DIGITAL HEALTH

Background: Patient Decision aids (PtDAs) are important tools that support shared decision-making (SDM) between clinicians and patients, enabling patients to be more informed and engaged in decisions regarding their care. The use of PtDAs can increase patient knowledge, reduce decisional regret, and engage the clinician and patient in meaningful dialogue. Shared decision-making relies on decision aids, but how patients receive the PtDA can vary. This qualitative study elucidated patient familiarity with PtDAs and the barriers, needs, and requirements of patients to access and confidently use DAs, including when received via a clinician-facing EHR-integrated Decision Aid Navigator System (DEANS). Methods: We conducted six focus groups (FG) with participants who were recruited from two academic medical centers. Focus groups included an introduction to what a PtDA is and an exploration of how patients or family members, i.e., people who had not made a medical decision for themselves using a PtDA, but who have helped a family member, had previously used PtDAs in their healthcare. The FGs focused on PtDA-users, affordances and barriers to using PtDAs, and PtDA-user needs and requirements of a proposed DEAN patient portal. We applied Rapid Qualitative Analysis (RQA) to summarize the data, using a consensus making approach to identify emerging themes. Results: We identified three overarching themes: 1) Patients had varied familiarity with PtDAs and often confused them with general health tools, but found value in SDM, 2) Patients perceived technology accessibility, PtDA credibility, and time as barriers potentially impeding PtDA use, and 3) Patients valued flexible, user-friendly, and accessible PtDAs that support patient-led decisions, particularly when introduced by clinicians and delivered via a patient portal. Conclusion: This study provides patient-derived insights into factors that influence PtDA uptake, highlighting common misconceptions about PtDAs and identifying key patient priorities such as accessibility, flexibility, clear content, and clinician-guided usage. Patient also expected clinicians to be familiar with PtDAs, especially when the PtDA covers a complex decision. These findings can inform the development of more patient-centered SDM infrastructure and strategies to support PtDA use in clinical practice, including clinician-supported delivery, accessibility, and integration into routine care to strengthen shared decision-making.

Patient narratives can reveal aspects of health that standardized measures miss, yet collecting open-ended responses at population scale remains difficult. We evaluated whether speech-to-text (STT) could enable richer responses in online health surveys without the resource demands of interviews. Across four surveys involving people with post-COVID-19 condition, healthy adults, people who engage in sex work, and older adults, participants could answer open-ended questions by typing or using STT. We compared response length, lexical diversity, stopword proportion, and total unique content words, alongside STT uptake and participant feedback. Quantitative comparisons were made using a mixed-effects model including modality (STT vs typed) as fixed effect and a random intercept per participant. STT uptake varied markedly across populations (0.7-80.0%). Spoken responses were estimated to contain more characters than typed responses in two studies, by 245.0 and 891.8 characters on average (both p<0.001), and contained more unique content words, despite lower lexical diversity and higher stopword proportions. Participants generally valued STT for its convenience and spontaneity, but uptake was constrained by context, privacy, interface design and individual preferences. STT may enable population-scale collection of richer health narratives, provided implementation is tailored to the study population and research context.

Background: Digital systems are central to regulating access to care in Brazil's Unified Health System (SUS), but fragmented data can hinder coordination. Objective: This study examines the database structure of SUSfacilMG and identifies modernization priorities, using CORE Saude MG as a comparative reference. Methods: We assessed metadata from 1,586 SUSfacilMG tables, focusing on naming patterns, relationships, and repeated attributes. We then compared architectural features described for CORE Saude MG. Results: Approximately 40% of the assessed tables repeated descriptive attributes, including patient identifiers, suggesting substantial duplication. Metadata alone cannot establish operational failures, security incidents, or effects on care. Conclusion: A phased review of data models, interfaces, and governance is warranted. Standardized exchange, referential integrity, and controlled access should be evaluated alongside service continuity and data quality.

Introduction: Heart failure care requires coordination across hospital and community settings, yet information is often fragmented across electronic medical records and clinical systems. Clinical dashboards can bring together and display information to support care delivery and service management; however, existing dashboards have generally focused on specific measures, interventions and monitoring pathways. This study aimed to co-design, iteratively develop and user-test an integrated heart failure dashboard that links patient-level clinical decision-making with service-level management. Methods: A human-centred design approach comprising needs identification, collaborative ideation, iterative prototype development and end-user testing was applied across two complementary operational and clinical dashboard streams. Thirty-six clinicians, health service managers, data and implementation scientists, and consumers from metropolitan and regional services participated. Results: For operational decision-making, participants prioritised real-time visibility of patients across heart failure services, patient trajectories, service-performance information, and identification of variation in guideline-directed care and outcomes. Clinical priorities included rapid synthesis of longitudinal information, optimisation of guideline-directed medical therapy, continuity across care settings, and clinical workload prioritisation. These requirements informed the dashboard prototypes. Many prioritised information elements were incompletely represented in structured data and distributed across disconnected systems and structured and unstructured clinical data sources. Conclusion: Human-centred co-design identified complementary patient- and service-level information needs and translated them into linked dashboard prototypes. The proposed dashboard brings together current clinical status and longitudinal heart failure care history at the patient-level alongside service-level patterns. Implementation is required to evaluate whether these linked views improve care processes and patient outcomes.

Background: Diabetes poses a major global health and economic burden, and effective self management is essential for preventing complications. The National Health Service in England has recently announced plans to shift from analogue to digital services in their 10-year plan. However, digital health technology may also risk widening existing inequalities. MyWay Diabetes is a digital self-management intervention implemented in three regions across England. Understanding the acceptability of online and digital tools to support diabetes self-management, particularly among non users and underserved groups, is essential to ensure equitable engagement and implementation. Objectives: To (1) explore barriers to using online and digital support across diverse demographic groups with type 1 and type 2 diabetes who had not used the MyWay Diabetes platform, and (2) identify factors that could improve uptake and continued engagement. Design: Cross-sectional qualitative study. Methods: We conducted six focus groups with n=35 people living with diabetes (27F, 8M; 40% Asian British Pakistani) across three regions in England. The Theoretical Framework of Acceptability (TFA) informed data collection and analysis. Data were transcribed and analysed thematically using template analysis, mapped onto constructs of the TFA. Results: Digital engagement in diabetes self management was shaped by intersecting personal, social, and healthcare system factors. Participants' confidence with navigating digital environments was constrained by the emotional and practical burden of living with diabetes. Social and structural barriers, including cultural mismatch, accessibility challenges, financial constraints, and reliance on family further limited meaningful use of digital tools. Engagement was strongly influenced by trust in healthcare professionals and services; digital tools were considered acceptable when they complemented person-centred care, but resisted when perceived as replacing personalised, face to face support. Conclusions: Digital support for diabetes self management is more likely to be acceptable when it aligns with people's capabilities, social circumstances, and trusted healthcare relationships. Therefore, to prevent widening inequalities, digital tools should be accessible, culturally relevant, and embedded within supportive, person centred models of care. Lived Experience or Public Contribution: Public contributors with lived experience of diabetes (3F, 3M) reviewed participant materials and pseudonymised focus group transcripts, providing feedback on recruitment, data collection, and interpretation. Their insights informed analysis and theme development, ensuring a lived-experience perspective. Our public co-applicant is a co-author on this manuscript.

Background: Older adults living with mild cognitive impairment (MCI) or Alzheimer's disease and related dementias (AD/ADRD) and their care partners face substantial daily challenges that evolve with disease progression and extend far beyond the clinical setting, including symptom management, care coordination, healthcare navigation, and planning for future care needs. Understanding these challenges is essential for developing innovative solutions to provide ongoing and targeted support. In this study, we explored questions, challenges, and support needs experienced by dementia dyads following diagnosis to inform the development of an artificial intelligence (AI)-based chatbot designed to provide ongoing support for dementia dyads. Methods: We recruited 11 community-dwelling dyads comprising persons living with MCI or mild AD/ADRD and their care partners, one additional care partner, and 12 dementia care experts from academic medical centers, clinics, and aging organizations in the U.S. We conducted semi-structured interviews and analyzed them using conventional content analysis. Results: Participants described interconnected questions, challenges, and support needs that evolved while navigating life with MCI or AD/ADRD. Questions centered around day-to-day care management, medical concerns, behavioral symptom management, diagnosis and prognosis, safety, and resources and services. Challenges included care partner overwhelm, limited dementia knowledge, condition-related uncertainty, healthcare system navigation difficulties, financial constraints, and technology-related barriers. Participants identified needs for ongoing education, personalized care planning, resource navigation, logistical support and follow-up, culturally responsive care, and social and emotional support. Conclusions: Dementia dyads experience substantial informational, emotional, and practical support needs in the early stages of MCI and AD/ADRD. Findings suggest that post-diagnostic support should extend beyond information provision alone to provide ongoing, personalized, and practical guidance that helps dyads navigate changing needs, resources, and everyday dementia care. These findings are informing the development of an AI-based chatbot intended to complement existing dementia care by extending access to education, practical guidance, and resource navigation between clinical encounters.

Background: Electronic health records (EHRs) are central to NHS clinical practice, and the value of the data they hold depends on clinicians entering structured data. Structured data entry (SDE) is nonetheless used inconsistently, and most evidence comes from US settings documenting barriers rather than the mechanisms interventions must target. This is, to our knowledge, the first study to apply the Theory of Planned Behaviour (TPB) to SDE, to examine SDE behaviour among UK NHS doctors, and to apply corpus linguistics to clinicians' free-text accounts of EHR use. Methods: A two-part mixed-methods study was conducted at a large NHS teaching hospital in central London using the Epic EHR. Study 1 was a cross-sectional survey of non-consultant grade doctors comprising the System Usability Scale (SUS), a purpose-designed TPB questionnaire, and two open-text questions analysed using corpus linguistics. Study 2 was an Interpretative Phenomenological Analysis (IPA) of semi-structured interviews with six resident doctors. Results: Eighty-three doctors responded; 65 completed the SUS and 49 the TPB questionnaire. Mean SUS score was 62.1 (95% CI 58.3-65.9), below the benchmark of 68 and equivalent to a grade of "D". Structured features were used markedly less often than narrative notes. TPB constructs explained 56% of the variance in intention (F(3,45) = 18.95, p < .001); attitudes (p = .003) and subjective norms (p = .009) predicted intention independently, whereas perceived behavioural control did not (p = .14). Intention predicted self-reported use of problem lists, event-specific templates and standardised documents, but not narrative notes. Corpus analysis identified time pressure and interface friction as dominant barriers. IPA generated four themes: structured documentation as cognitive relief; as fragile collective infrastructure; as a practice clinicians are expected to perform but not enabled to learn; and as a format that can flatten clinical reasoning. Conclusions: Engagement with SDE is driven principally by attitudes and social norms rather than by perceived control, and the barriers clinicians describe arise from the intersection of interface design, training, team norms and institutional expectation. Interventions addressing usability alone are unlikely to succeed: sustained change requires team-level normative reinforcement, specialty-relevant training that explains both how and why, and systems that preserve space for clinical narrative. Keywords: Electronic health records; structured data entry; Theory of Planned Behaviour; System Usability Scale; Interpretative Phenomenological Analysis; corpus linguistics; clinical documentation; data quality; NHS

The explosive growth of ambient AI scribes in healthcare has focused almost exclusively on relieving clinician documentation burdens and enhancing clinician efficiency and reducing burnout. While most research has focused on the clinician experience, these same types of tools, such as a patient held AI Personal Health Assistant, can be put in the hands of patients to facilitate communication with healthcare providers, to provide greater autonomy and activation for patients in managing their own healthcare, and to facilitate the task of receiving and retaining complex new information. However, to ensure these tools serve as more than just administrative aids, we partnered with a large technology company to conceptually codesign how such a tool might be studied in real world settings in a way that reflected outcomes of greatest importance to patients and families. We used the Nominal Group Technique and engaged thirty three patients and caregivers and ten multidisciplinary clinicians in structured virtual sessions to identify the outcomes on which subsequent studies should hinge. Our findings reveal a significant misalignment between current industry trends and patient priorities. While the market emphasizes the scribe function for billing, stakeholders overwhelmingly prioritized the AIPHAs role in longitudinal care management and post visit coordination. The most critical outcome identified was the impact on communication and respect, suggesting that patients view AI as a mechanism to improve the human to human dynamic and ensure a shared understanding of the care plan. Our findings further highlighted that the primary hurdle for the use of patient held AI is not technical utility but clinician trust and the need for formal consent processes. Importantly, as this technology is new and integration into the clinical experience is still being explored, patients cannot assume they will have access to the transcript of their own encounter when that record is created and held by the health system. This gap highlights the potential value of a patient held tool, preserving discussions from their visit and follow up steps in clearer detail than they may receive in their clinical note or traditional visit summary. From a system perspective, this transition requires a strategic shift from measuring workflow focused efficiency to measuring patient side advocacy and engagement. By centering the evaluation of AI on these patient defined metrics and fundamentally designing subsequent AI investigations using a partnership model, health systems can move beyond simple automation toward a model of care delivery that empowers patients to navigate complex clinical journeys with greater autonomy and clarity.

Background.Respect for autonomy is central to biomedical ethics but is rarely assessed with a structured, reproducible measure. This gap matters as patients, families, and clinicians increasingly use general-purpose language models for health-related questions. We developed the Autonomy Index (AIx) to characterize autonomous agency in clinical-ethics vignettes and compared ratings from trained human reviewers with ratings from current language models. Methods.We reviewed ten decisional-capacity instruments and classified them by target construct into consent capacity, decision capacity, and functional autonomy, identifying a region of autonomous agency (values, deliberation, and enactment) that existing instruments measure least well. The resulting thirteen-item instrument scores four domains: Values Awareness, Factual Understanding, Rational Deliberation, and Intentional Action, on a five-point ordinal scale with an explicit not-applicable option; the External Constraint Index and Support Provided Index are reported separately. Nine models scored 89 clinical vignettes. We selected 50 vignettes by model-score tier and inter-model disagreement, then obtained 173 ratings from 30 clinical and bioethics-informed reviewers and 3,431 valid scores from eight models in July 2026. The prespecified comparison summarized each source within vignette and estimated the paired human-minus-model difference; mixed-effects, domain, agreement, and variance-component analyses were secondary or exploratory. Human data were collected under Harvard Longwood Campus IRB protocol IRB26-0146. Results.Across the selected 50-vignette comparison set, human and model composites were strongly associated (Pearson r = 0.781, P = 2.3 x 10-{superscript 1}{superscript 1}; Spearman {rho} = 0.775). The human mean was 50.2 and the model mean was 39.2, a paired human-minus-model difference of 11.0 index points (95% CI, 6.3 to 15.8; P = 2.6 x 10-; d_z = 0.66). Human means exceeded model means on 36 of 50 vignettes. Bland-Altman limits of agreement were wide (-21.9 to 43.9), indicating substantial case-level variation. Mean differences were positive in all four domains: Factual Understanding, 13.7; Intentional Action, 12.3; Values Awareness, 10.4; and Rational Deliberation, 7.9 points; the joint domain-by-source test did not detect heterogeneity (Wald {chi}{superscript 2}[3] = 2.27, P = 0.52). Vignette content accounted for 80.9% of score variance, whereas source accounted for 0.7%. Models never selected not applicable; human reviewers did so for 22.5% of item responses. Among 19 vignettes with at least four human ratings, 9 had human-minus-model differences greater than 15 points and none had a difference below -15 points. Conclusions.In this deliberately selected 50-vignette set, trained reviewers assigned higher autonomy scores than the tested general-purpose language models on average, while the two sources ranked cases similarly. Wide limits of agreement and variation across well-rated vignettes preclude treating the mean difference as a prediction for an individual case. Because the comparison set was selected using prior model scores, reviewers were a convenience sample, and human ratings are a reference standard rather than ground truth, the findings should be interpreted as evidence of systematic divergence in this sample, not proof that models underestimate patient capacity. External validation in prospectively sampled cases with denser human rating is required before clinical use.

Awareness of comorbidities in people with dementia is vital context for dementia research and clinical management. Natural Language Processing (NLP) applied to free text in the electronic health record (EHR) may help ascertain comorbidity presence. Our aim was to assess the validity of the MedCAT Comorbidity NLP app in a cohort of people with dementia. We used text from the EHR of South London and Maudsley NHS Mental Health Trust via Clinical Records Interactive Search (CRIS), linked to structured data from local primary care (Lambeth DataNet) and hospital admission data in a cohort of 3,543 people with dementia. We evaluated MedCAT Comorbidity, examining internal validity via manual review of text and external validity by cross-validation against linked primary care and hospital data; and comparison of the predictive value of comorbidity measures for mortality. Internal precision was highest for atrial fibrillation (100%) and lowest for diabetes (78%). Cross-validation with linked data varied substantially across conditions, with precision from 96% to 59% and recall 77% to 4%. The median number of comorbidities identified was two using any single source, or three when combined. MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. In people with dementia, the MedCAT Comorbidity app showed high precision for most conditions during internal validation. The variability of precision and recall on external validity assessment partly reflects incomplete documentation. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.

Background: Passively collected smartphone and wearable data, combined with genomic information, may enable scalable early identification of youth who are at risk for substance use disorder. However, heterogeneous data collected in naturalistic settings limit researcher control over data quality, requiring substantial preprocessing. Data cleaning and feature engineering decisions can meaningfully influence analytic outcomes yet are often minimally described or omitted in published work. Objectives: We provide rationales and recommendations for common preprocessing challenges in passive sensing research, including establishing inclusion criteria when "outliers" may reflect true behavioral variability, analyzing differences across smartphone operating systems, handling observations collected outside protocol windows, and engineering features for machine learning applications. Methods: We illustrate principled approaches to these challenges using smartphone and Fitbit data from the Adolescent Brain Cognitive Development Study (ABCD). The sample was drawn from the available half of the year-4 follow-up (data release 5.1; N = 4,754; ages 13-14). Results: Extreme outlying observations were concentrated among low-engagement participants with few observed days. Smartphone usage among Apple users appeared under-recorded relative to Android users when compared with self-report measures. Fitbit-derived behavior differed systematically between observations collected within versus outside protocol windows. We also identified potential errors in the study's default preprocessing pipeline. Conclusion: We recommend prioritizing person-level inclusion criteria that preserve meaningful behavioral variability, examining temporal distributions rather than assuming observations are exchangeable, and thoroughly investigating and reporting data quality issues. Although illustrated using the ABCD Study, these principles broadly apply to research using smartphones, wearables, and other digital data sources.

BackgroundPatient education is critical to safe and effective postoperative bariatric surgery care. Large language models (LLMs) may improve access to personalized patient education but evidence regarding direct patient use in clinical settings remains limited. We evaluated BariBot, an LLM-powered educational application for patients undergoing bariatric surgery. MethodsWe conducted a prospective, single-arm mixed-methods feasibility study at a tertiary academic medical center from May 2024 to May 2025. Adult patients undergoing bariatric surgery used BariBot independently on postoperative day 1. The application provided conversational postoperative education and adapted language complexity to participants educational attainment. Outcomes included feasibility, usability, acceptability, perceived trustworthiness, and anticipated future use. Usability was assessed using the System Usability Scale (SUS). Immediately after BariBot use, participants completed a semi-structured qualitative interview assessing acceptability, perceived trustworthiness, and anticipated future use. Interview transcripts underwent thematic analysis. ResultsSixteen participants completed the BariBot use session, SUS assessment, and post-intervention interview. No technical failures prevented application use, and no participant requested early discontinuation. The median SUS score was 91.3 (IQR, 81.3-98.1), consistent with excellent perceived usability. Qualitative analysis showed that participants valued the applications immediacy, structured responses, conversational context, ability to address follow-up questions, judgement-free nature, and future availability between clinical encounters. Trust was strengthened by alignment with prior clinical guidance and perceived institutional association, although participants emphasized the need for transparency, clinician oversight, and use as an adjunct rather than a replacement of clinician guidance. ConclusionsBariBot was feasible to implement and was associated with high perceived usability and acceptability postoperatively after bariatric surgery. Larger, longitudinal, and comparative studies are needed to evaluate safety during unsupervised use and determine whether LLM-based educational applications improve patient understanding, engagement, and clinical outcomes.

Background: Clinical decision support systems (CDSS) are increasingly embedded in national telemedicine services, however how different stakeholders who use them experience these tools, particularly in low- and middle-income countries, is less explored. In the assisted model of India's eSanjeevani service, spoke health workers enter structured symptoms and hub clinicians review a symptom summary and an artificial intelligence (AI)-generated differential diagnosis. Objective: To describe role-specific experiences of eSanjeevani's CDSS functions and to estimate how acceptance coexists with specific reported difficulties within each role. Methods: We analyzed a program feedback web survey completed between 11 July and 29 July, 2025 by hub clinicians and spoke providers. Prespecified item profiles covered review of summaries and suggested diagnoses, the perceived relation of suggestions to the clinician's own diagnosis, ease of use and intention to use (hub), and symptom-capture utility, question quantity, question relevance, summary presentation and satisfaction (spoke). Within-role contrasts were reported as absolute differences in acceptance with Newcombe 95% CIs. Short open comments underwent content analysis. Results: The cohort comprised 190 hub and 662 spoke respondents from 13 and 15 states (21.4% and 19.3% of eligible providers). Among hub respondents, 76.8% regularly reviewed the symptom summary and 58.9% the suggested diagnoses; 72.6% judged suggestions matched (32.6%) or appropriate but different (40.0%), 7.9% mostly inappropriate, and 11.1% reported that suggestions were mostly not generated. Overall, 85.3% intended to keep using the tools. Affirmative intention was 98.4% when suggestions matched, 66.7% when they were mostly inappropriate (difference vs matched or appropriate -23.2 percentage points, 95% CI -48.5 to -4.0) and 61.9% when mostly absent (-25.7, 95% CI -47.1 to -7.3); technical issues, reported by 38.4%, made little difference (-2.8, 95% CI -14.1 to 7.3). Among spoke respondents, 63.4% rated question quantity appropriate, with miscalibration in both directions (15.6% too many; 16.6% too few); 89.9% found the questions relevant and 91.8% found the summary clear. Satisfaction was 75.3% and did not differ materially between those reporting too many questions and those reporting an appropriate number (-2.7, 95% CI -12.8 to 6.0). Comments located spoke difficulties in question burden and missing complaints, and hub difficulties in freezing and in the scope and availability of suggestions. Estimates were stable across cohort, ambiguity and state-exclusion sensitivity analyses. Conclusions: Acceptance of eSanjeevani's CDSS was high in both roles. For hub clinicians it tracked the perceived quality and availability of AI suggestions rather than technical friction; spoke workers accepted the question burden, but its two-directional miscalibration points toward complaint-specific, adaptive questioning. These provider reports do not measure diagnostic accuracy. They identify log-based and audit-based indicators that the next evaluation cycle should measure directly.

Background: Estimating nutrient intake from meal photographs with vision-language models (VLMs) is now practical, but patient photographs often cannot leave the institution. We developed PLANS (Personal-device Local Assessment of Nutrients System), in which a small open VLM runs entirely on a personal device. Methods: Open VLMs were compared on two image sets with reference values (NutriImage, 887 Japanese meals; SNAPMe, 1,134 US meals), and the selected model (Qwen3-VL-4B, 4-bit) was run on an iPad Air without network access. Energy (primary outcome), protein, fat, carbohydrate and salt were evaluated by median absolute percentage error (MedAPE) and Spearman rank correlation (rho). The effect of supplying the food weight, including errors in it, was examined. Results: Energy estimates on the iPad were identical to those of a desktop-computer run for 84-86% of images (other values, 75-87%). MedAPE on the iPad was 37.0% and 36.5% for energy (26-28% of images within +/-20%), 43-65% for the macronutrients and 68-101% for salt (rho 0.51-0.85). On the desktop computer, supplying the true weight reduced MedAPE of the 4B model on NutriImage from 37.0% to 32.5% for energy and from 100.0% to 66.8% for salt; energy MedAPE stayed below the base value for declared weights of 0.5-1.25 times the true weight or rounded to 50 g, but rose markedly when the container weight was included. Conclusions: An offline VLM on a tablet reproduced server-side energy accuracy and is a candidate tool for dietary counselling where photographs cannot leave the institution; given the wide limits of agreement, its usefulness in practice remains to be examined.

Background: The first thousand days are a sensitive window for child health and social health inequalities. When routine care falls short, parents of young children turn to online sources and, recently, to generative artificial intelligence (AI). Evidence on their concerns, child-health literacy, and AI use is scarce. Objective: To describe health informational needs and concerns, child-health literacy, and use of AI for child-health questions, including differences by parental role and parity, among parents of young children. Methods: In this cross-sectional study, two anonymous self-administered online questionnaires were disseminated through a digital health application between May 12 and June 11, 2026, to French-speaking parents of at least one child aged 0 to 47 months in France. Questionnaire 1 covered unmet information needs, concerns across 16 topics, perceived intra-couple concordance, and confidence in routine care. Questionnaire 2 covered a four-item child-health literacy quiz and AI use (frequency, topics, 12-month trend, barriers, trust conditions, perceived effect on consultations, and disclosure). Results: In total, 12,634 parents responded (Questionnaire 1, n=5,410; Questionnaire 2, n=7,224), mostly mothers (94.6% and 92.9%). Most (61.8%) had searched for information without finding a clear, reliable answer, selecting a median of 4 (Interquartile range [IQR] 3-7) over 16 topics. Leading concerns were sleep (51.3%), nutrition (48.1%), and education (i.e., upbringing and behavior) (47.8%). Mothers were nearly twice as likely as fathers to select accident prevention (26.5% vs 16.3%) and parental mental health (38.7% vs 28.6%); first-time parents selected most topics more often than experienced parents, most markedly dental hygiene (43.8% vs 27.6%). The median child-health literacy score was 3 (IQR 3-4) on a 0-to-4 quiz, with 19.9% of parents scoring 2 or below; tooth-brushing age was the weakest item (68.4% correct). Mothers outscored fathers on every item. More than half of parents (54.2%) had used AI for a child-health question, 11.2% weekly; among those reporting a trend, 68.3% had increased AI use for child-health questions over the year and 6.2% decreased it. The main barriers to AI use were a preference for professionals (55.9%) and reliability concerns (48.7%); trust depended most on medical oversight of the tool (76.6%). AI use appeared additive, with 66.8% reporting no change in healthcare visit frequency. One third (35.5%) had told an AI something shared with neither a professional nor their network, rising to 64.1% among regular users. Conclusions: Parental informational needs were widespread and frequently unmet. AI has become a common and fast-rising source for child-health questions, at times a confidential one, used alongside rather than instead of professionals. Child-health literacy was generally high but uneven, with oral health the clearest gap. Strengths include the sample size and early-generative-AI timing; limitations include the cross-sectional design, self-report, digital-recruitment selection bias, and the small proportion of fathers.

Background: System One Model is designed for structured decision-making and can provide probabilistic outputs, but their performance in domain-specific physical therapy tasks has not been established. Objective: To evaluate the performance and potential utility of Jev, a System One Model, for professional knowledge-based selection tasks in physical therapy by comparing it with GPT-4o. Methods: A total of 380 multiple-choice questions from the 2024 and 2025 Korean Physical Therapist Licensing Examinations were independently submitted to Jev and GPT-4o. The outcomes included answer accuracy, subgroup performance, output validity, API response latency, token usage, estimated API cost, and Jev's prediction uncertainty. A retrospective probability-based model-cascading simulation was also performed. Results: Jev correctly answered 282 questions (74.2%), whereas GPT-4o answered 326 (85.8%), a difference of -11.6 percentage points. Jev produced valid structured responses for all questions, whereas GPT-4o produced three invalid responses. Jev's selected-answer probabilities discriminated correct from incorrect answers (AUROC = 0.866), and accuracy reached 99.3% when probabilities were greater than or equal to 0.90. The median API response latency was 0.94 s for Jev and 1.06 s for GPT-4o, with estimated costs of US$0.008 and US$0.161, respectively. At a retrospective cascading threshold of 0.70, accuracy was 86.6% with 175 simulated GPT-4o calls. Conclusion: Jev showed lower overall accuracy than GPT-4o but provided reliable structured outputs, informative prediction probabilities, lower latency, and lower estimated cost. Its probabilistic outputs warrant further prospective evaluation for selective model escalation and decision-support workflows in physical therapy.

Background: Approximately 2.5 billion people require assistive products (APs), yet access remains limited partly because of shortages of trained personnel. eHealth tools may help strengthen workforce capacity and translate evidence into practice. The World Health Organization's Training in Assistive Products (TAP) program is an open-access eHealth tool designed to strengthen AP provision. However, its impact on learner outcomes remains insufficiently evaluated. This study assessed the usability and acceptability of the TAP module and changes in self-efficacy in mobility aid provision. Methods: We conducted a quasi-experimental pre-post study with health science students who completed the WHO TAP "Introduction to Assistive Products" module. We assessed usability using the System Usability Scale, acceptability using the Technology Acceptance Model and Theoretical Framework of Acceptability, and self-efficacy using an adapted version of Bandura's Multidimensional Scales of Perceived Self-Efficacy. We analyzed data using descriptive statistics and paired-samples t-tests. Results: Of 53 participants, 33 completed both assessments. Usability exceeded established benchmarks (76.97 +/- 9.66), indicating a positive user experience. Acceptability findings demonstrated favourable responses across domains, with high ratings for ease of use and intervention coherence, and comparatively lower burden scores. Mean self-efficacy increased across all competencies from 2.32 +/- 0.58 before the module to 4.23 +/- 0.36 afterward (p < 0.001), reflecting substantial gains in confidence in AP provision. Conclusion: The eHealth TAP module demonstrated above-average usability and favourable acceptability, while significantly improving self-efficacy in AP provision. These findings highlight the potential for eHealth training implementation to strengthen workforce capacity and bridge training gaps in global health.

Aim. To evaluate the accuracy, run-to-run consistency, and agreement of large language models on the Ukrainian Integrated Licensing Examination Krok 2 for the Specialty of Physical Therapy. Materials and Methods. Five cloud-based LLMs (ChatGPT-5.5, Claude 5 Sonnet, Gemini 3.5 Flash, Grok 4, DeepSeek-V3) were evaluated using 150 Ukrainian-language multiple-choice questions from the official Krok 2 database. Each model completed two independent runs with identical inputs. Accuracy (with 95% confidence intervals, Wilson method), run-to-run consistency, and agreement were assessed. Statistical analysis included Cohen's {kappa}, Cochran's Q and pairwise McNemar tests with Holm-Bonferroni correction. Results. All models achieved >80% accuracy in both runs, exceeding the passing threshold of [&ge;]64%. In the first run, accuracy ranged from 80.00% (DeepSeek-V3) to 93.33% (Gemini 3.5 Flash); in the second run, from 82.67% (Claude 5 Sonnet) to 88.67% (Gemini 3.5 Flash). In the first run significant differences were observed between Gemini 3.5 Flash and Grok 4 (93.33% vs 84.00%; p=0.0022) and DeepSeek-V3 (93.33% vs 80.00%; p=0.0001). There were no statistically significant differences between models in the second run. R2R consistency exceeded 90% for all models with the highest in ChatGPT-5.5 (98.67%) and lowest in DeepSeek-V3 (90.00%). Cohen's {kappa} ranged from 0.64 to 0.94, indicating substantial to almost perfect agreement. The stability of LLMs' response correctness across two runs ranged from 77.33% (DeepSeek-V3) to 88.00% (Gemini 3.5 Flash). Conclusions. All LLMs demonstrated high performance on Ukrainian-language Krok 2 examination tasks. Gemini 3.5 Flash showed the highest accuracy in both runs and response correctness stability while ChatGPT-5.5 exhibited the greatest response consistency and agreement. Although the accuracy and stability of LLM responses may be interrelated, they represent distinct aspects of model performance. This highlights the need for a comprehensive approach to evaluating LLMs. The findings indicate the potential of LLMs as an auxiliary tool in the professional practice of physical therapy specialists.

Large language models (LLMs) are increasingly consulted for health advice, but they can produce fluent statements that are false or unsupported, known as hallucinations, and this risk is poorly characterised in low-resource languages such as Bangla. We measured hallucinations of locally deployable open-weight LLMs in Bangla diabetes question answering and tested whether retrieval-augmented generation (RAG) grounded in Bangladeshs National Guideline on Diabetes Mellitus reduces them. Forty patient questions spanning six clinical sub-domains were each answered twice, under zero-shot prompting, by Qwen3.5-9B, TigerLLM-9B, Qwen3.6-35B-A3B and Gemma-4-26B-A4B, and by BanglaDiaRAG, which adds hybrid dense (BGE-M3) and sparse (BM25) retrieval from a physician-reviewed Bangla guideline knowledge base to Qwen3.5-9B. Two physicians rated the 400 de-identified responses by consensus for medical accuracy, completeness, safety, readability and reliability on a 0-1 scale, and responses with accuracy below 0.70 were classified as hallucinated. Hallucination rates were 2.5% for Gemma-4-26B-A4B, 3.8% for TigerLLM-9B, 22.5% for Qwen3.6-35B-A3B and 77.5% for Qwen3.5-9B. Guideline grounding lowered the rate of Qwen3.5-9B to 11.3% (difference -66.3 percentage points; 95% CI -76.3 to -55.0) and raised every criterion by 0.24 to 0.38 points (Holm-adjusted p < 0.001). BanglaDiaRAG outscored Qwen3.6-35B-A3B on all five criteria and matched or exceeded TigerLLM-9B, but it remained below Gemma-4-26B-A4B in safety, readability and reliability, and its residual errors were specific, confident and potentially harmful. Guideline grounding can turn a weak general-purpose model into a much safer Bangla diabetes assistant, but the choice of base model mattered as much as retrieval and no configuration was free of hallucination, so clinical oversight remains essential before patient-facing use.

Discourse analysis can reliably predict real-world communicative success, yet is rarely implemented clinically, due to challenges in manually generating stimulus-specific main concept inventories (MCIs) and scoring patient narratives. We evaluated whether automatically derived macrolinguistic (main concept, sentiment) and microlinguistic (fluency, lexical, syntactic) features extracted from movie clip narrations can augment the clinical assessment of discourse in aphasia. The MCIs generated directly from each clip's video, audio and subtitles using a vision-language model approximated the main concepts healthy controls produced. Group classification (control: n=51; aphasia: n=54) based on the discourse features was excellent (AUC=0.95, 95% CI [0.90, 0.99]), with macrolinguistic features showing the largest group differences (main concept completeness: d=-1.84, 95% CI [-2.30, -1.38]) and strongest associations with aphasia severity (semantic distance to inventory: r=-0.79, 95% CI [-0.87, -0.66]). Findings support the feasibility and scalability of automated criterion-based discourse assessment using ecological stimuli, and highlight its potential for clinical implementation.