The rapid increase in the world's aging population to 16% by the year 2050 spurs the need for the application of digital health solutions to enhance older individuals' independence, accessibility, and well-being. While digital health technologies such as telemedicine, wearables, and mobile health applications can transform geriatric care, their adoption among older individuals is not evenly distributed. This study redefines the "digital divide" among older health care as a usability divide, contends that user experience (UX) poor design is the primary adoption barrier, rather than access. Drawing on interdisciplinary studies and design paradigms, the research identifies the main challenges: visual, cognitive, and motor impairment; complicated interfaces; and lack of co-creation with older adults, and outlines how participatory, user-focused, and inclusive notions of design can transcend them. Findings reveal that older persons easily embrace those technologies that are intuitive, accessible, and socially embedded as they promote autonomy, confidence, and equity in health. The study identifies the effects of the design attributes of high-contrast screens, lower interaction flow, multimodal feedback, and caregiver integration as having strong influences on usability outcomes. In addition, it critiques the current accessibility guidelines as being technically oriented rather than experiential and demands an ethical, empathetic understanding of design grounded in human-centered usability rather than technical accessibility in itself.
We present a Sovereign AI architecture for clinical triage in which all inference is performed on-device and inbound data is delivered via a physically unidirectional channel, implemented using receive-only broadcast infrastructure or certified hardware data diodes, with no return path to any external network. This design removes the network-mediated attack surface by construction, rather than attempting to secure it through software controls. The system performs conversational symptom intake, integrates device-captured vitals, and produces structured, triage-aligned clinical records at the point of care. We formalize the security properties of receiver-side unidirectionality and show that the architecture is transport-agnostic across broadcast and diode-enforced deployments. We further analyze threat models, enforcement mechanisms, and deployment configurations, demonstrating how physical one-way data flow enables high-assurance operation in both resource-constrained and high-risk environments. This work positions physically unidirectional channels as a foundational primitive for sovereign, on-device clinical intelligence at the front door of care.
Airway foreign body (FB) removal is challenging and a time-limited and lifesaving procedure. Herein, we report successful removal of a life-threatening FB in the subglottic airway in an infant by physically forcing the FB further into the distal airway to block one lung and save the other. A 12-month-old boy presented in the emergency department with choking. Upon arrival, his mental status was alert. However, respiratory failure rapidly progressed and respiratory arrest occurred. We attempted to move the FB distally by pushing the endotracheal tube as deep as possible and advancing the stylet. The patient was successfully resuscitated, and bronchoscopic FB removal was performed. The patient was discharged without respiratory or neurologic sequelae.
Medical emergencies. Critical care. Intensive care. First aid
Background: Nationally representative data on recent trends in racial/ethnic differences in metabolic syndrome (MetS) prevalence and treatment are sparse. Objectives: The purpose of this study was to examine 20-year trends in the prevalence, treatment, and interrelationships of MetS and its individual components among U.S. adults, overall and by race/ethnicity. Methods: We evaluated trends from 1999 to 2018 in 20,397 adults using data from the National Health and Nutrition Examination Survey. Age-standardized prevalence estimates were calculated for MetS, its components, and related prescription drug use. Trends were assessed using weighted linear regression, and racial/ethnic disparities were examined using t-tests. Results: The mean age was 47.5 (47.4-47.6) years; 51.3% were female; 77.9%, 12.8%, and 9.4% were White, Black, and Hispanic, respectively. MetS prevalence increased significantly from 1999 to 2018 across all groups (P < 0.001). Among MetS components, waist circumference and fasting glucose increased across all groups, while triglycerides increased only among Black individuals. Lipid-lowering medication use increased (P < 0.001), but racial/ethnic disparities persisted. Compared to White individuals, Hispanic individuals had lower antihypertensive and lipid-lowering medication use (P < 0.01). Despite increased prescriptions, <65% of eligible individuals received lipid-lowering therapy, and <35% received antihyperglycemic therapy, highlighting substantial treatment gaps. Racial/ethnic differences in MetS component relationships were observed: blood pressure played a larger role in Black individuals, while fasting glucose was more prominent in Hispanic individuals. Conclusions: MetS prevalence has increased over 2 decades. Persistent racial/ethnic disparities exist in antihypertensive, antihyperglycemic, and lipid-lowering medication use. Across all racial/ethnic subgroups, large opportunities remain for improving treatment strategies among individuals with medication indications.
Diseases of the circulatory (Cardiovascular) system, Medical emergencies. Critical care. Intensive care. First aid
Ayan Banerjee, PhD, Riya Sudhakar Salian, PhD, Hema Srikanth Vemulapalli, MBBS
et al.
Background: Exercise stress electrocardiogram (ECG) (ESE) is a widely used, noninvasive diagnostic tool for detecting coronary artery disease (CAD). Despite its widespread use, the diagnostic accuracy of ESE remains suboptimal. Objectives: This study aimed to develop and evaluate an artificial intelligence (AI) model, using a transformer-based architecture, to enhance the diagnostic performance ofESEs. Methods: Patients who underwent coronary angiography within 2 months of the ESE were eligible for inclusion. An AI model processed exercise stress ECG images into time-series data. A transformer-based architecture was employed to integrate temporal ECG features and predict CAD. Model performance in predicting severe CAD was first evaluated using 5-fold cross-validation on a test subset from the original cohort, and subsequently on a second validation cohort. Results: We developed a model using a total of 1,200 ECGs. An additional validation cohort of 91 patients was also analyzed. On the initial test subset, the AI model demonstrated a sensitivity of 93.6%, specificity of 93.2%, and overall accuracy of 93.4%. Notably, the model improved sensitivity with an absolute increase of 40.9% in women and 44.6% in men. In the second validation cohort, the model achieved an accuracy of 78%, with a sensitivity of 64.6% and a specificity of 93%. Conclusions: This study presents a proof of concept demonstrating that an AI-based model for stress ECG interpretation is feasible and shows acceptable performance.
Diseases of the circulatory (Cardiovascular) system, Medical emergencies. Critical care. Intensive care. First aid
Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabelled data for training. A promising solution consists of ensuring consistent predictions across different views of the data, where the efficacy of this strategy depends on the accuracy of the pseudo-labels generated by the model for this consistency learning strategy. In this paper, we introduce a new methodology to produce high-quality pseudo-labels for a consistency learning strategy to address semi-supervised 3D medical image segmentation. The methodology has three important contributions. The first contribution is the Cooperative Rectification Learning Network (CRLN) that learns multiple prototypes per class to be used as external knowledge priors to adaptively rectify pseudo-labels at the voxel level. The second contribution consists of the Dynamic Interaction Module (DIM) to facilitate pairwise and cross-class interactions between prototypes and multi-resolution image features, enabling the production of accurate voxel-level clues for pseudo-label rectification. The third contribution is the Cooperative Positive Supervision (CPS), which optimises uncertain representations to align with unassertive representations of their class distributions, improving the model's accuracy in classifying uncertain regions. Extensive experiments on three public 3D medical segmentation datasets demonstrate the effectiveness and superiority of our semi-supervised learning method.
Image denoising algorithms have been extensively investigated for medical imaging. To perform image denoising, penalized least-squares (PLS) problems can be designed and solved, in which the penalty term encodes prior knowledge of the object being imaged. Sparsity-promoting penalties, such as total variation (TV), have been a popular choice for regularizing image denoising problems. However, such hand-crafted penalties may not be able to preserve task-relevant information in measured image data and can lead to oversmoothed image appearances and patchy artifacts that degrade signal detectability. Supervised learning methods that employ convolutional neural networks (CNNs) have emerged as a popular approach to denoising medical images. However, studies have shown that CNNs trained with loss functions based on traditional image quality measures can lead to a loss of task-relevant information in images. Some previous works have investigated task-based loss functions that employ model observers for training the CNN denoising models. However, such training processes typically require a large number of noisy and ground-truth (noise-free or low-noise) image data pairs. In this work, we propose a task-based regularization strategy for use with PLS in medical image denoising. The proposed task-based regularization is associated with the likelihood of linear test statistics of noisy images for Gaussian noise models. The proposed method does not require ground-truth image data and solves an individual optimization problem for denoising each image. Computer-simulation studies are conducted that consider a multivariate-normally distributed (MVN) lumpy background and a binary texture background. It is demonstrated that the proposed regularization strategy can effectively improve signal detectability in denoised images.
The growing aging population has significantly increased demand for efficient home health care (HHC) services. This study introduces a Vehicle Routing and Appointment Scheduling Problem (VRASP) to simultaneously optimize caregiver routes and appointment times, minimizing costs while improving service quality. We first develop a deterministic VRASP model and then extend it to a stochastic version using sample average approximation to account for travel and service time uncertainty. A tailored Variable Neighborhood Search (VNS) heuristic is proposed, combining regret-based insertion and Tabu Search to efficiently solve both problem variants. Computational experiments show that the stochastic model outperforms the deterministic approach, while VNS achieves near-optimal solutions for small instances and demonstrates superior scalability for larger problems compared to CPLEX. This work provides HHC providers with a practical decision-making tool to enhance operational efficiency under uncertainty.
Current mental-health conversational systems are usually based on fixed, generic dialogue patterns. This paper proposes an adaptive framework based on large language models that aims to personalize therapeutic interaction according to a user's psychological state, quantified with the Acceptance of Illness Scale (AIS). The framework defines three specialized agents, L, M, and H, each linked to a different level of illness acceptance, and adjusts conversational behavior over time using continuous feedback signals. The AIS-stratified architecture is treated as a diegetic prototype placed in a plausible near-future setting and examined through the method of design fiction. By embedding the architecture in narrative scenarios, the study explores how such agents might influence access to care and therapeutic relationship. The goal is to show how clinically informed personalization, technical feasibility, and speculative scenario analysis can together inform the responsible design of LLM-based companions for mental-health support.
AI-assisted gait analysis holds promise for improving Parkinson's Disease (PD) care, but current clinical dashboards lack transparency and offer no meaningful way for clinicians to interrogate or contest AI decisions. We present Con-GaIT (Contestable Gait Interpretation & Tracking), a clinician-centered system that advances Contestable AI through a tightly integrated interface designed for interpretability, oversight, and procedural recourse. Grounded in HCI principles, ConGaIT enables structured disagreement via a novel Contest & Justify interaction pattern, supported by visual explanations, role-based feedback, and traceable justification logs. Evaluated using the Contestability Assessment Score (CAS), the framework achieves a score of 0.970, demonstrating that contestability can be operationalized through human-centered design in compliance with emerging regulatory standards. A demonstration of the framework is available at https://github.com/hungdothanh/Con-GaIT.
Introduction: Atraumatic corneal melting and perforation is a rare etiology of eye pain and visual loss in the Emergency Department (ED), and xerophthalmia from vitamin A deficiency is primarily described as a cause of blindness in pediatric patients. Case: A 68-year-old female presented to the ED with worsening eye pain and months of clouding and vision loss. History was limited by cognitive impairment and was provided by spouse. On exam, she was found to have a body mass index of 13.7 kg/m 2, dry mucous membranes, purulent discharge from both eyes, and opacification and erosion of both corneas. She was diagnosed with bilateral corneal perforations due to xerophthalmia with superimposed bacterial keratitis. Her vitamin A levels were found to be undetectable. Conclusion:: This presentation was highly concerning for elder neglect due to delay in presentation, poor outpatient follow-up, and presence of severe malnutrition. This case exemplifies the intersection of an acute medical presentation with a syndrome of neglect and demonstrates the importance of ED clinician evaluation for elder abuse and neglect.
Geriatrics, Medical emergencies. Critical care. Intensive care. First aid
Background: Muscle relaxants are used for two general purposes. One is to ease endotracheal intubation, and the other is provide surgical relaxation.
This study has been designed with the aim of assessing the impact of atracurium and cisatracurium on patients at the anesthesia induction and the neutrophil to lymphocyte ratio.
Methods: This is a randomized clinical trial that was performed in 2022-2023 in Kashani hospital in Isfahan, Iran on patients that were candidates for elective surgery under general anesthesia by atracurium or cisatracurium. A total number of 80 patients entered and were randomized into two group’s one receiving group atracurium 0.5mg/kg, and other group received cisatracurium 0.15mg/kg over 60 seconds as NMB. Blood sample were taken base time, after 3, and 20 minutes following intubation. Qualitative data is reported as frequency with percentage. And quantitative data as average with standard deviation. Statistical analysis was done using SPSS version 25. Qualitative data were analyzed using chi-square tests and quantitative data using independent T test. Significance level was defined as p value <0.05.
Results: Overall, 80 patients were enrolled in this study. 40 of them belonged to the cisatracurium group and 40 to the atracurium group. Average age of the participants was 42.86 (±14.52) years old. Mean arterial pressure (MAP) in cisatracurium group dropped significantly following intubation (p<0.005), while it rose significantly in the atracurium group (p<0.05). However neutrophil to lymphocyte ratio (NLR) was significantly higher in the cisatracurium group following intubation (P<0.05).
Conclusion: While the use of atracurium in patients is still safe, is yet more correlated with pronounced hemodynamic instability compared to cisatracurium.
Anesthesiology, Medical emergencies. Critical care. Intensive care. First aid
Objective: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques. Materials and Methods: We first created a lexicon and regular expression lists from literature-driven stem words for linguistic features of stigmatizing patient labels, doubt markers, and scare quotes within EHRs. The lexicon was further extended using Word2Vec and GPT 3.5, and refined through human evaluation. These lexicons were used to search for matches across 18 million sentences from the de-identified Medical Information Mart for Intensive Care-III (MIMIC-III) dataset. For each linguistic bias feature, 1000 sentence matches were sampled, labeled by expert clinical and public health annotators, and used to supervised learning classifiers. Results: Lexicon development from expanded literature stem-word lists resulted in a doubt marker lexicon containing 58 expressions, and a stigmatizing labels lexicon containing 127 expressions. Classifiers for doubt markers and stigmatizing labels had the highest performance, with macro F1-scores of .84 and .79, positive-label recall and precision values ranging from .71 to .86, and accuracies aligning closely with human annotator agreement (.87). Discussion: This study demonstrated the feasibility of supervised classifiers in automatically identifying stigmatizing labels and doubt markers in medical text, and identified trends in stigmatizing language use in an EHR setting. Additional labeled data may help improve lower scare quote model performance. Conclusions: Classifiers developed in this study showed high model performance and can be applied to identify patterns and target interventions to reduce stigmatizing labels and doubt markers in healthcare systems.
In healthcare intelligence, the ability to fuse heterogeneous, multi-intent information from diverse clinical sources is fundamental to building reliable decision-making systems. Large Language Model (LLM)-driven information interaction systems currently showing potential promise in the healthcare domain. Nevertheless, they often suffer from information redundancy and coupling when dealing with complex medical intents, leading to severe hallucinations and performance bottlenecks. To this end, we propose MedAide, an LLM-based medical multi-agent collaboration framework designed to enable intent-aware information fusion and coordinated reasoning across specialized healthcare domains. Specifically, we introduce a regularization-guided module that combines syntactic constraints with retrieval augmented generation to decompose complex queries into structured representations, facilitating fine-grained clinical information fusion and intent resolution. Additionally, a dynamic intent prototype matching module is proposed to utilize dynamic prototype representation with a semantic similarity matching mechanism to achieve adaptive recognition and updating of the agent's intent in multi-round healthcare dialogues. Ultimately, we design a rotation agent collaboration mechanism that introduces dynamic role rotation and decision-level information fusion across specialized medical agents. Extensive experiments are conducted on four medical benchmarks with composite intents. Experimental results from automated metrics and expert doctor evaluations show that MedAide outperforms current LLMs and improves their medical proficiency and strategic reasoning.
Marie-Frédéric Tremblay,1 Frédéric Leblanc,1 Étienne Laroche,1 Virginie Blanchette,2,3 Magali Brousseau-Foley1,2 1Centre intégré universitaire de santé et de services sociaux de la Mauricie et du Centre-du-Québec affiliated to Université de Montréal Faculty of Medicine, Trois-Rivières, Québec, Canada; 2Department of Human Kinetics and Podiatric Medicine, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada; 3VITAM - Centre de recherche en santé durable, Centre intégré universitaire de santé et services sociaux de la Capitale-Nationale, Quebec, QC, CanadaCorrespondence: Magali Brousseau-Foley, Groupe de médecine familiale universitaire de Trois-Rivières, 731 Rue Sainte-Julie 2e étage, Trois-Rivières, QC, G9A 1X9, Canada, Email magali.brousseau-foley@uqtr.caObjective: Compassion and physician well-being are two key components related to quality care in health including emergency medicine. The objective of this study was to explore the impact of compassion in care on the well-being of emergency physicians. We conducted a scoping review to explore the impact of provision of compassionate care by emergency physicians on their well-being and subconcepts.Methods: Four electronic databases and grey literature were searched to find evidence related to compassion, empathy, self-compassion, and their impact on emergency physicians’ well-being. Following title and abstract review, two reviewers independently screened full-text articles, and extracted data. Data were presented using descriptive statistics and a narrative analysis.Results: A total of 803 reports were identified in databases. Three articles met eligibility criteria for data extraction. None directly examined compassion and well-being. Included studies addressed empathy and burnout in emergency medicine professionals.Conclusion: No high-quality evidence could be found on the topic in the population of interest. Literature related to the topic of compassion in physicians, especially in emergency physicians, a field known for its high demand and stress levels, is currently scarce and additional evidence is needed to better describe and understand the association between physicians’ compassion and well-being.Keywords: compassion fatigue, emergency medicine, empathy, evidence-based emergency medicine, physicians’ role
Medical emergencies. Critical care. Intensive care. First aid
Abhinav Anand, Parvez Mohi Ud Din Dar, Preksha Rani
et al.
Abstract. Background. Pancreatic trauma (PT) accounts for less than 1% of all trauma admissions. Occasionally, PT is undetected during the primary survey and becomes apparent only when complications arise. It occurs in up to 5% of blunt abdominal trauma cases and 12% of individuals with penetrating abdominal injuries. Management is determined by the status of the main pancreatic duct and associated injuries.
Methods. This was an ambispective study conducted at the Jai Prakash Narayan Apex Trauma Center, All India Institute of Medical Sciences, New Delhi, from January 2015 to December 2017 (retrospective), and January 2019 to December 2020 (prospective). In total, 113 patients with PT were included in this study.
Results. We analyzed the data of 113 patients with PT included in this study, of which males predominated (93.7%). Blunt PT was present in 101 patients (89.4%) and penetrating PT in 12 patients (10.6%). Half of the patients (51.3%) had the American Association for the Surgery of Trauma grade III PT, followed by grade II (18.6%), and grade I (15%). Of the total 113 patients, 68 (60.2%) were treated with operative management, and 45 (39.8%) with nonoperative management. Distal pancreatectomy, with or without splenectomy, was the most common procedure performed in our study, followed by drainage. There were 27 mortalities (23.8%) during the study period, of which 7 were directly related to PT and 20 were due to other organ-related sepsis and hemorrhagic shock.
Conclusion. Pancreatic trauma is rare but challenging for trauma surgeons, with persistent management controversies. Early diagnosis is important for favorable results; however, a delay in diagnosis has been associated with higher morbidity and mortality. Low-grade pancreatic injuries can be successfully managed nonoperatively, whereas high-grade pancreatic injuries require surgical intervention.
Medical emergencies. Critical care. Intensive care. First aid