DIGITAL HEALTH

Background: Mobile phone-based tools are increasingly used to collect data on non-communicable disease (NCD) risk factors, particularly in low-resource settings where traditional data collection systems face operational and infrastructural constraints. This study examined stakeholder perspectives on the use of enhanced mobile phone-based capabilities to support the collection of public health surveillance data on NCD risk factors in low-resource settings. Methods: An exploratory qualitative study was conducted between November 2022 and July 2023. Twenty in-depth interviews were conducted with public health specialists, ethicists, NCD researchers, health informaticians, and policy makers in Uganda. Thematic analysis was used to interpret the results. Results: Four themes emerged from the data, including benefits of using mobile phone capabilities for NCD risk factor data collection; ethical, legal, and social implications; perceived challenges of using such mobile phone capabilities; and proposed solutions to improve the utility of phone-based capabilities in data collection on NCD risk factors. Participants recognized the potential of mobile technologies to improve data collection efficiency and expand access to hard-to-reach populations. However, concerns emerged regarding inadequate informed consent, risks to privacy and confidentiality, unclear data ownership, and vulnerabilities created by inconsistent enforcement of data protection laws. Social concerns included low digital literacy, unequal access to mobile devices, and fear of stigmatization. Participants emphasized the need for transparent communication, robust data governance, and community engagement. Conclusion: Mobile phone-based systems can strengthen the collection of NCD risk factor data in low-resource settings; however, their benefits depend on addressing key ethical, legal, and social challenges. To ensure responsible deployment, digital health initiatives must prioritize participant autonomy, data protection, equity, and trust building. Integrating contextualized ethical, legal, and social considerations into design and policy frameworks will be essential to leveraging mobile technologies in ways that support inclusive and effective NCD prevention and control.

BackgroundCaring for people with dementia can impose a considerable psychological burden on caregivers, yet access to caregiver support in Malaysia remains limited. The World Health Organizations iSupport for Dementia program provides dementia education via textual, e-learning format. However, a culturally adapted Malaysian version has not been available. ObjectiveThis study aimed to develop and gather user feedback on a culturally adapted, multimedia version of iSupport tailored for Malaysia (iSupport-Malaysia). MethodsGuided by a four-phase cultural adaptation framework, the generic iSupport content was translated into Bahasa Malaysia, adapted to local customs, and transformed into multimedia lessons on an e-learning platform. A mixed-methods design was used to explore user perceptions and evaluate usability through four homogeneous focus group discussions and 15 individual usability test sessions with informal caregivers (FG: n=9; UT: n=9) and healthcare professionals (FG: n=11; UT: n=6). Focus groups examined aesthetics, ease of use, clarity, cultural relevance, comprehensiveness, and satisfaction. Usability testing involved Think Aloud tasks, post-test questionnaires, and brief interviews. Qualitative data was analysed thematically, and descriptive statistics summarised usability performance. ResultsiSupport-Malaysia demonstrated good usability (M=74.3{+/-}18.0), with most tasks completed without assistance. Strengths included interactive learning activities, peer discussion features, and flexible self-paced learning. Content was viewed as culturally appropriate, credible, and useful. Suggested improvements included enhancing visual aesthetics, shortening videos, refining quizzes, and increasing practical relevance. ConclusionUser insights indicate that iSupport-Malaysia is usable and culturally appropriate. These findings will inform refinement of the platform prior to the pilot feasibility study and provide recommendations for future multimedia-based caregiver interventions.

BackgroundRecruitment remains a major barrier to timely clinical trial completion. Trialshub is an LLM-powered, chat-based platform intended to help users identify relevant trials and connect with coordinators to streamline recruitment workflows. ObjectiveTo evaluate the perceived usability and operational value of Trialshub, and identify implementation considerations for real-world deployment. MethodsA usability test was conducted at Morehouse School of Medicine for the Trialshub application. Purposively selected participants included clinical research coordinators and individuals with and without clinical trial search experience. Participants completed a pre-test survey assessing demographics, digital health information behaviors, and familiarity with AI tools, followed by a moderated usability session using a Trialshub prototype. Users completed scenario-based tasks (locating a breast cancer trial, reviewing results, and initiating coordinator contact) using a think-aloud protocol. Task ratings, screen recordings, and transcribed feedback were analyzed descriptively and thematically, and reported. ResultsParticipants reported high comfort with using digital tools and moderate-to-high familiarity with AI. Trialshubs chat-first design, guided prompts, and checklist-style eligibility display were perceived as intuitive and reduced cognitive load. Fast access to trials and the coordinator-contact workflow were viewed positively. Key usability issues included uncertainty at step transitions, insufficient cues for selecting results and next actions, and inconsistent system reliability (loading delays, errors, and broken trial detail pages). Participants also noted redundant questioning due to limited conversational memory, requested improved filtering/sorting, and clearer calls-to-action. All participants indicated that Trialshub has strong potential to meaningfully improve clinical trial processes. ConclusionsTrialshub shows promise for improving trial discovery and recruitment workflows, with identified design implications for real-world deployment.

Background: Safety is a critical concern in behavioral health crisis units (BHCUs), where environmental risks (e.g., ligature points) can lead to injury to self or others. However, limited research has examined how perceived safety influences facility selection among patients and care partners, or how these perceptions align with AI-driven safety risk assessments in such environments. Method: To address these gaps, a nationwide discrete choice online survey was conducted using image-based scenarios of BHCU environments, where participants selected preferred facilities based on a range of attributes, including environmental safety risks (e.g., ligature points). Additionally, participants identified safety risks in survey images, which were compared with outputs from an AI-driven tool developed and trained to detect environmental risks by experts. Quantitative analysis using conditional logit models examined the influence of attributes on facility choice, while spatial comparisons of annotated images and heatmaps assessed participant and AI-identified risk alignments. Results: Findings revealed that the higher frequency of safety risks in images significantly reduced the likelihood of facility selection (p < .001, OR {approx} 1.28), highlighting the importance of perceived safety in user decision-making. While there was notable alignment between heatmaps generated by participants and AI, key differences emerged, suggesting that participant safety perception was influenced by features not fully captured by AI, such as the type of materials or unknown, out-of-label safety risks in facility images. Conclusions: Despite these limitations, results highlighted the value of integrating AI-driven assistive tools for non-expert user safety risk assessment to support decision-making for safer BHCU environments.

Background: LLMs enable patient-facing conversational agents, creating a pathway toward digital twins that capture older adults' lived experiences and behavioral responses across time. A central barrier is personality drift---inconsistent trait expression across repeated interactions---which undermines reliability of generated trajectories and intervention-response simulation in geriatric care. Objective: To develop ELDER-SIM, a multi-role elderly-care conversational platform for building personality-stable digital twin agents, and to propose a psychometric validation framework for quantifying personality consistency in LLM-based agents. Methods: ELDER-SIM was implemented via n8n workflow orchestration with local LLM inference (Ollama/vLLM), integrating (1) Big Five (OCEAN) trait specifications, (2) a Cognitive Conceptualization Diagram (CCD) grounded in Beck's CBT framework, and (3) a MySQL-based long-term memory module. Ablation studies across four conditions---Baseline, +Memory, +CCD, and +LoRA (fine-tuned on 19,717 instruction pairs from CHARLS)---were evaluated via Cronbach's $\alpha$, ICC, and role discrimination accuracy. Results: Personality measurement reliability was acceptable to excellent across conditions (Cronbach's : 0.70-0.94), with consistently high test-retest stability (ICC: 0.85- 2 0.96). Role discrimination improved stepwise from 83.3% (Baseline) to 88.9% (+Memory), 94.4% (+CCD), and 97.2% (+LoRA). CCD produced the largest gain in internal consistency (mean 0.702[-&gt;]0.892), while LoRA achieved the highest overall internal consistency ( 0.940) and ICC (0.958). Conclusions: ELDER-SIM provides a psychometrically validated approach for constructing personality-consistent elderly digital twin agents. Structured cognitive modeling and domain adaptation reduce personality drift, supporting reliable longitudinal simulation for elderly mental health care and reproducible in silico evaluation before clinical deployment.

BackgroundDigital health plays an indispensable role in facilitating data analysis and use for enhancing healthcare delivery across health settings. However, there is scant information on the extent to which digital health influences the improvement of primary health services delivery through data use. This study examined the determinants that influence the use of digital health to improve health service delivery in council hospitals in Tanzania. MethodsA cross-sectional design was employed in six regions, involving 12 council hospitals. We used a self-administered questionnaire to collect data from 203 members of hospital quality improvement teams. Descriptive analysis was used to determine the frequency, proportion, and mean of responses, while bootstrapping analysis was conducted to test the statistically significant influence of digital health factors on data use for improving health service delivery. ResultsResults show moderate agreement on data compatibility for planning and decision-making, with 40.4% of respondents agreeing it supports ordering commodities, 43.8% for staff allocation, and 38.4% for planning. However, dissatisfaction was higher for user-friendliness (47.8%), reliability (up to 65.5%), and usefulness (up to 63.5%). Overall, 50.2% (M=2.74{+/-}0.87) disagreed that digital systems effectively support data use. Structural model analysis confirmed significant positive influence of usefulness ({beta}=0.199, p<0.001) and access to quality data ({beta}=0.729, p<0.001) on data use, which strongly impacted service delivery ({beta}=0.593, p<0.001), despite some factors showing no direct influence. ConclusionThe study finds that current digital health initiatives only modestly improve the user-friendliness, reliability, and usefulness of data systems, partly due to fragmented, non-interoperable platforms that burden data management. However, compatibility, usability, reliability, and usefulness of digital tools significantly enhance access to quality data and data-driven decisions. The study recommends strengthening and integrating existing systems and providing continuous digital health training to institutionalize data-informed decision-making.

Global healthcare is targeting patient-centred care, as it leads to better health outcomes and higher level of patient satisfaction. Patient-centred communication, is an important part of patient-centred care because it focuses on involving patients in their care. Recent surveys both nationally and globally have shown that patients are not involved enough in their own healthcare decisions. This problem is especially common among the elderly with chronic conditions. This study aimed to describe patient-healthcare professional interactions, expectations, and satisfaction in physiotherapy within an understudied context, thereby providing important, specific data on ICE dynamics and satisfaction in the specific setting. Cross-sectional study of participants in scheduled consultations was conducted. Two government physiotherapy centres, seven private physiotherapy centres and two trust centres with physiotherapy facilities in Gujarat, India. 232 patients (from various public and private physiotherapy clinics) participated in the study. Patients' ideas, concerns, expectations (ICE) and satisfaction were explored. Almost 88% of patients reported their thoughts and explanations about their symptoms during the consultation. Most patients described not having any concerns about the diagnosis/treatment, and more than two-third of patients consulting PTs expected explanation for their symptoms. Almost 90% patients were satisfied with the consultation. The study revealed that while most patients conveyed their thoughts during consultations, very few expressed their concerns. Overall, patients were satisfied with their consultations.

Background: Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods: A search strategy in Ovid MEDLINE identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on socio-demographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analysed using descriptive statistics and thematic content analysis. Results: The survey received 716 responses. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (57.02%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Discussion: By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, and guide future policies and collaboration among researchers.

Abstract Background People with disabilities use technology, like search engines, to seek health information online. This health information includes information on coronavirus disease, or COVID-19. COVID-19 remains a public health concern. Research shows that people with disabilities encounter frustrations, or "pain points," when seeking online information, but little is known about these specific pain points and who encounters them. Objective The goals of this study are to determine pain points for people with disabilities who seek health information online, and to assess how pain points impact the experience of technology use and information seeking. Methods Ten participants recruited from a prior quantitative survey completed the concurrent think-aloud study over a month-long period. Participants completed four online search tasks and narrated their experiences in real-time while doing so. Transcripts were stored in Taguette; thematic analysis was performed on these transcripts. Findings Participants were predominantly white, with three identifying as Asian. All ten participants reported having disabilities. Participants with attention deficit hyperactivity disorder (ADHD) reported distracting webpage layout, whereas participants with physical disabilities reported physical fatigue while navigating online information. All participants encountered AI-generated information; only one participant indicated trust in the AI-generated information. Other common sources of information included hospital and governmental webpages, peer-reviewed articles, and news and advertising results. News and advertising results were especially common with respect to search results for "COVID-19 vaccine." Themes identified included the following: accessibility/usability, AI-generated information, government/hospital and related sources of information, peer-reviewed articles, news and advertising, and sentiment and trust. Conclusions Information can be fatiguing, distracting, or otherwise difficult to navigate for people with diverse disabilities searching for COVID-19 related information online. Further work should incorporate user feedback from people with disabilities when designing online content.

Background: Clinical documentation and information retrieval consume over half of physicians working hours, contributing to cognitive overload and burnout. While artificial intelligence offers a potential solution, concerns over hallucinations and source reliability have limited adoption at the point of care. Objective: To evaluate clinician-reported time savings, decision-making support, and satisfaction with DR. INFO, an agentic AI clinical assistant, in routine clinical practice. Methods: In this prospective, single-arm pilot study, 29 clinicians across multiple specialties in Portuguese healthcare institutions used DR. INFO v1.0 over five working days within a two-week period. Outcomes were assessed via daily Likert-scale evaluations and a final Net Promoter Score. Non-parametric methods were used throughout. Results: Clinicians reported high perceived time saving (mean 4.27/5; 95% CI: 3.97-4.57) and decision support (4.16/5; 95% CI: 3.86-4.45), with ratings stable across all study days and no evidence of attrition bias. The NPS was 81.2, with no detractors. Conclusions: Clinicians across specialties and career stages reported sustained satisfaction with DR. INFO for both time efficiency and clinical decision support. Validation in larger, controlled studies with objective outcome measures is warranted. Keywords: Medical AI assistant, LLMs in healthcare, Agentic AI, Clinical decision support, Point of care AI

A Wearable Monitoring System (WMS), comprising a chest patch, wrist-worn pulse oximeter, and arm-worn blood pressure device, was developed in preparation for a pilot Randomised Controlled Trial (RCT) on a UK surgical ward. The system was designed to support continuous physiological monitoring and early detection of deterioration. An initial prototype user interface was developed by the research team based on prior clinical experience and engineering knowledge. To ensure suitability for clinical practice, iterative user-centred refinement was undertaken through a series of clinician focus groups and wearability assessments. Six focus groups were conducted between November 2019 and May 2021 involving multidisciplinary healthcare professionals. Feedback from these sessions informed successive interface and system modifications. System development spanned the COVID-19 pandemic, during which the WMS was rapidly adapted and deployed to support clinical care on isolation wards. Feedback obtained during this period was incorporated into later versions of the system and provided a unique opportunity to examine changes in clinician priorities under pandemic conditions. Clinicians consistently prioritised alert visibility, alarm fatigue mitigation, parameter flexibility, and centralised monitoring. Notably, preferences regarding alert modality and access mechanisms evolved over time: early enthusiasm for mobile or smartphone-type devices shifted towards a preference for fixed, ward-based displays and audible alerts at the nurses station following pandemic deployment. Building on previous wearability testing in healthy volunteers, wearability testing using a validated questionnaire was completed by 169 patient participants during the RCT. The chest patch and pulse oximeter demonstrated high tolerability, whereas the blood pressure cuff showed poor wearability and was removed from the final system. These findings demonstrate the importance of iterative, clinician-led design for wearable WMS and highlight how extreme clinical contexts such as the COVID-19 pandemic can significantly reshape perceived requirements for safety-critical monitoring technologies.

Background: Health systems in low- and middle-income countries are frequently described as "data rich, information poor", collecting substantial amounts of data that rarely inform local decision-making. In tribal settings, this challenge is compounded by geographic isolation, fragmented governance, sectoral silos, and the absence of disaggregated tribal health data within routine health information systems. We conducted a systematic mapping of data sources available for maternal and child health (MCH) decision-making at tribal Primary Health Centres (PHCs) in Andhra Pradesh, India. Methods: Using a participatory data discovery approach embedded within an action research project, we mapped data sources across three PHCs under the Integrated Tribal Development Agency (ITDA) - Rampachodavaram, Alluri Sitarama Raju District of Andhra Pradesh, India. Data discovery proceeded through three phases: document review, key informant interviews with Medical Officers and frontline health workers, and stakeholder validation. Sources were classified using the HEALTHY framework (Healthcare, Education, Access, Labour, Transportation, Housing, Income) and the Keller's data discovery typology (Designed, Administrative, Opportunity, Procedural). Accessibility was assessed based on whether Medical Officers could retrieve data for local planning and decision-making. Results: We identified 28 distinct data sources relevant to MCH decision-making. Healthcare dominated (57.1%), while determinant domains remained underrepresented: Housing (10.7%), Income (10.7%), Education (7.1%), Labour (7.1%), Transportation (3.6%), and Access to healthy choices (3.6%). By data origin, Administrative sources predominated (46.4%), followed by Opportunity (21.4%), Procedural (17.9%), and Designed (14.3%). Despite 67.9% of sources having digital components, only 32.1% were fully accessible to Medical Officers, with 10.7% partially accessible and 57.1% inaccessible at the PHC level. Accessibility barriers were consistent across data categories, ranging from 50.0% to 66.7% inaccessibility. Conclusions: The tribal PHC data ecosystem exhibits a fundamental mismatch between data generation and local utility. Data is predominantly collected for administrative reporting rather than local decision-making. Addressing MCH outcomes in tribal populations requires reorienting health information systems toward local needs.

Background Comprehensive sexuality education (CSE) is essential to the health and well-being of young people. In the Democratic Republic of Congo (DRC), where more than 65% of the population is under the age of 25, access to interpersonal CSE remains limited owing to sociocultural and structural barriers. This exposes young people to persistent socio-sanitary vulnerabilities. In this context, mobile health apps (MHAs) constitute a promising solution, supported by the growing use of smartphones among young Congolese. However, this group's intention to use MHAs for CSE has been the subject of little research to date. Objective The aim of this study was to identify predictors of intention to use MHAs among young Congolese, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). Methods A predictive correlational study was conducted in eight public secondary schools in Bukavu (DRC) with a stratified random sample of 859 students. Predictors of intention to use--performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR)--and moderators--age, gender, and past MHA experience--were measured from data collected through a self-administered UTAUT questionnaire. Descriptive and multivariate analyses were run on SPSS version 28. Results Mean age of participants was 16.3 years (SD = 1.5). Boys made up 55.1% of the sample. Overall, 51.0% of the sample owned a smartphone, of which 62.3% reported having easy access to mobile data and 16.2% were already using MHAs to learn about sexual health. Intention to use MHAs was positively influenced by PE ({beta} = 0.523, p < 0.001), EE ({beta} = 0.115, p < 0.001), and SI ({beta} = 0.113, p < 0.001). FC (p = 0.260) and PR (p = 0.631), however, had no significant influence. Age moderated all of the relationships tested (F (1, 849-854) = 9.97-20.82; p [&le;] 0.002), with more marked effects observed among younger participants 14-15 years old. The final model explained 44% of the variance, indicating good predictive power. Conclusion Intention to use digital CSE was explained primarily by PE, EE, and SI and moderated by age. To strengthen this intention, stakeholders will need to promote e-interventions that are pertinent, easy to use, socially valorized, and tailored to young people's needs and to the local context.

Background: Nursing education in conflict-affected settings faces significant disruptions that compromise the preparation of a competent and resilient workforce. In regions such as Iraq, prolonged instability, resource constraints, and fragmented health systems challenge traditional educational models, necessitating innovative and context-responsive approaches to ensure continuity, quality, and equity in nursing training. Purpose: This study aimed to explore innovative strategies in nursing education within conflict-affected settings and to examine their implications for leadership development, health policy reform, and the advancement of health equity. Methods: A cross-sectional descriptive study was conducted among undergraduate nursing students across selected universities in the Nineveh Governorate, Iraq, during the 2025-2026 academic year. Data were collected using a structured, self-administered questionnaire designed to assess students educational experiences, engagement with digital learning approaches, perceived barriers, and attitudes toward innovation in nursing education. The instrument captured multiple dimensions of the learning environment, including access to educational resources, institutional support, and exposure to blended and technology-enhanced learning. Descriptive and inferential statistical analyses were performed using SPSS (version 28), including frequency distributions, chi-square tests, and binary logistic regression modeling to identify key predictors of positive educational outcomes, such as engagement, satisfaction, and perceived clinical readiness. Results: The findings indicate that, although students demonstrated a high level of motivation to engage with innovative learning approaches, notable gaps remained in access to digital resources, faculty preparedness, and institutional support. A majority of participants reported engagement with blended and technology-enhanced learning, which was significantly associated with higher levels of engagement, improved critical thinking, and greater perceived clinical readiness (p < 0.001). Multivariable analysis identified institutional support, digital learning access, and learner-centered teaching strategies as significant predictors of positive educational outcomes. Students with access to digital learning resources and supportive educational environments were more likely to report higher levels of satisfaction and competence. Conclusions: Innovating nursing education in conflict-affected settings is essential to building a resilient and future-ready nursing workforce. Integrating digital technologies, flexible learning models, and competency-based approaches can enhance educational outcomes despite contextual constraints. Implications for Nursing Practice and Policy: Strategic investment in nursing education infrastructure, faculty development, and digital transformation is critical to strengthening health systems in fragile contexts. Policymakers and academic leaders must prioritize inclusive, scalable, and sustainable educational reforms to promote health equity and empower nurses as key agents of system-level change.

Background: The urgent care departments in Europe face a structural paradox: accelerating digitalisation is accompanied by a patient population that is disproportionately unable to engage with standard digital tools. An internal analysis at the Emergency Department (Akutafdelingen) of Nordsjaellands Hospital in Hilleroed, Denmark found that 43% of emergency patients struggle with digital solutions - a figure that reflects the predictable composition of acute care populations rather than any individual failing. Objective: This paper presents the design, iterative development, and secondary validation of the ED Adaptive Interface (v5): a prototype adaptive patient terminal developed in response to this challenge. The system operationalises what the author terms impairment-first design - a methodology that treats the most constrained patient experience as the primary design problem and derives the standard experience as a subset. The interface configures itself in under ten seconds via nurse-led setup, adapting across four axes of impairment: visual, motor, speech, and cognitive. System: Version 4 supports five accessibility modes, a heatmap pain assessment grid, a Privacy and Dignity panel, a live workflow tracker with care notifications, structured dual-category help requests, and plain-language medical term definitions across four languages. Version 5, reported here for the first time, introduces a Condition Worsening Escalation button, a Referral Pathway Display, a "Why Am I Waiting?" triage explainer, a Symptom Progression Log, MinSP/Yellow Card Scan simulation, expanded language support (seven languages: English, Danish, Arabic with full RTL layout, Turkish, Romanian, Polish, and Somali), and an expanded ten-item Communication Board. The entire system runs as a single 79-kilobyte HTML file with zero infrastructure requirements. Methods: To base the design on patient-generated evidence, two independent social media threads were subjected to an inductive thematic analysis (Braun and Clarke, 2006): a primary corpus of 83 entries in the Facebook group Foreigners in Denmark (collected March 2026) and a corroborating corpus in an international community group in the Aarhus region (collected April 2026). All identifiers in both datasets were fully anonymised under GDPR Article 89 research provisions prior to analysis. No participants were contacted. Generative AI tools were used to assist with drafting, writing, and prototype code development; all scientific content, data collection, analysis, and conclusions are the sole responsibility of the authors. Results: The first discourse corpus produced five major themes corresponding to the five problem areas the prototype was designed to address: system navigation and triage literacy gaps (31 entries); language and cultural barriers (6 entries); communication failures during care (5 entries); staff overload and capacity constraints (8 entries); and pain and severity assessment failures (14 entries). The corroborating dataset supported all five themes and introduced two additional themes: differential treatment of international patients and medical gaslighting as a long-term pattern of patient advocacy failure. One structural finding - the five most-liked comments incorrectly criticised the original poster for self-referring when she had received explicit 1813 telephone triage approval - directly inspired the Referral Pathway Display and "Why Am I Waiting?" features in v5. Conclusions: The convergence of design rationale and independent social evidence across all five problem categories suggests that impairment-first design is not a niche accessibility concern but a structural approach to healthcare interface quality. The prototype is ready for a structured clinical pilot using the System Usability Scale (SUS) and semi-structured staff interviews. The long-term roadmap includes full MinSP integration, hospital PMS connectivity, and clinical validation.

Objective To map the presence, public availability, and content of clinical trial data sharing policies (DSP), data management and sharing plans (DMSP), and data use agreements (DUA) among the most prolific public and private clinical trial sponsors operating in the European Union, and to identify key areas of convergence, divergence, and constraint in the context of General Data Protection Regulation (GDPR). Eligibility criteria We included organisation-level documents describing approaches to clinical trial data sharing or data management from the top 20 public and top 20 private sponsors ranked by the number of trials registered in the EU Clinical Trials Information System (CTIS). Eligible materials comprised publicly available or sponsor-shared policies, guidelines, statements, templates, and agreements relevant to clinical trial data sharing or management. Sources of evidence Evidence was identified through systematic searches of sponsors' public websites, structured Google searches, and major data management plan platforms (DMPTool, DMPonline, DMP Assistant), complemented by direct contact with sponsors to verify findings and request missing documentation. All sources were archived and catalogued. Charting methods Two reviewers independently extracted data using a structured form, capturing the existence, accessibility, and content of data sharing policies, data management and sharing plans, and data use agreements. Quantitative data were summarised descriptively, and a non-interpretive descriptive content analysis was conducted to characterise recurring policy elements and areas of heterogeneity. Results Among 40 sponsors, private sponsors were substantially more likely than public sponsors to make trial-specific data sharing policies and data use agreements publicly accessible, often via established data sharing platforms. Public sponsors more frequently referenced data management and sharing plans, but these were heterogeneous in scope and often embedded within broader institutional governance documents rather than tailored to clinical trials. Across sectors, GDPR compliance, data protection, and legal safeguards were emphasised, while operational aspects such as dataset readiness, review criteria, and downstream responsibilities varied widely. Overall response rate to sponsor verification was 37.5%. Conclusion Clinical trial data sharing governance in the EU shows a marked sectoral imbalance among the top sponsors. Private sponsors tend to provide more detailed and operationally explicit documentation, whereas public sponsors often articulate high-level commitments without trial-specific guidance. Greater clarity and standardisation, particularly among public sponsors, could improve transparency and facilitate responsible data reuse, while remaining compatible with GDPR requirements.

Background HIV remains a major public health challenge in South Africa, with gaps in early diagnosis and linkage to care driving onward transmission. Adolescent girls and young women face barriers to timely care, including stigma, privacy concerns, and limited clinic access, while healthcare providers work in resource-constrained settings with high client volumes. We evaluated the Self-Care from Anywhere (SCFA) toolkit, an AI-enabled intervention comprising an AI Companion for AGYW and a provider-facing Clinical Portal to support HIV prevention, testing, and linkage to care. The AI Companion is designed to complement and extend human-delivered services, particularly in resource constrained settings, rather than replace in-person counselling. Methods We conducted an exploratory study to assess the usability, feasibility, and acceptability of the SCFA toolkit in Gauteng Province (November 2024-May 2025). AGYW engaged with the AI Companion, and a subset completed a simulated HIV self-testing activity with AI-delivered counselling. Pre and post-intervention surveys, including the System Usability Scale (SUS), were administered. Usability testing of the Clinical Portal involved healthcare providers using the toolkit without formal training to capture first impressions. A subset of AGYW and healthcare providers participated in separate focus group discussions or in-depth interviews. Quantitative data were analysed using descriptive statistics, and qualitative data were analysed thematically. Results A total of 97 AGYW were enrolled; 75.3% had completed high school and 91.8% were unemployed or full time students. Most participants (85.6%) self-reported HIV-negative status, and 63.9% reported sexual activity in the past 12 months. The AI Companion demonstrated high usability (mean SUS 87.7, SD 12.7) and was perceived as acceptable and useful, particularly for its personalisation and confidentiality features. Healthcare providers had a mean age of 34 years (SD 6.5), with about half serving as HIV testing and screening counsellors. Most providers rated the Clinical Portal ease of use, comprehension, and client support as positive to very positive, though 23% expressed concerns regarding workflow efficiency and their ability to manage additional client volume. Providers also highlighted the Clinical Portal value for case management. Conclusion AI-powered digital health tools, such as the SCFA toolkit, show potential to enhance user engagement and support care delivery, with high usability and acceptability demonstrated among AGYW and healthcare providers. Continued user-centred refinement is essential to ensure these tools remain responsive to the evolving needs and care contexts of diverse user groups.

Background Continuous Quality Improvement (CQI) is a core strategy for strengthening health systems, yet documentation and monitoring of CQI activities remain fragmented in many low- and middle-income country (LMIC) settings. In Jamaica, CQI has been institutionalized across priority programs, but it largely relies on paper-based tools and basic digital platforms that limit timely learning and oversight. To address these gaps, Jamaicas Ministry of Health and Wellness (MOHW), in collaboration with the Caribbean Training and Education Centre for Health (C-TECH), adapted a web-based CQI application using a participatory, human-centered design approach. Methods We conducted a formative, convergent mixed-methods evaluation across 24 healthcare facilities to assess early-stage implementation of the CQI app. Guided by the Implementation Outcomes Framework, we examined acceptability, adoption, appropriateness, and feasibility. Quantitative data were collected through a structured survey of healthcare workers (n=43), and qualitative data were gathered through five focus group discussions (n=33) and three key informant interviews with CQI leads. Survey data were summarized descriptively, and qualitative data were analyzed using rapid qualitative analysis. Findings were integrated using joint displays. Results Survey findings indicated moderate to high perceived acceptability and appropriateness of the CQI app, with 70% of participants reporting that it saved time and 67% noting that it aligned with facility goals. However, 19% reported never using it. Qualitative findings highlighted the apps value for improving CQI documentation, visualizing trends, and supporting supervisory oversight. Key barriers to sustained adoption included inconsistent internet connectivity, limited follow-up training, unclear team roles, and challenges integrating app use into routine workflows. Leadership engagement and alignment with existing CQI structures emerged as critical enablers. Conclusion This formative evaluation suggests that a digitally enabled CQI platform can strengthen documentation and oversight of quality improvement activities in resource-constrained health systems when embedded within supportive organizational and infrastructural contexts. Addressing foundational system readiness, including leadership engagement, capacity-building, and workflow integration, will be essential to realizing the CQI apps potential in Jamaica and similar LMIC settings.

Background: The integration of artificial intelligence (AI) into clinical practice holds transformative potential for healthcare in West Africa, but safe deployment requires context-appropriate governance, accountability, and post-deployment monitoring frameworks. This cross-sectional mixed-methods study examined preferences and concerns of West African clinicians and technical experts regarding AI governance structures, post-deployment surveillance mechanisms, and accountability allocation. Methods: A structured questionnaire was administered to 136 physicians affiliated with the West African College of Physicians (February 22-28, 2026), complemented by 72 key informant interviews with technical leads, AI developers, data scientists, policymakers, and healthcare leaders. Data were analyzed using descriptive statistics, inferential tests, and thematic analysis. Results: Clinicians strongly preferred independent regulatory bodies (40.4%) for overseeing AI tool performance, with high trust ratings (mean:4.3/5), while vendor self-monitoring received minimal support (3.7%, mean:2.4/5). Real-time dashboards were the most favored monitoring approach (41.9%). Clear accountability pathways (94.1%), algorithm transparency (91.9%), and real-time performance data (89.7%) were rated essential by majorities. Major concerns included clinicians being unfairly blamed for AI errors (76.5%), excessive vendor control (72.8%), and absence of clear reporting pathways (69.9%). Qualitative findings emphasized continuous performance tracking for accuracy, fairness, and bias; structured incident reporting; protocols for model drift and failure; and multi-layered governance combining independent oversight, institutional AI committees, and explicit liability frameworks. Conclusion: This study provides the first empirical evidence from West Africa on clinician preferences for AI governance. Findings offer actionable guidance for policymakers to build trustworthy, equitable, and safe AI integration frameworks that prioritize transparency, independent oversight, and clinician protection. Keywords: artificial intelligence; AI governance; post-deployment monitoring; accountability; West Africa; clinician preferences; health data science.

Background: Artificial intelligence chatbots (AICs), as a form of generative artificial intelligence (AI), are increasingly being considered for use in scholarly peer review to assist with tasks such as identifying methodological issues, verifying references, and improving language clarity. Despite these potential benefits, concerns remain regarding their reliability, ethical implications, and transparency. Evidence on how medical journal peer reviewers perceive the role and impact of AICs is limited. This study explored reviewers' familiarity with AICs, perceived benefits and challenges, ethical concerns, and anticipated future roles in peer review. Methods: We conducted a cross-sectional online survey of medical journal peer reviewers. Corresponding author information was extracted from MEDLINE-indexed articles added to PubMed within a two-month period using an R-based approach. A total of 72,851 authors were invited via email to participate; those who self-identified as peer reviewers were eligible. The 29-item survey assessed familiarity with AICs and perceptions of their benefits and limitations in peer review. The survey was administered via SurveyMonkey from April 28 to June 16, 2025, with two reminder emails sent during the data collection period. Results: A total of 1,260 respondents completed the survey. Most participants were familiar with AICs (86.2%) and had used tools such as ChatGPT for general purposes (87.7%), but the majority had not used AICs for peer review (70.3%). Most respondents reported that their institutions do not provide training on AIC use in peer review (69.5%), although many expressed interest in such training (60.7%). Perceptions of AIC benefits were mixed, while concerns were widely shared, particularly regarding potential algorithmic bias (80.3%) and issues related to trust and user acceptance (73.3%). Conclusions: While familiarity with AICs is high among medical journal peer reviewers, their use in peer review remains limited. There is clear interest in training and guidance, however, concerns related to ethics, data privacy, and research integrity persist and should be addressed before broader implementation.