted their confidence in providing instructions to patients. Preference for QR codes ranged from 43.5% to 65.5% across different aspects evaluated. Notably, among patients aged 60 years or older, 65.5% considered QR codes the most convenient for the elderly. Mann–Whitney U tests showed statistically significant differences between students and patients for ease of access (p = 0.009, rank biserial r = −0.143), video length appropriateness (p = 0.003, rank biserial r = −0.160), and the unlikelihood of missing instructions (p < 0.001, rank biserial r = −0.446). Conclusions: QR code-based video instructions were widely preferred over traditional methods by both dental students and patients for post-extraction care delivery. These findings support the integration of digital communication tools into post-extraction care protocols as a complement to traditional instruction delivery methods, though longitudinal studies assessing actual clinical outcomes are needed.

Language barriers hinder healthcare, particularly during case history-taking, a key part of diagnosis. While multilingual artificial intelligence (AI) chatbots offer solutions, there is fragmented evidence of their effectiveness and impact. This systematic review followed PRISMA 2020 guidelines, examining studies published between 2015 and 2025 on multilingual AI chatbots in healthcare across four databases (Google Scholar, Scopus, Web of Science, and PubMed), using a two-stage screening process. Data extraction focused on applications, supported languages, underlying technologies, target populations, and clinical outcomes. From 503 records, 49 studies, covering primary care, telemedicine, oncology, mental health, and other areas, met the criteria. Supported languages included English, Spanish, Arabic, Chinese, Hindi, and other underrepresented languages. In individual system evaluations using heterogeneous methodologies and evaluation settings, AI chatbots achieved a diagnostic accuracy ranging from 72–92%. Core technologies included large language models (LLMs), bidirectional encoder representations from transformers (BERT), a generative pre-trained transformer (GPT), retrieval-augmented generation (RAG), speech recognition, and distillation. The findings show that these improve clinical workflow (30–70% time savings) and patient engagement, reduce language barriers, and promote health equity. However, the overall evidence certainty was low to moderate, reflecting the predominance of prototype and proof-of-concept studies. Multilingual AI chatbots demonstrate a boost in healthcare efficiency, a reduction in language barriers, and the promotion of health equity, but exhibit challenges regarding validation, workflow integration, and evaluation standards, along with ethical issues such as privacy and bias. Future research should include real-world studies, diverse populations, standardized outcome measures, and long-term equity assessments.