DIGITAL HEALTH

Introduction: Diabetic macular edema (DME) management requires integration of ophthalmic and systemic data, yet clinical decision-making remains limited by fragmented information systems and insufficient interoperability. This study aims to design a user-centered, interoperable dashboard prototype to support integrated clinical decision-making in DME care. Methods: The prototype was developed using an agile, user-centered approach and implemented within a SMART-on-FHIR-compatible layered architecture. FHIR-based data standardization, including ophthalmology-specific profiling, enabled interoperable integration and visualization of structured clinical and imaging data within a modular web-based dashboard prototype. Results: The resulting prototype provides a patient-centered dashboard integrating ophthalmic imaging findings and systemic risk factors, such as long-term glycemic markers, inflammatory markers, and blood pressure, within a unified interface. Built on standardized FHIR resources and SMART-on-FHIR authorization, the system demonstrates how user-centered dashboards can be implemented within interoperable, vendor-neutral healthcare IT infrastructures. Conclusion: The presented design case illustrates how user-centered interface design and standards-based interoperability can jointly support the translation of clinical decision support tools into routine care environments. Establishing such interoperable infrastructures represents a key prerequisite for the sustainable deployment of future model-based clinical decision support systems.

Background Digital legacy (the digital information available about someone following their death) has increasing societal importance as personal assets and interactions become increasingly digitized. Healthcare professionals often have a limited understanding of how to address digital legacy in practice, and there is a lack of interdisciplinary networks to improve education, research, and professional development in digital legacy. Objective This paper describes the development of an interdisciplinary initiative designed to build research capacity and develop consensus-based recommendations for integrating digital legacy into palliative care. Method Over 12-months, we conducted interdisciplinary engagement activities with diverse stakeholders, including clinicians, designers, and sociologists. We used a modified World Cafe method to facilitate dialogue and capture feedback on how memories are digitally curated, the management of digital estates, and intergenerational perspectives on digital legacy. Results We identified eight core recommendations for research and policy, including promoting digital legacy education, supporting policy development, and broadening the scope of interdisciplinary research. Our discussions highlighted the complexity of modern digital estates and the need for legal and ethical frameworks to protect individual rights. Conclusions The Network demonstrates that interdisciplinary collaboratives can address important issues relating to digital legacy, which provides a foundation to conduct collaborative research that improves the management of digital legacies in society.

With the rapid advancement of artificial intelligence (AI) technologies, growing interest has emerged in its potential application in pediatric healthcare. Understanding the factors influencing AI acceptability among parents and clinical staff is an important prerequisite for facilitating its integration in clinical routine. To date, these factors have not been systematically assessed across different stakeholder groups. Here we investigated AI acceptability among parents (first cohort n = 198; second cohort: n = 79) and pediatric health care professionals (n = 33) across different disease, diagnosis or treatment scenarios. The effects of demographic variables and contextual predictors, such as data privacy, AI knowledge and perceived disease severity, on willingness to use AI were evaluated. More liberal data privacy was associated with reduced willingness to use AI (p = < .001). A higher perceived disease severity was linked to higher willingness to use AI ({beta} = .10, p = .013) in the second parent group. When the recommendations of AI and clinicians conflicted, parents were more likely to choose AI over the judgment of clinicians in treatment compared to diagnosis scenarios. Healthcare professionals showed similar patterns but additionally weighted perceived disease severity when resolving disagreements. These findings highlight the importance of addressing stakeholder concerns regarding disease specific customization, accuracy, data privacy and accountability when introducing AI into pediatric healthcare settings.

Background Stroke remains one of the leading causes of mortality and long-term disability worldwide, with low- and middle-income countries bearing a disproportionate share of the global disease burden. In India, delays in risk identification, fragmented referral pathways, and limited continuity of preventive care present significant challenges, particularly in rural communities. As a frontline health worker Accredited Social Health Activists (ASHAs) are strategically positioned to support community-based stroke prevention; however, existing workflows are frequently constrained by multi-tasking, predominantly paper-based documentation and fragmented digital systems. Advances in mobile health, artificial intelligence along with digital health ecosystem provided by Ayushman Bharat Digital Mission (ABDM) provide an opportunity to strengthen community healthcare through integrated digital platforms. Objective This protocol describes the design, system architecture, and prospective evaluation framework of ASHA Assist India, an integrated AI-assisted mobile health platform intended to support community-based stroke prevention by connecting citizens, ASHA workers, Primary Health Centres (PHCs), and higher levels of healthcare facilities within a unified digital ecosystem. Methods ASHA Assist India has been designed as a modular, cloud-based digital health platform supporting standardized data collection, longitudinal health monitoring, referral management, and AI-assisted clinical decision support. The proposed system comprises four user-facing applications corresponding to citizens, ASHA workers, PHCs, and referral hospitals, integrated through a centralized backend providing authentication, secure data management, interoperability, analytics, and notification services. The AI framework includes three planned analytical modules: (i) population-level stroke risk stratification, (ii) longitudinal stroke risk prediction, and (iii) acute stroke symptom recognition. A prospective implementation study is planned to evaluate platform usability, feasibility, workflow integration, implementation outcomes, and operational performance within routine community healthcare settings. Future validation of the AI modules will be conducted using prospectively collected longitudinal datasets. Expected Impact The proposed platform aims to strengthen community-based stroke prevention by improving digital workflow integration, facilitating coordinated referral pathways, and supporting longitudinal monitoring through the existing healthcare providers at health and wellness centres like ASHA, Community Health Officers (CHOs), ANM, etc. Beyond stroke prevention, the modular architecture is intended to provide a scalable framework for future digital health programmes addressing multiple non-communicable diseases within primary healthcare systems. Publication of this protocol establishes a transparent implementation and evaluation framework that may guide future research, digital health innovation, and implementation science in resource-constrained settings.

Background: The disconnect between episodic in-clinic assessments and patients goals to improve activity in daily life presents a challenge in rehabilitation. Wearable sensors are promising for addressing this disconnect but are not part of routine practice. The purpose of this study was to develop and test a digital health solution that integrated wearable sensor technology into outpatient rehabilitation. Methods: Using an iterative user-centered design approach, a digital solution was built that integrated data from a wrist sensor into the electronic health record (EHR). The feasibility, usability, and resources required of the solution were evaluated in a 9-month proof-of-concept study in outpatient rehabilitation. Results: 17 clinicians and 38 patients (median monitoring duration: 42 days) participated. Patient adherence to daily data syncing was a median of 68.6% and was not significantly correlated with digital technology self-efficacy (r = 0.03, p = 0.86). Recent sensor data ([&le;] 3 days old) were available in the EHR for 90.2% (193/214) of clinic visits, and clinicians viewed these data in 81.9% (158/193) of these visits. Overall user-friendliness on the System Usability Scale received a median rating of "good" from both patients and clinicians. Resources included costs to build the data transfer pipeline, a 24-month build timeline, and ongoing technical support. Conclusions: By combining user-centered design principles with dedicated resources, we built a feasible and usable digital health solution to bridge the gap between brief clinic visits and daily life in outpatient rehabilitation. These results and future refinements will facilitate broader-scale implementation of sensor technology in rehabilitation.

Background: Informal caregivers of persons living with Alzheimer's disease or related dementias (ADRD) increasingly seek support in online environments. Prior work suggests that perceived value may shape online support use, yet practical and trust-related barriers may still impede uptake even when beliefs are favorable. Objective: To characterize patterns of online peer support use among informal ADRD caregivers and identify and analyze the predisposing, enabling, and need factors under the Andersen and Newman Framework of Health Services Utilization (ANFHSU) framework that are associated with use and nonuse behaviors. Methods: We conducted a cross-sectional, web-based survey that recruited a national, nonprobability sample of US adults through Qualtrics online panels from July 15 to September 16, 2023. Online peer support use was defined as searching for information or discussing caregiving topics in online communities during the past 3 months. Guided by ANFHSU, we measured predisposing characteristics, enabling resources, and need-related factors, along with additional technology-related preference measures, including willingness to use an experience-search tool and a caregiving-education tool. We summarized patterns in platform ecosystem and engagement modes and fitted 5 nested multivariable logistic regression models to identify factors associated with recent online peer support use. Among non-users, we evaluated multiple thresholds to identify a high-belief subgroup, summarized reported barriers, and modeled intention to use online peer support in the next 3 months. Results: Of 18,245 invited panel participants, 12,072 completed the survey (participation rate, 66.2%). After eligibility screening and data-quality exclusions, 1,113 unpaid informal ADRD caregivers comprised the analytic sample. Overall, 740/1,113 (66.5%) caregivers reported online peer support use in the past 3 months. Use was distributed across a fragmented platform ecosystem, with a long tail of infrequently named sources. More than half of users (402/740, 54.3%) primarily read rather than wrote posts, indicating substantial passive participation. In the fully adjusted model, higher belief in the value of online peer support was associated with greater odds of use (OR 1.04, 95% CI 1.01-1.08), along with higher eHealth literacy (OR 1.06, 95% CI 1.02-1.10), greater willingness to use an experience-search tool (OR 1.68, 95% CI 1.39-2.04), and higher caregiving stress (OR 1.03, 95% CI 1.01-1.05). Among non-users (373/1,113, 33.5%), 108/373 (28.9%) met the retained high-belief threshold despite reporting no recent use, indicating a belief-behavior gap. Among non-users, the most commonly reported barriers were sufficient offline support (118/373, 31.6%), lack of time (102/373, 27.3%), dislike of online communities (79/373, 21.2%), limited opportunity to join despite awareness (74/373, 19.8%), and security or trust concerns (67/373, 18.0%). Among non-users, 161/373 (43.2%) reported intending to use online peer support in the next 3 months. Conclusions: Our findings suggest that uptake of online peer support reflects perceived value, digital readiness, and caregiving-related need rather than need alone. A meaningful belief-behavior gap indicates that favorable beliefs do not ensure use when time, trust, and access barriers persist. These findings suggest that efforts to support ADRD caregivers should focus on reducing practical and trust-related barriers to online peer support and should recognize that engagement often occurs through reading rather than posting.

With the growing use of artificial intelligence (AI) tools in clinical care, it is essential to assess ethical issues related to using these tools, both before and after deployment. To address this need, a multidisciplinary team of clinical and AI experts developed and tested a scoring rubric to evaluate AI tools in clinical settings from an ethical perspective, considering potential issues that may arise during their use. The rubric provides a replicable scoring system comprising five content areas and four scoring categories. The content areas include evaluation population, accessibility, clinical bias, user control, and monitoring and updates. To test the applicability of the rubric, we applied it to three AI tools designed for clinical decision-making, clinical documentation, and clinical image analysis. The evaluation relied solely on publicly available information to simulate typical clinical settings. The rubric addresses a critical gap in the evaluation and governance of clinical AI tools by providing a structured framework to assess their ethical aspects. This approach enables healthcare systems to address and monitor the ethical impact of AI tools, fostering greater trust among clinicians and patients and guiding improvements in clinical practice.

Introduction: Computerized cognitive training (CCT) is a promising and innovative solution to improve the quality of life for those experiencing age-related cognitive decline. The comprehension of instructions for CCT plays a crucial role in determining technology engagement. This study delves into the relationship between the presentation modes of CCT serious games instructions, their comprehension, and the resulting acceptability among older adults (aged over 65) without any known cognitive impairments. Methodology: In a within-subjects experimental design, two types of CCT instructions were submitted to 128 older participants (mean age 71.5, 70% female): without visual cues and with visual cues. This approach was complemented by a study of the influence of self-efficacy and technology-related anxiety on the acceptability of the games. Results: Instructions without salient visual cues were more acceptable for a complex functional game. Additionally, individuals with lower confidence in their cognitive abilities were less receptive to cognitive training, except for a highly familiar game. Conclusion: The study highlights that older individuals may prefer simpler instructions for complex functional games, suggesting a preference for reduced cognitive load. It also shows the subtle role of self-efficacy in technology acceptance, except for the most familiar games, with higher cognitive self-confidence linked to greater acceptability. It emphasizes the importance of metacognition and self-efficacy in engagement when CCT involves mobilizing cognitive resources. It points the need for simple and personalized instructions to improve acceptance of CCT, and to contribute to the development of tailor-made interventions for older people.

Background: The uptake of new digital and AI based technologies in the health care setting, including digital diagnostics, has been comparatively slower than in other sectors. This is a consequence of the complex legislature, lack of routine procedures for the secondary use of sensitive medical data. Inadequate level of education and incentives for different stakeholders prevents overcoming strong barriers, such as clinical workflow integration, and need for comprehensive eval uation of utility of interventions which requires multi-disciplinary collaboration effort. The paper presents a Re-hospitalization Challenge, organised by the European Digital Innovation Hub (EDIH)- AI4Health.Cro, a co-creation activity and an effective tool for overcoming the mentioned barriers for implementation of new AI technologies and overall innovation ecosystem build-up. Methods: Crowdsourcing challenges or hackathons are R&D strategies in which ad-hoc assembled competing teams collaborate on early-stage technology devel opment or research problem solving. Re-hospitalisation challenge, in our case, has had multiple facets: as a co-creation activity tasked with construction of an AI predictive model with the explanatory support and plausible business model for its deployment; it involved also extensive organisers effort: data anonymisation, provision of secure processing environment, and comprehensive assessment of solutions. The paper details the challenge structure and processes, from problem inception to execution and comprehensive evaluation of developed solutions. Results: As a co-creation effort in digital healthcare, the challenge provided results on multiple levels: (i) through tailored solutions based on real evi dence data, serving as benchmarks or prototypes for later deployment in practice; (ii) through sandboxing EHDS principles to enable secondary data use; (iii) as an ecosystem building event yielding ad-hoc collaborative teams and cross-disciplinary participative learning experience. Conclusions: Crowdsourcing innovation challenges are valuable digital health care ecosystem building tool that provides a number of direct and indirect 2 impacts, from providing valuable diagnostic prototypes for innovation elicitation, creation and transformation of startups, to streamlining secondary use of clinical data for research and innovation. Overall impact is also enhanced through raising the awareness and education of all stakeholders relevant for the translation of AI technologies and into routine clinical setting.

Digital mental health tools are increasingly promoted as scalable supports for young people, yet implementation remains inconsistent, particularly for marginalised youth. Acceptability and usability are key determinants of successful adoption, but little is known about how these factors shape engagement across diverse youth populations. The aim of the study was to examine the acceptability, usability, and implementation potential of 11 evidence?based digital mental health tools among marginalised young people across the Republic of Ireland (ROI) and Northern Ireland (NI). A mixed?methods design integrated baseline surveys (n = 38), a two?week trial of digital tools delivered through a co?designed Google Site, online workshops/individual interviews (n = 22), and a final usability and engagement survey (n = 24). Usability was assessed using the System Usability Scale (SUS), engagement using the Twente Engagement with E?Health Technologies Scale (TWEETS), and mental wellbeing using the Short Warwick-Edinburgh Mental Well?Being Scale (SWEMWBS). Qualitative data were analysed thematically and mapped to the Consolidated Framework for Implementation Research (CFIR). Only two tools exceeded the SUS usability benchmark. Engagement was moderate overall, with one tool achieving the highest engagement despite lower usability. SWEMWBS scores indicated moderate baseline mental wellbeing. Thematic analysis identified five acceptability themes: credibility and trust; accessibility and ease of use; positive content supporting emotional regulation; personalisation and self?monitoring; and engagement and habit formation. CFIR analysis highlighted usability, institutional trust, cultural relevance, and emotional needs as core implementation determinants. Digital literacy was high and supported engagement, and usability remained a critical gateway to implementation. Designers and commissioners of digital mental health tools should ensure that supports are simple, trustworthy, culturally relevant, and youth?centred to enable adoption among marginalised young people. Implementation strategies are needed that will co?design with diverse youth communities and prioritise youth work settings as well as governance clarity.

Objectives. To assess how digital inclusion factors and physical access barriers are associated with user trust in smartphone-based remote photoplethysmography (rPPG) hypertension screening, and to identify implications for digital health pol- icy, procurement and implementation in low-resource settings. Methods. Cross-sectional mixed-methods survey in five outpatient clinics in Kebbi State, northern Nigeria (N =287). Trust was measured using comfort, confidence and perceived usefulness Likert scales. Primary analyses used binary logistic models with HC3 robust standard errors; sensitivity analyses are reported in supplementary material. Free-text responses were thematically analysed. Results. Smartphone ownership was 51.2%; Transsion-brand devices comprised 56.5% of owners. Greater distance to a blood pressure facility was independently associated with lower perceived usefulness (OR 0.51, 95% CI 0.30-0.87; p=0.013) and lower comfort (OR 0.61, 0.37-0.98; p=0.042). Among owners, Transsion versus Samsung showed higher confidence odds (OR 3.82, 1.02-14.27; p=0.046). Qualitative themes supported the implementation interpretation: platform-fit and device speed requests among Transsion owners; connectivity and offline-first concerns among those with greater travel distance. No brand contrast achieved FDR-adjusted significance; brand findings are exploratory. Conclusions. Digital health policy and health technology assessment for smartphone-based screening should incorporate local device ecology, connectivity constraints, physical access burden and trust-calibration safeguards. Pre-implementation assessment of these factors is necessary for equitable and safe rPPG adoption in low-resource health systems.

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.

Patients with a brain tumour receive evidence-based clinical care in Australia but a focus on supportive care, including social connection, is often deficient. Digital health platforms hold promise to support these patients and their carers. Existing platforms often lack end-user co-design, evidence-based development and rigorous evaluation. Recognising this unmet need, we co-designed Brain Tumours Online, a digital supportive care platform to streamline access to educational resources, symptom management tools, and peer support for patients, carers, and healthcare professionals. In this article, we present our evaluation approach for Brain Tumours Online to advance methodological thinking in the evaluation of multi-faceted, co-designed digital health platforms. In contrast to standardised procedures in clinical trials, digital health interventions such as supportive care platforms are more complex due to their interactive nature, no prescriptive protocols for usage and the dynamic content of web-based information. Thus, traditional evaluation approaches often fall short in evaluating such multi-faceted digital health supportive care platforms. To address these challenges, we developed a bespoke, logic-modelling based evaluation approach to assess the usability, engagement, impact, and economic value of our platform. Our pragmatic but rigourous evaluation approach required the adaptation of existing evaluation frameworks, subject-matter, and lived experience expert knowledge. Our implementation science and co-design approach are shared in different papers. Our study outcomes will also be shared in a separate paper. In the current paper, we share our approach to the evaluation of Brain Tumours Online and provide insights that may be of value for other researchers interested in the nuances of trialing multi-faceted digital health supportive care platforms.

Documentation burden remains a persistent challenge in nursing practice, and Ambient artificial intelligence (Ambient AI) may offer new opportunities to mitigate the burden and improve nursing practice. This brief scientific communication reports survey findings on the current status of Ambient AI implementation in U.S. inpatient nursing settings and nurses' perceptions of its anticipated benefits and concerns. We conducted a cross-sectional online survey of practicing inpatient nurses recruited from nursing-focused social media communities. Descriptive analyses were used to summarize Ambient adoption, user experience, and perceived benefits and concerns. Among 61 eligible respondents, nearly 60% (n = 37) reported that Ambient AI had been piloted or implemented in nursing at their healthcare institutions, and approximately half (49.2 %, n = 30) reported having direct experience using Ambient AI in clinical practice. Most nurses with direct experience (73.3%, n = 22) described positive experience using the technology in practice. The frequently identified perceived benefits of Ambient AI include reduced documentation time and improved patient care workflow, and improved data quality. However, nurses also raised concerns about patients' acceptance of the new technology in their care, potential nursing job displacement, and loss of nursing skills due to overreliance on AI technology. Findings suggest that Ambient AI may help reduce nursing documentation burden and workload, yet implementation strategies should address nurses' concerns about its influence on nursing professions, foster trust among clinicians and patients, and support integration into everyday nursing practice.

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.

Background: Digital health technologies, spanning mobile applications, telemedicine, and AI-driven platforms, are rapidly reshaping healthcare delivery globally. Although Generation Z university students are classified as digital natives, empirical data evaluating their eHealth literacy, technology acceptance, and specific trust barriers in developing South Asian nations like Sri Lanka remain scarce. Objective: This study evaluated eHealth literacy, technology acceptance, online health information-seeking behaviors, and adoption barriers among Gen Z undergraduates in Sri Lanka, focusing on the interplay between eHealth literacy, AI trust, and digital care preferences. Methods: A cross-sectional survey (N = 172) was conducted among Sri Lankan university undergraduates utilizing adapted, validated instruments: the eHealth Literacy Scale (eHEALS) and the Technology Acceptance Model (TAM). Statistical analysis included scale reliability validation (Cronbach's alpha), descriptive profiling, Chi-Square ({chi}{superscript 2}) contingency tests, Pearson correlations, and Multiple Linear OLS Regression models. Results: Participants demonstrated high overall eHealth literacy (Mean = 3.84 {+/-} 0.58) and strong endorsement of digital health utility (Mean = 3.99 {+/-} 0.59). Online health searches were reported by 86.6% of respondents. AI tools (e.g., ChatGPT, Gemini) emerged as the second most frequent source for health queries (57.6%), surpassing YouTube (44.2%) and social media (26.2%), with medical students showing significantly higher AI utilization ({chi}{superscript 2} = 8.70, p = .003). In multiple regression analysis, digital platform preference over physical clinic visits (R{superscript 2} = .352, p < .001) was significantly predicted by Perceived Ease of Use ({beta} = 0.371, p = .001) and Trust in AI Recommendations ({beta} = 0.370, p < .001), whereas face-to-face consultation preference (76.7%) and personal data privacy risks (50.0%) remained predominant adoption barriers. Conclusion: Gen Z students in Sri Lanka exhibit high digital health readiness and substantial reliance on AI-driven information seeking. However, institutional deployment must address privacy concerns and integrate hybrid clinical workflows to bridge the gap between high perceived utility and physical consultation preferences.

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.