Search Results (1,357)

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

Background: Treatment compliant with the Global Initiative for Asthma (GINA) can promote more effective disease control. Single-inhaler triple therapy (SITT) is one method that is used to optimize therapy in this context, but TRIPLE therapy is still employed by physicians to a limited extent. Objective: This study aimed to describe the factors influencing challenges in optimizing asthma therapy. Methods: A 19-question survey, created via the CATI system, was distributed among pulmonologists, allergologists, general practitioners, and internal medicine specialists in Poland, Greece, Sweden, Slovenia, and Austria. Results: Statistically significant percentage differences in the use of TRIPLE therapy in the context of asthma management were observed among countries as well as between pulmonologists, allergists, and other specialists. Overuse of oral corticosteroids (OCSs) to treat nonsevere and severe asthma in the absence of an approach that focuses on optimizing inhalation therapy among asthma patients receiving TRIPLE therapy was observed in different countries as well as among physicians with different specialties. Twenty elements associated with the challenges involved in diagnosing and managing difficult-to-treat and severe asthma were identified. Six clinical categories for the optimization of asthma therapy via SITT were highlighted. The degree of therapeutic underestimation observed among severe asthma patients was assessed by comparing actual treatment with the recommendations of the GINA 2023 guidelines. Conclusions: Physicians of various specialties in Europe are subject to therapeutic inertia in terms of their compliance with the GINA 2023 guidelines. Full article

Background/Objectives: Benign paroxysmal positional vertigo (BPPV) is the most prevalent vestibular disorder encountered in clinical settings and is highly responsive to evidence-based diagnostic and therapeutic interventions. However, variations in practice patterns among physician specialties can compromise timely diagnosis and effective treatment. Understanding these variations is essential for improving clinical outcomes and standardizing care. This study aimed to assess the diagnostic and treatment practices for BPPV among Ear, Nose, and Throat (ENT) specialists, neurologists, general practitioners, and family physicians in Saudi Arabia and to examine how these practices are influenced by clinical experience and patient case exposure. Methods: A cross-sectional, questionnaire-based study was conducted between April 2023 and March 2024 at King Khalid University, Abha, Saudi Arabia. A total of 413 physicians were recruited using purposive sampling. Data were analyzed using IBM SPSS version 24.0. Parametric tests, including one-way ANOVA and chi-square tests, were used to assess differences across groups. A p-value of <0.05 was considered statistically significant. Results: Overall, all physician groups exhibited limited adherence to guideline-recommended positional diagnostic and therapeutic maneuvers. However, ENT specialists and neurologists demonstrated relatively higher compliance, particularly in performing the Dix–Hallpike test, with 46.97% and 26.79% reporting “always” using the maneuver, respectively (p < 0.001, Cramér’s V = 0.22). Neurologists were the most consistent in conducting oculomotor examinations, with 73.68% reporting routine performance (p < 0.001, Cramér’s V = 0.35). Epley maneuver usage was highest among neurologists (86.36%) and ENT specialists (77.14%) compared to family physicians (50.60%) and GPs (67.50%) (p = 0.044). Physicians with 11–15 years of experience and >50 BPPV case exposures consistently showed a greater use of diagnostic maneuvers, repositioning techniques, and guideline-concordant medication use (betahistine 76.67%; p < 0.001). Continuing medical education (CME) participation and the avoidance of unnecessary imaging were also highest in this group (46.67% and 3.33%, respectively; p < 0.001). Conclusions: Significant inter-specialty differences exist in the management of BPPV in Saudi Arabia. Greater clinical experience and higher case exposure are associated with improved adherence to evidence-based practices. Targeted educational interventions are needed, particularly in primary care, to enhance guideline implementation. Full article

Objectives: Automatic tip localization is critical in ultrasound (US)-guided interventions. Although deep learning (DL) has been widely used for precise tip detection, existing methods are limited by the availability of real puncture data and expert annotations. Methods: To address these challenges, we propose a novel method that uses synthetic US puncture data to pre-train DL-based tip detectors, improving their generalization. Synthetic data are generated by fusing clinical US images of healthy controls with tips created using generative DL models. To ensure clinical diversity, we constructed a dataset from scans of 20 volunteers, covering 20 organs or anatomical regions, obtained with six different US machines and performed by three physicians with varying expertise levels. Tip diversity is introduced by generating a wide range of synthetic tips using a denoising probabilistic diffusion model (DDPM). This method synthesizes a large volume of diverse US puncture data, which are used to pre-train tip detectors, followed by subsequently training with real puncture data. Results: Our method outperforms MSCOCO pre-training on a clinical puncture dataset, achieving a 1.27–7.19% improvement in AP_0.1:0.5 with varying numbers of real samples. State-of-the-art detectors also show performance gains of 1.14–1.76% when applying the proposed method. Conclusions: The experimental results demonstrate that our method enhances the generalization of tip detectors without relying on expert annotations or large amounts of real data, offering significant potential for more accurate visual guidance during US-guided interventions and broader clinical applications. Full article

Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims to evaluate and compare five major LLMs (Grok, ChatGPT, Gemini, Claude.ai, and Meta AI) regarding dry eye syndrome information delivery across different demographic groups. Methods: LLMs were queried using standardized prompts simulating a 62-year-old patient with dry eye symptoms across four demographic categories (White, Black, East Asian, and Hispanic males and females). Responses were analyzed for word count, readability, cultural sensitivity scores (0–3 scale), keyword coverage, and response times. Results: Significant variations existed across LLMs. Word counts ranged from 32 to 346 words, with Gemini being the most comprehensive (653.8 ± 96.2 words) and Claude.ai being the most concise (207.6 ± 10.8 words). Cultural sensitivity scores revealed Grok demonstrated highest awareness for minority populations (scoring 3 for Black and Hispanic demographics), while Meta AI showed minimal cultural tailoring (0.5 ± 0.5). All models recommended specialist consultation, but medical term coverage varied significantly. Response times ranged from 7.41 s (Meta AI) to 25.32 s (Gemini). Conclusions: While all LLMs provided appropriate referral recommendations, substantial disparities exist in cultural sensitivity, content depth, and information delivery across demographic groups. No LLM consistently addressed the full spectrum of dry eye causes across all demographics. These findings underscore the importance for physician oversight and standardization in AI-generated healthcare information to ensure equitable access and prevent care delays. Full article

Background/Objectives: Effective teamwork is crucial for minimizing human error in healthcare settings. Medical teams, typically composed of physicians and nurses, supported by auxiliary professionals, achieve better outcomes when they possess strong collaborative competencies. High-quality teamwork is associated with fewer adverse events and complications and lower mortality rates. Based on this background, the objective of this study is to analyze the perception of non-technical skills and immediate learning outcomes in interprofessional simulation settings based on E-CRM items. Methods: A cross-sectional observational study was conducted involving participants from the official postgraduate Medicine and Nursing programs at the Catholic University of Murcia (UCAM) during the 2024–2025 academic year. Four interprofessional E-CRM simulation sessions were planned, involving randomly assigned groups with proportional representation of medical and nursing students. Teams worked consistently throughout the training and participated in clinical scenarios observed via video transmission by their peers. Post-scenario debriefings followed INACSL guidelines and employed the PEARLS method. Results: Findings indicate that 48.3% of participants had no difficulty identifying the team leader, while 51.7% reported minor difficulty. Role assignment posed moderate-to-high difficulty for 24.1% of respondents. Communication, situation awareness, and early help-seeking were generally managed with ease, though mobilizing resources remained a challenge for 27.5% of participants. Conclusions: This study supports the value of interprofessional education in developing essential competencies for handling urgent, emergency, and high-complexity clinical situations. Strengthening interdisciplinary collaboration contributes to safer, more effective patient care. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

Background/Objectives: Laboratory tests conducted in community settings are occasionally repeated within hours of presentation to pediatric emergency departments (PEDs). Reducing unnecessary repetitions can ease child discomfort and alleviate the healthcare burden without compromising the diagnostic process or quality of care. The aim of this study was to develop a decision tree (DT) model to guide physicians in minimizing unnecessary repeat blood tests in PEDs. The minimal decision tree (MDT) algorithm was selected for its interpretability and capacity to generate optimally pruned classification trees. Methods: Children aged 3 months to 18 years with community-based complete blood count (CBC), electrolyte (ELE), and C-reactive protein (CRP) measurements obtained between 2016 and 2023 were included. Repeat tests performed in the pediatric emergency department within 12 h were evaluated by comparing paired measurements, with tests considered justified when values transitioned from normal to abnormal ranges or changed by ≥20%. Additionally, sensitivity analyses were conducted for absolute change thresholds of 10% and 30% and for repeat intervals of 6, 18, and 24 h. Results: Among 7813 children visits in this study, 6044, 1941, and 2771 underwent repeated CBC, ELE, and CRP tests, respectively. The mean ages of patients undergoing CRP, ELE, and CBC testing were 6.33 ± 5.38, 7.91 ± 5.71, and 5.08 ± 5.28 years, respectively. The majority were of middle socio-economic class, with 66.61–71.24% living in urban areas. Pain was the predominant presented complaint (83.69–85.99%), and in most cases (83.69–85.99%), the examination was conducted by a pediatrician. The DT model was developed and evaluated on training and validation cohorts, and it demonstrated high accuracy in predicting the need for repeat CBC and ELE tests but not CRP. Performance of the DT model significantly exceeded that of the logistic regression model. Conclusions: The data-driven guide derived from the DT model provides clinicians with a practical, interpretable tool to minimize unnecessary repeat laboratory testing, thereby enhancing patient care and optimizing healthcare resource utilization. Full article

Background: Kidney transplantation from a living donor is considered the most beneficial form of treatment for end-stage renal failure, which, in addition to providing patients with better treatment results, significantly improves their quality of life. Understanding factors that influence the willingness to donate kidneys to strangers is critical in promoting and expanding the living donor pool. When considering the decision to become an altruistic kidney donor, individuals must evaluate multiple factors, including the identity of the recipient and their own perceived level of safety. This study aimed to assess the willingness of dialysis center employees to act as living kidney donors for a stranger. Methods: We conducted a cross-sectional study from February 2023 to June 2024 among dialysis specialists across Poland. The study involved 1093 people (doctors and nurses). The study used our survey questionnaire and standardized tools. Results: Nurses (vs. physicians) and those who advocated the regulation of unspecified living kidney donation in Poland, did not believe in the risk of organ trafficking, and would donate a kidney to a husband/wife or friend and accept kidney transplantation from a husband/wife were more likely to donate a kidney to a stranger. Furthermore, respondents who accepted a loved one’s decision to donate a kidney to a stranger were significantly more willing to donate a kidney to such a person themselves. Perceived self-efficacy was positively associated with the willingness to donate a kidney to a stranger. Conclusions: Less than half of healthcare professionals supported unspecific living organ donation in Poland, and nurses were more willing to donate than physicians. The factors supporting the decision generally included knowledge about organ donation and transplantation, a lack of fear of organ trafficking, and attitudes towards donation. Full article

Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care. Full article

(1) Background: Human Papillomavirus (HPV)-associated oropharyngeal cancer is the fastest-growing head and neck malignancy, yet vaccination coverage remains suboptimal. (2) Methods: In this cross-sectional survey conducted from April 2022 to April 2023, 400 parents of patients aged 8–18 years (mean ± SD = 12.8 ± 2.6; 59.3% female) reported their child’s HPV vaccination status and willingness to initiate or complete the vaccine series at a dental clinic. For those who were not fully vaccinated, reasons for refusal were documented. (3) Results: Over half (54.5%, n = 218) of the children were not fully vaccinated. Notably, 21% (46/218) of parents indicated an immediate willingness to vaccinate their child if the dentist offered it—a significant potential for improvement compared to general healthcare settings. Reported barriers included preference for a physician’s office (43.6%), indecision (20.3%), unspecified concerns (14.5%), safety worries (8.1%), and religious objections (5.2%). Male and younger patients (9–11 years) showed significantly lower vaccination coverage (p < 0.05). (4) Conclusions: Dentists can substantially impact public health by integrating immunization counseling, interprofessional collaboration, and vaccine administration, thereby addressing critical gaps in HPV-related cancer prevention. These findings highlight the opportunity for dental offices to enhance vaccination rates and prompt further research, education, and policy initiatives to advance the oral and general health of our patients. Full article

Background/Objectives: Otolaryngologists are increasingly using head and neck ultrasound (HNUS). Determining whether a practitioner of HNUS has achieved adequate theoretical knowledge remains a challenge. This study assesses the performance of two large language models (LLMs) in generating multiple-choice questions (MCQs) for head and neck ultrasound, compared with MCQs generated by physicians. Methods: Physicians and LLMs (ChatGPT, GPT4o, and Google Gemini, Gemini Advanced) created a total of 90 MCQs that covered the topics of lymph nodes, thyroid, and salivary glands. Experts in HNUS additionally evaluated all physician-drafted MCQs using a Delphi-like process. The MCQs were assessed by an international panel of experts in HNUS, who were blinded to the source of the questions. Using a Likert scale, the evaluation was based on an overall assessment including six assessment criteria: clarity, relevance, suitability, quality of distractors, adequate rationale of the answer, and an assessment of the level of difficulty. Results: Four experts in the clinical field of HNUS assessed the 90 MCQs. No significant differences were observed between the two LLMs. Physician-drafted questions (n = 30) had significant differences with Google Gemini in terms of relevance, suitability, and adequate rationale of the answer, but only significant differences in terms of suitability compared with ChatGPT. Compared to MCQ items (n = 16) validated by medical experts, LLM-constructed MCQ items scored significantly lower across all criteria. The difficulty level of the MCQs was the same. Conclusions: Our study demonstrates that both LLMs could be used to generate MCQ items with a quality comparable to drafts from physicians. However, the quality of LLM-generated MCQ items was still significantly lower than MCQs validated by ultrasound experts. LLMs are therefore cost-effective to generate a quick draft for MCQ items that afterward should be validated by experts before being used for assessment purposes. In this way, the value of LLM is not the elimination of humans, but rather vastly superior time management. Full article

Background and Objectives: During pandemics, emergency departments face the challenge of managing critically ill patients with limited resources. Point-of-Care Ultrasound (POCUS) has emerged as a crucial diagnostic tool in such scenarios. This study aimed to develop a standardized POCUS protocol using expert consensus via a modified Delphi survey to guide physicians in managing these patients more effectively. Materials and Methods: A committee of emergency imaging experts and board-certified emergency physicians identified essential elements of POCUS in the treatment of patients under investigation (PUI) with shock, sepsis, or other life-threatening diseases. A modified Delphi survey was conducted among 39 emergency imaging experts who were members of the Korean Society of Emergency Medicine. The survey included three rounds of expert feedback and revisions, leading to the development of a POCUS protocol for critically ill patients during a pandemic. Results: The developed POCUS protocol emphasizes the use of POCUS-echocardiography and POCUS-lung ultrasound for the evaluation of cardiac and respiratory function, respectively. The protocol also provides guidance on when to consider additional tests or imaging based on POCUS findings. The Delphi survey results indicated general consensus on the inclusion of POCUS-echocardiography and POCUS-lung ultrasound within the protocol, although there were some disagreements regarding specific elements. Conclusions: Effective clinical practice aids emergency physicians in determining appropriate POCUS strategies for differential diagnosis between life-threatening diseases. Future studies should investigate the effectiveness and feasibility of the protocol in actual clinical scenarios, including its impact on patient outcomes, resource utilization, and workflow efficiency in emergency departments. Full article

Background: Riboflavin transporter deficiency type 2 is an ultra-rare, yet treatable, inborn error of metabolism. This autosomal recessive disorder is caused by pathogenic mutations in the SLC52A2 gene leading to progressive ataxia, polyneuropathy, and hearing and visual impairment. The early initiation of riboflavin therapy can prevent or mitigate the complications. To date, only 200 cases have been reported, mostly in consanguineous populations. The p.Gly306Arg founder mutation, identified in patients of Lebanese descent, is the most frequently reported worldwide. It was described in a homozygous state in a total of 21 patients. Therefore, studies characterizing the phenotypic spectrum of this mutation remain scarce. Methods: A retrospective review of charts of patients diagnosed with riboflavin transporter deficiency type 2 at a tertiary-care reference center in Lebanon was performed. Clinical, biochemical, and molecular profiles were analyzed and compared to reported cases in the literature. Results: A total of six patients from three unrelated families were diagnosed between 2018 and 2023. All patients exhibited the homozygous founder mutation, p.Gly306Arg, with variable phenotypes, even among family members. The median age of onset was 3 years. Diagnosis was achieved by exome sequencing at a median age of 5 years, as clinical and biochemical profiles were inconsistently suggestive. The response to riboflavin was variable. One patient treated with high-dose riboflavin recovered his motor function, while the others were stabilized. Conclusions: This study expands the current knowledge of the phenotypic spectrum associated with the p.Gly306Arg mutation in the SLC52A2 gene. Increased awareness among physicians of the common manifestations of this rare disorder is crucial for early diagnosis and treatment. In the absence of a consistent clinical or biochemical phenotype, the use of next-generation sequencing as a first-tier diagnostic test may be considered. Full article

Background/Objectives: Acetazolamide is widely used for acute mountain sickness (AMS) prophylaxis. Whilst generally safe, acute kidney injury (AKI) is a rare but serious adverse event. We present a case of anuric AKI following minimal exposure to acetazolamide, contributing to the limited literature on its nephrotoxicity at prophylactic doses. Methods: A 54-year-old previously healthy male ingested 250 mg/day of oral acetazolamide for two days. He developed acute anuria and lumbar pain. Diagnostic evaluation included laboratory tests, imaging, microbiological cultures, autoimmune panels, and diuretic response. No signs of infection, urinary tract obstruction, or systemic disease were found. Results: The patient met KDIGO 2012 criteria for stage 3 AKI, with peak serum creatinine of 10.6 mg/dL and metabolic acidosis. Imaging confirmed non-obstructive nephrolithiasis. Conservative treatment failed; intermittent hemodialysis was initiated. Renal function recovered rapidly, with the normalization of serum creatinine and urinary output by day 4. Conclusions: This case represents the lowest cumulative dose of acetazolamide reported to cause stage 3 AKI. The findings support a pathophysiological mechanism involving sulfonamide-induced crystalluria and intratubular obstruction. Physicians should consider acetazolamide in the differential diagnosis of AKI, even with short-term prophylactic use. Full article