JMIRx Med

Background: Electrolyte replacement is ubiquitous in the acute care setting, but its familiarity cannot belie that even small dosing errors with potassium can cause lethal cardiac arrhythmias. Recently, MedAgentBench offered a benchmark for agentic artificial intelligence (AI) including the ability to correctly dose potassium based on a single rule; however, this does not adequately reflect the clinical complexity or safety concerns of an agent that has been used as the lethal injection. The purpose of this analysis was to a probe leaderboard large language model (LLM) capabilities to follow basic dosing rules to safely replace potassium in a series of clinician-annotated cases. Methods: Using a clinician panel, we developed a series of dosing principles and 20 clinical cases reflective of the complexity of potassium replacement. External clinicians were surveyed to assess practice variability and agreement to clinician panel answers. We tested GPT-5-chat with each case in triplicate, with and without the clinician curated dosing principles, and prompted the model to answer six questions involving potassium goals, dosing, route, lab frequency, concurrent interventions, and the model's perceived level of confidence for the output and complexity of the case. The primary outcome was the rate of appropriate recommendations in comparison to clinician answers. Results: A total of 54 clinicians reviewed the 20 hypokalemia cases and hypokalemia dosing guideline. Clinicians expressed "highly agree" or "somewhat agree" for 66.8% of the cases evaluated when asked if they agree with the guideline-recommended management. When given the potassium dosing guideline, total errors dropped from 165 to 104, and average accuracy improved from 45% to 65% with GPT-5-Chat. GPT-5-Chat conveyed a high level of confidence for 100% of responses, while labeling 80% and 76% of cases as highly complex with and without the criteria, respectively. Potential harm scores were considerable in both groups, however, a notable reduction in severity scores occurred with the dosing guidance document. Recommendations on concurrent interventions and dosing had the highest rate of errors in both groups. Conclusions: Benchmarks must appropriately reflect clinical complexity to be considered valuable for the deployment of agentic artificial intelligence tools in the healthcare domain. GPT-5-Chat assessment on a comprehensive medication management task for potassium replacement showed improvement with dosing guidance, yet unfit benchmarking performance.

Background: Concerns about "AI psychosis" have swirled in the media since ChatGPT's release, but few systematic analyses exist. We therefore conducted an electronic health record (EHR) analysis to identify the frequency, clinical characteristics, and quality of AI interactions in patients experiencing psychosis treated in a medical center. Methods: AI keywords (e.g., ChatGPT, AI) were used to search Vanderbilt University Medical Center's EHR from 12/1/2022-4/1/2026. Records were discarded if they were not AI-related or if the primary diagnosis did not include psychosis. Three raters read notes to determine if a patient was experiencing AI psychosis and classified the interactions using 4 a-priori categories (Catalyst, Amplifier, Co-Author, Object) formulated to explain how AI-related negative outcomes emerge. Findings: 73 patients met our criteria. 28 patients were rated as experiencing AI psychosis, 17 had neutral interactions, and 28 expressed delusional content related to AI without documented evidence of conversational AI use. ChatGPT was the matching keyword for 53.6% patients experiencing AI psychosis. The majority of AI psychosis cases were documented after ChatGPT's "4o" model was released in May 2024. Notably, the AI Psychosis group had significantly more patients experiencing a first psychotic episode (60.7%) compared to the other two groups. Amplifier was the most common (64.3%) qualitative rating in the AI Psychosis group. Interpretation: "AI psychosis" is an infrequent but real phenomenon observed in clinical practice. Most affected patients were experiencing their first psychotic episode and presented with AI psychosis following the release of the more sycophantic GPT-4o. Among the affected patients, AI most often exacerbated an existing condition by reinforcing distorted ideas.

Introduction: The use of artificial intelligence (AI) by clinicians has increased rapidly in recent years, with large language models (LLMs) emerging as tools that can equal clinician diagnostic performance in simulated settings. However, limited data exist regarding physicians use of LLMs in real-world clinical practice. This study aimed to evaluate the frequency of LLM use among practicing hospitalists, identify which LLMs are most commonly utilized, and assess hospitalists' perceptions of the benefits and limitations of LLM use in clinical care. Methods: We conducted a cross-sectional survey study of academic hospital medicine faculty across 8 institutions within the Hospital Medicine Reengineering Network (HOMERuN), a collaborative research consortium. Eligible participants included hospitalists practicing within participating HOMERuN sites during the study period. The survey assessed the frequency of LLM use, types of LLMs used, clinical applications, and physician perceptions regarding usefulness, efficiency, and concerns associated with LLM adoption. Results: 170 respondents (67.1%) reported ever using an LLM in clinical practice. Among LLM users, OpenEvidence was the most used tool (88.9%), followed by ChatGPT (58.5%), Google Gemini (26.9%), and Microsoft Copilot (20.5%). Only a minority of hospitalists reported using LLMs daily while seeing patients. The most common use cases of LLMs were answering diagnostic (77.1%) and management (77.6%) questions. A majority also reported using LLMs to identify or summarize primary literature (60.0%). Lack of trust in outputs (49.8%), uncertainty around institutional policies (48.6%), and lack of access to secure applications (43.1%) were cited as the most frequent barriers to using LLMs in practice. Discussion: The use of LLMs in clinical practice is already widespread, though regular or daily use is not yet typical. Concerns regarding reliability, patient privacy, and safe integration into clinical workflows remain significant barriers to broader adoption. The responsible implementation of LLMs in hospital medicine will require addressing these barriers.

Objectives: Older adults with comorbidities and polypharmacy have disproportionately high risk of hospitalization as well as readmission from adverse drug events (ADEs), of which 28%-71% are preventable (pADEs). This paper introduces an LLM application, CommunicADE, designed to support risk-mitigation of pADE-related readmission for the aforementioned population. We aim to evaluate CommunicADE's technical performance with OpenAI's HealthBench criteria: accuracy, completeness, communication quality, context awareness, and instruction following. Materials and Methods: Our technical validation study used an LLM (KimiK2.5) to simulate interviews between CommunicADE and nine high-fidelity synthetic patients hospitalized and at increased risk for pADE-related readmission (65+ years, comorbidities, 5+ medications). Some pADE risk mechanisms clues were visible to CommunicADE in patient H&Ps, but most mechanisms were solely discoverable in interviews. Two pharmacists evaluated CommunicADE's interview questions and EHR notes with HealthBench-informed variables. Analyzes used descriptive statistics. Results: For 35 mechanisms across 9 patients (avg=3.89 mechanisms/patient), CommunicADE's precision and recall were 0.92 and 0.63, respectively. Hallucinations were absent. Coherence and person-centeredness scored 4.28 and 4.44 on a 5-point scale (5=highest). On average, communication was at a 5th grade level and objective for 78% of patients. Most patient-reported quotes included in notes (92%) supported detected mechanisms. CommunicADE followed all instructions regarding interview length and patient approvals. Discussion: CommunicADE's strongest performance was in accuracy (precision, hallucinations), communication quality (coherence, readability), context awareness (person-centeredness). Completeness (recall) and instruction following (objectivity, pADE mechanism/quote alignment) show room for improvement. Conclusion: Findings suggest technical readiness for a feasibility pilot with real-world patients, and key areas for performance improvement.

Background: With growing impetus to integrate artificial intelligence (AI) tools into radiology, clinical practices must navigate workflow redesign. This carries implications for medical malpractice liability. Methods: We conducted an online vignette experiment with United States adults who acted as hypothetical jurors in a malpractice case involving a missed intracranial hemorrhage. Participants (n=2,347) were randomized to one of 22 conditions: a no-AI control and 21 conditions involving a hypothetical AI system. These twenty-one conditions varied by whether (1) a single-read or double-read workflow was used, (2) the radiologist's initial interpretation was documented, (3) the radiologist changed their interpretation after viewing AI output, (4) the AI detected the abnormality, and (5) the AI error rate--False Discovery Rate (FDR) or False Omission Rate (FOR--was provided to participants only, both participants and radiologist, or neither. The primary outcome was perceived liability, assessed by whether the radiologist met their duty of care. Findings: Perceived liability differed across conditions (p<0.0001). Double-read workflows (p<0.0001), documenting initial interpretations (p=0.0125), and providing participants with AI error rates, including the FDR (p=0.0038) or FOR (p=0.0035), reduced perceived liability. Liability was also lower when AI was incorrect (p<0.0001). Radiologists' awareness of AI error rates did not significantly impact liability. Notably, we observed an erroneous change penalty: the greatest liability occurred when radiologists initially identified an abnormality but later changed their interpretation to normal after seeing that AI identified the case as normal; conversely, perceived liability was lowest with documented, double-read workflows. Interpretation: Double-read workflows with documented initial interpretations and disclosure of AI error rates reduce perceived liability, though changing a correct initial interpretation increases it. Strategic workflow design is critical for successful AI implementation that can mitigate malpractice risk.

INTRODUCTION In the multiple-risk approach of cardiovascular disease management, communication of cardiovascular disease risk and its prevention play a significant role. in Sri Lanka, this function is conducted via Healthy Lifestyle Centres. METHODOLOGY A clinical audit was conducted to describe communication quality in 79 healthy lifestyle centers. A checklist developed based on Patient-Centered Communication Tools with the support of an expert panel. Two trained observers independently conducted the observations while the healthcare provider at the Healthy Lifestyle Centre revealed details of cardiovascular disease risk communication and health education sessions. RESULTS: The majority of Healthy Lifestyle Centers involved patients in decision-making (n = 228, 92.0%), explained patient choices (n = 230, 92.8%) and responded to patients' interest in decision-making (n =2 35, 99.2%). Most patients received a summary (n =159,67.1%), a follow-up plan (n =212,89.5%) and were communicated in a jargon-free language (n =127,53.6%). The majority of sessions demonstrated satisfactory use of examples (95.7%, n = 22), and responsiveness to questions (73.9%, n =17). However, most sessions were unsatisfactory regarding provision of a follow-up plan (95.7%, n = 22), encouragement of questions (95.7%, n = 22), allowing clients to talk (87.0%, n = 20), and active listening (69.6%, n = 16). CONCLUSIONS: Strengthening healthcare worker training in patient-centred communication especially shared decision-making, active listening, and encouraging patient questions are essential to improve cardiovascular disease risk communication and patient adherence to preventive guidelines at Healthy Lifestyle Centres. Key words Communication, Cardiovascular disease risk, health communication, effective communication, paternalistic healthcare

Youth in the United States are experiencing growing mental health challenges, yet many face barriers to accessing timely, affordable, and stigma-free support. At the same time, artificial intelligence (AI) chatbots have become widely available and are increasingly being used by young people as tools for information seeking, coping, and self-reflection. This brief report explores how youth are engaging with AI chatbots for mental health support, drawing from qualitative interviews conducted in New Jersey. Nine semi-structured interviews were completed with participants ages 19-22. Thematic analysis revealed five core themes: (1) generational change, peer communication, and humor as coping and normalization tools; (2) internal and external barriers to self-recognition and help-seeking; (3) AI chatbots as a safe and accessible first step; (4) AI chatbots as a tool for filling information gaps; and (5) limits of AI chatbots and the preference for human connection. These findings indicate that young people see AI chatbots as private, judgment-free starting points for exploring their emotions and seeking early support. However, they also recognize that these tools cannot replace human connection or professional care. For public health, this presents both challenges and opportunities in utilizing the accessibility of AI chatbots while ensuring ethical design, cultural responsiveness, and protections that safeguard youth privacy and equity.

Background: Despite the significant risks associated with online substance procurement (SP), few researchers have examined this practice in U.S. youth. The studies that do exist are cross-sectional and cannot temporally connect specific digital behaviors to online SP. This longitudinal cohort study examined youth SP and digital media habits to determine whether use of certain smartphone applications correlated with increased odds of online SP or being contacted online about procuring drugs or alcohol. Methods: A cohort of U.S. youth (aged 15-20) with a history of non-daily substance use in the 3 months prior to enrollment was recruited to use the digital phenotyping smartphone application EARS for 90 days. On a nightly basis, participants were asked to complete surveys about online experiences related to SP and instances of substance use. Smartphone-generated screen use data were also collected passively each day. Results: Out of 112 enrolled participants, 106 were able to be included in analyses. Over approximately 3 months, 28.3% of participants (n=30) reported a collective 91 instances where they used social media to acquire drugs or alcohol. Screen use data demonstrated temporal relationships between social media SP and applications previously connected to the social media drug-purchasing process (e.g., TikTok, encrypted apps), as well as other school-specific social media. Discussion: Our results provide critically needed research evidence to support a body of literature composed predominantly of anecdotal reports. Despite measures taken by social media companies to prevent use of their platforms for drug procurement, underage youth continue to engage in this practice.

Although large language models (LLMs) have shown promise for discharge summary generation, their value may be greater in longer hospitalizations, where increasing documentation volume and complexity increase both clinician burden and the risk of communication failures during transitions of care. Prior evaluations of LLM-generated discharge summaries have largely involved shorter stays and have rarely examined receiving-clinician priorities or incidental finding reporting. We compared LLM-generated and human-authored discharge summaries for 60 Internal Medicine hospitalizations lasting 7 to 21 days, with paired assessment by hospitalists and primary care physicians (PCPs). Clinician reviewers preferred LLM-generated summaries for 95% of encounters and rated them higher for quality, readability, factuality and completeness. PCPs, the primary recipients responsible for post-discharge care, found that LLM-generated summaries were better for understanding and communicating hospital care to patients, and providing follow-up care. LLM-generated summaries had fewer annotated errors, primarily due to fewer omissions, without increased estimated harm potential or likelihood compared with human-authored summaries. Benefits of LLM-generated summaries were especially salient for PCPs, who identified more omissions with greater downstream likelihood of harm than hospitalists. This underscores the importance of designing transition documents around the needs of clinicians assuming care post-discharge. LLM identification of radiology incidental findings was generally accurate and appropriate, suggesting potential to improve follow-up of clinically relevant findings. These findings extend prior work by demonstrating clinical value of LLMs in summarizing longer, complex hospitalizations and highlighting the value of stakeholder-centered design in clinical AI systems. Together, they support supervised LLM-assisted discharge summarization as a tool to reduce cognitive burden, improve documentation quality, and enhance transition-of-care communication.

Introduction: Cardiovascular diseases (CVDs) are the leading cause for death and disability worldwide accounting for 75% of deaths in low- and middle-income countries (LMICs) like Nepal. Urbanization and globalization remains the major cause of rise in CVDs among urban poor population along with growth in slum settlements. This study aims to assess the knowledge, attitude and practice (KAP) of CVDs and its risk factors among women of one such urban poor community in Nepal. Methodology: This cross-sectional study (n=388) in the Sinamangal-Minbhawan slum area was conducted using semi structured questionnaire based on STEPs survey and HARDIC study among the participants selected through convenient sampling. Descriptive analysis was done using SPSS version 21 and KAP scores were further categorized based on median score to perform multivariate logistic analysis. Additionally, Anthropometric and blood pressure measurements were also recorded and analyzed. Results: The median age (Interquartile range) of participants was 33 years (17) with majority of them being Dalit by ethnicity, housewives, with up to primary level education belonging to upper lower socioeconomic class. More than half (53.3%) of the participants were obese and over 23% were hypertensive. While half of the hypertensive women were aware of their status, only 3% had their blood pressure under control.The median knowledge, attitude and practice (KAP) scores were 12, 60 and 10 respectively. The KAP scores were positively associated with socioeconomic status of the participants. Conclusion: The study revealed low knowledge with high prevalence of behavioral risk factors of CVDs along with high prevalence of other metabolic risk factors like high body mass index, high waist hip ratio and hypertension among women of slum area with a positive attitude to prevent CVDs and its risk factors.

Background High Intensity Use of urgent medical services by patients is widely recognised in urgent and emergency care. Studies of high intensity use of the emergency department have consistently shown features of complex systems behaviour in addition to highly heterogeneous individual patient characteristics. There have been no comparable studies of prehospital care use. Methods We examined the use of prehospital urgent and emergency services (NHS 111 and ambulance dispatch) using routinely collected data from regional service in the UK (population 5 million). We used a complex systems perspective, to examine (1) distribution of contacts per individual; (2) the temporal stability of service use by individuals and at the whole-system level (3) the distribution of bursts of contacts. Results We analysed data from 847555 individuals who contacted NHS111 and 389550 who contacted the ambulance dispatch service. 35120 (4.2%) individuals who contacted NHS111 had 5 or more contacts with the service over the two-year period and accounted for 290625 (20.1%) of contacts. 16755 (4.3%) individuals had 5 or more ambulance dispatch contact days and accounted for 169085 (25.8%) of contacts. The distribution of contacts per individual showed a monotonic distribution between 5 and over 100 contacts that was heavy tailed and compatible with a power law distribution. At any level of use, patients with one or more mental health related contacts had a greater likelihood of further contact than those without. Conclusion Prehospital emergency service use shows multiple statistical features typical of a complex system. Interventions to manage demand need to consider both individual high intensity users (particularly in relation to their mental health) and the behaviour of the whole system.

This study analyzes 794,811 digitized medical examina- tions from Indian life-insurance applicants, a working-age, urban-skewed demographic often undersampled by national surveys. The cohort exhibits a pronounced South-Asian car- diometabolic risk profile: among valid adult records, 41.9% met the criteria for dyslipidemia (driven heavily by low HDL and elevated triglycerides), and 61.4% met AHA 2017 crite- ria for stage 1 hypertension. However, canonicalizing this dataset across 33,244 diagnostic centers revealed significant heterogeneity in laboratory reference ranges. At the clinical prediabetes threshold of 110 mg/dL for fasting blood sugar, the record-pair disagreement rate across laboratories was 49.7%, with similar variance across other common tests. This structural inconsistency materially affects patient classi- fication and the tracking of disease prevalence, underscoring a critical need for the national standardization of laboratory reporting in India

This study aims to assess women's perceptions of artificial intelligence (AI) used in breast cancer screening in France by examining their knowledge of AI and the barriers to their participation in organized screening. The results of a survey conducted in June 2025 among a national sample of 2000 women (aged 40-75) reveal limited participation and persistent concerns among women. Nevertheless, despite a low awareness of specific AI applications, a large majority of the women surveyed are very favorable to the use of AI in breast cancer diagnosis, even considering it a lever to increase screening participation.

BackgroundPrevious studies evaluated large language model (LLM) performance on the American Society of Nuclear Cardiology (ASNC) Board Preparation Exam. Without domain-specific context, the best model (GPT-4o) achieved 63.1%, below the estimated 65% passing threshold and the 78% mean score of human fellows-in-training (FITs). Providing textbook context improved GPT-4o to 73.8% on text-only questions, but still fell short of human trainees. Whether next-generation LLMs with retrieval-augmented generation (RAG) can exceed this gap is unknown. MethodsClaude Opus 4.7 and GPT-5.5 were administered all 168 questions (141 text-only, 27 image-based) from the 2023 ASNC Board Preparation Exam across 5 iterations each, using RAG with a nuclear cardiology textbook, companion atlas, and ASNC clinical guidelines. Claude used local FAISS-based semantic retrieval; GPT-5.5 used Azures cloud-hosted vector store. Performance was compared to prior LLM results and 13 human FITs. ResultsAcross 5 iterations, Claude Opus 4.7 achieved a mean accuracy of 86.3% {+/-} 1.4% (text 88.8%, image 73.3%). GPT-5.5 achieved 86.7% {+/-} 2.2% (text 88.5%, image 77.0%) but refused a mean of 12.2 questions (7.3%) per iteration due to safety filters. Both models surpassed the human FIT mean (78.0%) and the estimated passing threshold. Compared to GPT-4o without context (63.1%), this represents a 23-percentage-point improvement in 18 months. ConclusionNext-generation LLMs with RAG now surpass average human trainee performance on nuclear cardiology board preparation questions, suggesting significant potential as educational tools and knowledge-reference aids in cardiovascular imaging. Condensed AbstractAcross 5 iterations each, Claude Opus 4.7 and GPT-5.5 with retrieval-augmented generation achieved mean accuracies of 86.3% and 86.7% on the 2023 ASNC Board Preparation Exam (168 questions), both surpassing the mean human fellow-in-training score of 78%. GPT-5.5 refused a mean of 12.2 questions (7.3%) per iteration due to safety filters. These results represent a 23-percentage-point improvement over the best prior LLM without context (63.1%), demonstrating that RAG-enhanced LLMs have reached human-level proficiency in nuclear cardiology knowledge. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/26352768v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@5f2465org.highwire.dtl.DTLVardef@4e80d3org.highwire.dtl.DTLVardef@1ebbb93org.highwire.dtl.DTLVardef@167d3c1_HPS_FORMAT_FIGEXP M_FIG C_FIG Overview of the three-study research arc evaluating LLM performance on the 2023 ASNC Board Preparation Exam. Study 1 (2024) tested four LLMs without context (best: GPT-4o, 63.1%). Study 2 (2025) added textbook context to GPT-4o (73.8%). Study 3 (2026, current) evaluated Claude Opus 4.7 and GPT-5.5 with retrieval-augmented generation across 5 iterations each (mean 86.3% and 86.7%, respectively), both surpassing the human fellow-in-training mean of 78%. Right panel shows the performance scale with key thresholds.

Background: Large language models (LLMs) are increasingly used in telehealth, but their safety in antibiotic prescribing remains uncertain, particularly in the presence of patient misinformation. Methods: A cross-sectional analytical study evaluated 5,000 responses from five chatbot models using 1,000 primary-care vignettes of mild infections. Guideline adherence, overprescribing, misinformation effects, and safety behaviors were assessed. Inappropriate prescriptions were classified using the WHO AWaRe framework. Results: Overall, 76.2% of responses were guideline-concordant, while 6.6% showed unprompted overprescribing and 17.2% were influenced by misinformation. Some models were more vulnerable to misinformation than others. Although most responses correctly noted that antibiotics do not treat viral infections, fewer advised consulting a doctor, and warnings against self-medication were rare. Many inappropriate prescriptions involved broad-spectrum antibiotics. Conclusion: LLMs show potential in telehealth but remain prone to misinformation and inappropriate prescribing. Stronger guideline integration and clinical oversight are necessary to ensure safe use. Keywords: antimicrobial stewardship; large language models; telehealth; antibiotic prescribing; misinformation; clinical safety

BackgroundSmartphone addiction is an emerging public health concern among university students in sub-Saharan Africa. Limited data exist on its prevalence and associated factors in Uganda. ObjectiveThis study aimed to determine the prevalence of smartphone addiction and its associated sociodemographic and economic factors among students at Islamic University in Uganda (IUIU). MethodsA cross-sectional study was conducted among 287 undergraduate students at IUIU Kampala campus. Data were collected using a structured self-administered questionnaire incorporating the Smartphone Addiction Scale Short Version (SAS-SV). Bivariate and multivariate analyses were performed using modified Poisson regression. ResultsThe prevalence of smartphone addiction was 76.7% (95% CI: 71.4 to 81.2). Female students were 1.16 times more likely to be addicted than male students (APR: 1.16; 95% CI: 1.04 to 1.32). Students who spent more time on smartphones than on academic revision were 1.33 times more likely to be addicted (95% CI: 1.11 to 1.61). Those using smartphones for five or more hours daily were 1.32 times more likely to be addicted (95% CI: 1.02 to 1.48). ConclusionSmartphone addiction is highly prevalent at IUIU. Female gender and prolonged daily screen time are significant independent predictors. Targeted digital wellness programmes and institutional policy interventions are urgently needed.

Introduction: Stroke care is time-sensitive, yet patients in low-resource settings may reach tertiary services only after passing through multiple formal and informal care options. This study examined documented care-seeking pathways and time to presentation among stroke cases recorded at the University of Medical Sciences Teaching Hospital (UNIMEDTH), Ondo State, Nigeria. Methods: A retrospective hospital record review was conducted using secondary data from the Stroke Registry, radiology department records, referral notes, and ambulance records at UNIMEDTH. The analysis included 371 stroke cases with documented time from symptom onset to UNIMEDTH presentation and reconstructable care pathways. First-contact routes were classified as hospital/biomedical, self/informal or traditional/faith-based care, and the number of documented steps defined pathway complexity before and including tertiary presentation. Frequencies and percentages described pathway patterns; median presentation times were compared using Mann-Whitney U and Kruskal-Wallis tests. Results: The median time to tertiary presentation was 24 hours (interquartile range [IQR] 9-72), and 317 patients (85.4%) presented after four hours. Only 30 patients (8.1%) presented directly to UNIMEDTH; 44 distinct care-pathway sequences were recorded. Hospital-facility first contact was documented for 81 patients (21.8%). It was associated with a median presentation time of 3 hours (IQR 2-6), compared with 48 hours (IQR 24-72) among patients whose initial contact was outside a hospital facility (U = 699.50, p < 0.001). The median time also differed across grouped first-contact categories and pathway complexity levels (both p < 0.001). Conclusion: Non-hospital or multi-step care-seeking pathways commonly preceded tertiary stroke presentations in this setting. The findings indicate that delayed tertiary arrival is partly embedded in the pathway followed after symptom onset. Interventions should combine public recognition of stroke warning signs with urgent referral linkages involving hospitals, patent medicine vendors, traditional and faith-based providers, and emergency transport systems.

Background The use of oral retinoids and valproate during pregnancy can cause birth defects. In 2018, the EMA revised Pregnancy Prevention Programs (PPPs) for these medications. Pharmacy technicians in Denmark dispense prescription medications and must counsel customers. Aims This study aimed to examine knowledge of the teratogenicity of oral retinoids and valproate and use of the relevant PPPs among pharmacy technicians in Denmark. Methods A cross-sectional survey was conducted in spring 2025 using questionnaires developed for and tested in an international project. Data was collected via relevant Facebook groups and email invitations. Descriptive statistics were used for analyses. Results For oral retinoids, 80 respondents were analyzed; 95% were women, 86% were pharmacy technicians, the mean age was 37.2 years. Most dispensed oral retinoids several times per month. Two respondents did not know retinoids were teratogenic. The most used PPP measure was the outer packaging warning (54%). Informing women about teratogenic effects was the most common practice. For valproate, 41 respondents were analyzed. Their characteristics were similar to those of respondents in the oral retinoid survey. Most dispensed valproate once per month. One-third did not know valproate was teratogenic. The outer packaging warning was used by 19%. The most common practice was referring to the prescribing physician if pregnancy was suspected. Conclusion Danish pharmacy technicians knowledge about teratogenic drugs and the PPP was poorer than that of pharmacists, especially regarding valproate, and requires attention in educational programs. The feasibility of PPP measures for both oral retinoids and valproate should be optimized.

Background: The global SARS-CoV-2 pandemic disrupted healthcare systems worldwide, raising concerns about its impact on clinical research. Early reports suggested reductions in participant enrollment, interruptions to ongoing trials, and challenges to protocol adherence, yet the magnitude and duration of these operational disruptions remain unclear. Methods: We conducted a registry-based analysis comparing clinical trials during the COVID-19 pandemic (December 2019 to November 2022) with a matched pre-pandemic cohort (December 2016 to November 2019). Studies were included if they reported any modifications to trial status, enrollment, or protocols during the study periods. Key variables included trial stoppage, enrollment changes, and adoption of remote or hybrid procedures. Results: The global SARS-CoV-2 pandemic resulted in widespread disruptions to trial operations with 13,323 clinical trials terminated, suspended or withdrawn over the course of the pandemic, a 38% increase compared to the 9,665 trials that stopped in the 3 years prior to the pandemic. Registries indicated a sharp decline in new participant enrollment across geographic regions and therapeutic areas, with partial recovery in later months. Review findings highlighted barriers including patient inaccessibility, staff redeployment, and supply chain interruptions. Conclusions: The pandemic caused system-wide operational shocks that compromised trial timelines and may have downstream methodological consequences. Recovery in enrollment does not imply restoration of pre-pandemic protocol fidelity or outcome ascertainment. Standardized reporting of disruptions, proactive contingency planning, and resilient trial designs are needed to maintain data integrity during large-scale disruptions and to support reliable evidence generation.

Background: Mental health issues are prevalent among men, yet help-seeking remains low due to stigma, masculinity norms and access barriers. Digital mental health (DMH) screening questionnaires offer opportunities for early detection, but their uptake among men is limited. Objective: This study explored the barriers and facilitators influencing mens willingness to use DMH screening questionnaires, with the aim of informing user-centered design that supports early detection and engagement. Methods: This interpretive qualitative study was conducted through semi-structured interviews with 17 purposively sampled Estonian men (aged 20-54) in a highly digitalized context until data saturation was reached. Thematic analysis followed a mixed deductive-inductive approach: deductive codes were derived from theoretical frameworks (Technology Acceptance Model, Health Belief Model, User-Centered Design, Behavioral Design), while inductive themes emerged from participants responses across the three research questions, including their evaluations of four screening questionnaire (PHQ-2, PHQ-9, EEK-2, WHO-5). Results: Key barriers included data privacy fears, distrust of digital solutions, lengthy questionnaires, and poor user experience (UX). Facilitators were anonymity, institutional trust, short (5-10 min) questionnaires, mobile-optimized design, personalized feedback, and clear next steps. As main contribution, four archetypes were identified: Skeptic, Self-Manager, Explorer, and Situational Seeker. They reflected distinct patterns across privacy concerns, institutional trust, user experience preferences, and help-seeking orientations. Skeptics were characterized by low institutional trust, high concern about data misuse, and a preference for anonymous, low-friction interactions, often delaying help-seeking. In contrast, Self-Managers emphasized autonomy, transparency, and evidence-based support, engaging in structured self-monitoring and purposeful help-seeking. Explorers showed openness to experimentation and engagement, particularly when supported by intuitive, interactive, and visually clear UX, while data sharing depended on perceived value. Situational Seekers demonstrated episodic engagement patterns, where trust, data-sharing, and help-seeking were highly context-dependent, preferring fast, low-effort interactions when needed. Conclusions: Mens uptake of DMH screening questionnaires is influenced by a combination of social, psychological, and usability factors. Effective design should integrate anonymity, institutional credibility, and user-centered features to support engagement and early mental health detection. Personalized, actionable feedback with transparency, user control, and clear next-step guidance emerged as key drivers of sustained engagement, while poor usability and lack of meaningful feedback led to disengagement. Importantly, the proposed archetypes capture how these factors co-occur in dynamic, context-dependent user profiles, offering a more actionable alternative to one-size-fits-all and demographic approaches for designing DMH questionnaires tailored to male users.