<![CDATA[Why teens use drugs and alcohol: avoidant attachment and emotional isolation fuel addiction; attachment-based therapy rebuilds bonds and cuts relapse.]]>
Path and Bayesian network analyses in the complex design of a well-being survey via New Zealand’s Integrated Data Infrastructure
IntroductionIn mental health research involved with sensitive features and privacy issues, using integrated data offers an efficient alternative to traditional approaches such as interviews or in−person data collection. These conventional methods frequently face logistical barriers, including low response rates among study populations. Using integrated data from multiple existing administrative and survey sources provides protection for participants and economic savings for researchers, despite being constrained by the limitations of the original data sources. Our research questions were: 1) Can we conduct path and network analyses of school absenteeism, psychosocial factors, and mental health outcomes using the integrated survey data from New Zealand’s Integrated Data Infrastructure (IDI)? and 2) How can we account for the complex design of the General Social Survey (GSS) to ensure representative inference?Materials and methodsThe study population was New Zealand youth enrolled in school, aged 15 years and older in 2018, who participated in the 2018 GSS. The study analyzed the Ministry of Education data integrated with the 2018 GSS survey data from New Zealand’s IDI. To explore the relationship between outcomes of including the WHO-5, “perceived life worthwhileness”, “perceived general health”, and predictors including school absenteeism, psychosocial factors, and family factors, quantile mixed−effects regression, path analysis, and Bayesian network (BN) analysis were used. The complex survey design was calibrated using replicated weights from the GSS, a design-based method for complex sampling, in path and regression analyses, and bootstrap resampling, in BN analysis.ResultsIn descending order from the weighted path model, overall life satisfaction (standardized path coefficient: 0.36), perceived health condition (0.34), ease in accepting cultural identity (0.12), and trust in the education system (0.07) were all significantly (positively) related to students’ mental health well-being (WHO-5). Perceived life worthwhileness correlated with the WHO-5 but was only connected to overall life satisfaction. Similar factors were identified for perceived health, with additional attributing factors, such as fear of crime in the area and the family’s overall well-being.ConclusionThe integration of Bayesian networks and path analysis offers rigorous methods for detecting complex interrelationships among integrated mental health outcomes from population data and variables from a complex survey design.
NeuroCon-AutismNet: a privacy-preserving multimodal framework toward autism screening via diffusion-regularized EEG biomarkers and empathy-aware multilingual dialogue
IntroductionAutism Spectrum Disorder (ASD) screening requires multimodal biomarkers to capture the heterogeneous neurological and behavioral phenotypes. Current screening approaches remain siloed across EEG analysis and conversational assessment, limiting integrated diagnostic architecture. Privacy-preserving machine learning frameworks for mental health screening are underdeveloped, particularly for multilingual deployment contexts. This paper presents NeuroCon-AutismNet, a candidate multimodal architecture integrating diffusion-regularized EEG synthesis, multilingual conversational screening, and formal differential privacy as architectural proof-of-concept. No diagnostic discrimination capability is claimed; all validation is scoped to synthetic evaluation.MethodsNeuroCon-AutismNet comprises four modules: (1) Temporal Diffusion Biomarker Generator (TDBG), a latent diffusion model over VAE-encoded 19-channel EEG; (2) Multilingual Affective Dialogue Screening Network (MADSN), a fine-tuned GPT-2-small module deployed in English, Spanish, and Hindi; (3) Neuro-Linguistic Fusion Transformer (NLFT), enforcing positional alignment as a design prior rather than learned cross-modal association; and (4) Adaptive Mixture-of-Experts Layer (AMEL-X) for entropy-regularized multimodal fusion. Formal (ε, δ)-differential privacy (ε = 1.0, δ = 1e-5) is verified via DP-SGD RDP composition (σ = 1.2, q = 0.0914, T = 550 steps, verified ε = 0.97). Privacy verification establishes architectural readiness for future real-data deployment; no real patient records are present in the training set.Results and DiscussionWithin closed synthetic evaluation, held-out diagnostic AUC is 0.503 (95% CI: 0.487–0.519, DeLong p = 0.67), statistically indistinguishable from chance and the central limitation of this study. Two partial external benchmarks are provided. Spectral comparison against three independently published real ASD EEG studies yields Pearson r = 0.87 across five frequency bands; delta and alpha directions are reproduced, but theta and gamma reproduce poorly with large amplitude errors (delta MAE 14.79%, alpha MAE 11.57%). Expert evaluation of MADSN outputs by 50 annotators under single-blind protocol yields 90% empathy satisfaction and Cohen’s κ = 0.82, reflecting text quality rather than clinical screening validity. The null diagnostic AUC and synthetic-only evaluation prevent any current screening or clinical-utility claims. Real-data EEG validation, clinician-caregiver interaction studies for MADSN, and DP-protected training on real patient records are prerequisites for future clinical deployment.
Usability and Feasibility of an In-Home Full-Color Imaging Scale for Remote Monitoring of High-Risk Patients With a History of Diabetic Foot Ulcers: Retrospective Observational Formative Evaluation
Background: Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations, amputations, and health care costs among individuals with diabetes, often due to delayed detection and treatment. Early identification of skin changes is critical for preventing ulcer progression, yet daily visual foot self-inspections are often limited by impaired mobility, visual deficits, and poor compliance. Although some foot-focused remote patient monitoring technologies are in use, most of these approaches do not provide standardized, full-color visual documentation, which can affect clinicians’ ability to interpret findings using familiar visual cues. Objective: This formative evaluation aimed to assess the usability, compliance, and feasibility of an in-home full-color imaging scale for the remote monitoring of patients with DFUs to inform the design of larger prospective studies. Methods: We conducted a retrospective formative feasibility evaluation analyzing 7 months of preexisting data from adults with a history of DFUs who had enrolled in an outpatient remote monitoring program. During this program, participants were provided with an internet-connected in-home scale that captured high-resolution, full-color scans of the plantar surface of the feet during daily use. Scans were securely transmitted to a HIPAA (Health Insurance Portability and Accountability Act)-compliant web portal for podiatrist review. Usability was assessed through compliance with daily scanning, patient and physician surveys, and descriptive review of clinical workflows. Results: A total of 21 participants aged 43 to 86 years (median 65, IQR 59-74; mean 65.3, SD 10.6 years) were retrospectively analyzed. Participants demonstrated high compliance and feasibility of home use, with device use on 82.5% (3638/4410) of possible days, a median use of 27 days (IQR 21-30) per 30-day period, and a mean use of 23 (SD 6.4) days per 30-day period. Participants reported high satisfaction and minimal burden associated with daily use, with most rating the device as physically easy to use (median 7.0, IQR 7-7; mean 6.9, SD 0.45), expressing willingness to use it daily (median 7.0, IQR 7-7; mean 6.9, SD 0.31), and indicating trust in the system (median 7.0, IQR 7-7; mean 6.9, SD 0.32) on a 7-point Likert scale (1=worst; 7=best). Clinician usability was supported by podiatrist review of 3295 scans, of which 3220 (97.7%) were rated as usable for clinical assessment. Three representative cases illustrated real-world device use and longitudinal image review, demonstrating the ability to visualize skin changes such as foreign body presence, new ulceration, and early skin breakdown. Conclusions: This retrospective formative evaluation demonstrates the preliminary usability and feasibility of an in-home, full-color imaging scale for daily remote visual monitoring of diabetic feet in a real-world outpatient setting. High compliance and usability in clinical workflows suggest that this approach may support early identification of foot-related concerns. Future prospective studies are warranted to evaluate the impact of this technology on clinical outcomes, including ulcer progression and health care use.
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Understanding cyber grooming in children and adolescents: a scoping review of risk factors and prevention strategies
The increasing digitalization of children’s daily lives has created new opportunities for communication, education, and social interaction while simultaneously increasing exposure to online exploitation risks. Among these risks, child grooming and cyber grooming have emerged as significant psychological, social, and public health concerns. Cyber grooming extends traditional grooming processes into digital environments through the use of anonymity, persistent communication, and technological accessibility, enabling offenders to manipulate and exploit children and adolescents in online settings. This scoping review aimed to examine the existing literature on grooming and cyber grooming, focusing on psychological, social, technological, and preventive dimensions. A literature search was conducted using PubMed, Scopus, and Web of Science databases for studies published between 2000 and 2025. Relevant findings were synthesized thematically to evaluate victim and perpetrator characteristics, developmental and environmental risk factors, prevention strategies, and legal approaches. The reviewed literature indicates that adolescents with high online engagement, limited digital literacy, emotional vulnerability, and inadequate social support may be at increased risk of grooming victimization. Offenders frequently utilize emotional manipulation, secrecy, and digital communication tools to establish trust and maintain control. Current evidence further suggests that effective prevention requires multidimensional approaches integrating digital literacy education, family involvement, psychological support, platform safety measures, and legal protections. Despite growing awareness of cyber grooming, important gaps remain regarding long-term psychological outcomes, prevalence variability, and the effectiveness of intervention programs. This review highlights cyber grooming as a multifaceted developmental and psychological phenomenon shaped by the interaction of individual vulnerabilities, online environments, and offender manipulation strategies. Effective prevention therefore requires coordinated, evidence-based, and multidisciplinary child protection efforts integrating psychological, technological, educational, and legal perspectives to improve children’s safety and well-being in digital environments.
Multilingual Voice AI for Postoperative Cataract Follow-Up in Turkish Speaking Patients in the United Kingdom: Patient and Public Involvement Focus Group Study
Background: Conversational voice AI assistants can automate postoperative follow-up calls in high-volume, low-complexity pathways such as cataract surgery but may widen health inequalities if language access and inclusive design are not built in. This patient and public involvement focus group was conducted to inform the Turkish-language adaptation of Dora ahead of a forthcoming multilingual clinical trial at Moorfields Eye Hospital. Objective: This study aims to inform the Turkish-language adaptation of Dora by gathering input from Turkish speaking community contributors about their experiences with UK ophthalmic care, language-related barriers, and design requirements for an equitable voice AI. Methods: We conducted a 1-time, 2-hour patient and public involvement focus group with 7 Turkish speaking adults recruited via the Derman community charity. The session ran in 2 phases: contributors first discussed their experiences with UK ophthalmic care, then evaluated a prerecorded Turkish-language telephone call from a voice AI to a Turkish speaking volunteer. The session was delivered bilingually, recorded with consent, and synthesized using an approach informed by the principles of reflexive thematic analysis. The voice AI uses automatic speech recognition and neural text-to-speech, with a large language model–based dialog manager for open-ended conversation within a postoperative review protocol. Results: Contributors described how pathway delays and limited language support shape their care, including reliance on family members for translation and concerns about privacy and autonomy. A language-concordant voice AI was conditionally acceptable for standardized postoperative follow-up, provided specific safeguards were met. Priorities included advance notice of calls, caller verification, privacy assurances, a clear standard Turkish accent at a slower pace, tolerance for regional dialects, interpersonal warmth, interactivity, accessibility for low vision and low literacy, and clinician escalation for complex issues. These priorities were synthesized into a 10-point checklist: preparation, verification, confidentiality, clarity and pace, voice, empathy, interactivity, dialect handling, accessibility, and efficiency. Conclusions: For patients facing language barriers, conversational voice AI may complement existing services when implemented with clear verification, privacy protections, and a defined scope under clinician oversight. The 10-item checklist will guide the Turkish-language adaptation of Dora and will be tested alongside similar consultations with other language communities in the forthcoming multilingual cataract follow-up trial.
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<![CDATA[How to recognize racial trauma, sharpen diagnosis, and apply “Cost of Capable” and “weathering” to build trust and assess suicide risk.]]>
AI is moving inside the device. Who owns the clinical risk?
Tips to mitigate the risk that medtech teams keep leaving unowned as artificial intelligence moves into Class II and III medical devices. By Dr. Sarah Matt, Vital Werks By the time a clinician trusts a wrong output from your artificial-intelligence-powered device, every decision that made the trust possible was made years earlier. How many decisions…
The post AI is moving inside the device. Who owns the clinical risk? appeared first on Medical Design and Outsourcing.
A Network Visualization Query System for Multidrug Compatibility Based on a WeChat Mini Program: Preliminary Usability and Efficiency Evaluation
Background: Intravenous drug incompatibility is a significant medication safety hazard, particularly in complex multidrug regimens. Traditional text-based, pairwise query methods are inefficient and impose a substantial cognitive load on clinicians. While network visualization has the potential to address these challenges, its application in drug compatibility queries remains underexplored. Objective: This study aimed to design, develop, and evaluate a novel drug compatibility query system based on a WeChat Mini Program. The system integrates diverse data sources and uses network visualization to present complex compatibility relationships. We sought to empirically assess its impact on efficiency and user experience. Methods: A preliminary crossover usability and efficiency evaluation was conducted. Phase 1 involved the construction of a drug compatibility knowledge base from authoritative handbooks and drug labels, and the development of the query system. Phase 2 comprised a system evaluation with 36 pharmacists, 6 physicians, and 5 nurses. The evaluation included a scenario-based task completion time analysis comparing the system (Mode A) with traditional print-based resources (Mode B), a quality assessment using the Mobile Application Rating Scale (MARS), and structured posttask user feedback to gather insights on user experience. Results: The query system demonstrated a substantial reduction in task completion time, with median time savings of 2.85 (IQR 1.98‐4.15) minutes, 5.45 (IQR 3.95‐7.20) minutes, and 31.2 (IQR 27.5‐35.1) minutes, respectively, in 3 scenarios with different complexity. The system received a high mean overall quality score of 3.88 (SD 0.35) on the MARS. The functionality dimension scored the highest (mean 4.21, SD 0.51), while engagement scored the lowest (mean 3.21, SD 0.62). Posttask structured feedback revealed five major areas of user feedback: (1) baseline experience and first impressions, (2) user experience with network visualization, (3) perceived efficiency and cognitive load, (4) trust and information quality, and (5) future application considerations. Users praised the system’s efficiency and intuitive design but expressed a strong need for transparent data sources and management advice to build trust. Conclusions: A query system based on network visualization demonstrates potential to support the efficiency of multidrug compatibility queries within this preliminary evaluation. It may mitigate cognitive load and offer an at-a-glance understanding of complex drug relationships. However, formal clinical accuracy validation remains a mandatory precondition before bedside clinical deployment is considered.
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Defining and Assessing Empathic Communication in Patient Portal Secure Messages: Adapted Coding Framework Development Study
Background: Empathic communication in the clinical setting has been associated with improved clinical outcomes, decreased anxiety, and increased patient satisfaction, treatment adherence, and trust. Despite the recent growth in patient portal use, the expression of empathic communication through patient portal secure messages is not well understood. Objective: This study aimed to construct a coding schema to define and assess empathic opportunities initiated by patients and primary care clinicians’ responses to these opportunities within a patient portal messaging environment. Methods: Data for this study included adult patient secure messages and responding messages from clinicians working in family practice clinics within a regional health care system serving central and northeast Pennsylvania between January 2018 and December 2023. We conducted a manual review of messages using the Empathic Communication Coding System as a guiding framework, which defines empathic opportunities created by patients and corresponding empathic responses by clinicians. We double-coded 500 patient messages for 3 empathic opportunity types: statements of emotion, progress, and challenge. Coding definitions were iteratively updated to describe specific textual cues unique to the patient portal context. This procedure was repeated to code for empathy in clinician responses to empathic opportunities. An additional 100 patient messages were double-coded for interrater reliability testing, and 500 patient messages were single-coded using the finalized coding schema. Results: Among 1100 patient messages coded, 576 (52.4%) included an empathic opportunity. Of these messages, 100 (17.3%) included a statement of emotion, 85 (14.7%) included a statement of progress, and 539 (93.6%) included a statement of challenge. Statements of challenge were primarily characterized by patients explicitly describing physical or mental health issues, a barrier in care, or difficulties in their personal lives. Among the 576 patient messages with empathic opportunities, 446 (77.4%) received at least 1 response from a clinician. Clinicians sent 483 response messages, of which 64 (13.2%) expressed empathy. Conclusions: While patients created empathic opportunities in over half of patient portal secure messages, primary care clinicians infrequently responded with empathy. The findings of this study provide initial evidence of gaps in empathic communication within patient portal secure messages and lay the groundwork for using artificial intelligence models to systematically measure and improve this communication across the patient portal messaging system.
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