Frontiers in digital health最新文献

{"title":"Artificial intelligence is transforming disease surveillance, but governance is failing to keep pace.","authors":"Abdullahi Osman Mohamed, Abdifatah Afyare, Abdinasir Mohamed Abdi, Yusuf Hassan Muhumed, Mohamed Adam Abdihashi","doi":"10.3389/fdgth.2026.1939009","DOIUrl":"10.3389/fdgth.2026.1939009","url":null,"abstract":"<p><p>Artificial intelligence (AI) is transforming disease surveillance by enabling early outbreak detection, predictive modeling, and real-time analysis of diverse health data sources. These advances can strengthen epidemic preparedness and improve public health decision-making. However, AI adoption has progressed faster than the development of effective governance frameworks, creating challenges related to algorithmic bias, transparency, privacy, cybersecurity, accountability, and global equity. Low- and middle-income countries face additional barriers due to limited digital infrastructure and technical capacity. This policy brief highlights the urgent need for responsible AI governance through international standards, explainable algorithms, independent validation, strong data protection, and inclusive stakeholder engagement. Ensuring ethical and equitable AI implementation is essential to maximize its potential for improving disease surveillance and global health security.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1939009"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing referrals in primary care through specialist-supported telehealth: quasi-experimental evidence from Northeast Brazil.","authors":"Frederica Valle de Queiroz Padilha, Francisco Antônio Sousa de Araújo, Jorge Norio Rezende Ikawa, Maria Eulália Vinadé Chagas, Natalia Luiza Kops, Fernanda Saks Hahne, Vivian Oliveira Balan","doi":"10.3389/fdgth.2026.1890968","DOIUrl":"10.3389/fdgth.2026.1890968","url":null,"abstract":"<p><strong>Background: </strong>Synchronous tele-interconsultations - real-time interactions linking primary care physicians and hospital-based specialists - have been proposed as a way to support clinical decision-making and, potentially, to build clinical competencies over time through repeated exposure to specialist reasoning. However, causal evidence on whether such programs reduce reliance on specialist referrals remains limited, particularly in low- and middle-income country settings characterized by large territories and uneven specialist distribution.</p><p><strong>Methods: </strong>We conducted a quasi-experimental evaluation of the TeleNordeste project, a large-scale telehealth initiative implemented in Northeast Brazilian states under a public-private partnership. Using staggered difference-in-differences methods, our primary aim was to estimate the treatment effect on primary care referral rates, using a panel of primary care units observed from 2018 through 2025. After applying a municipality-level restriction to reduce spillover contamination, the analytical sample comprised 6,825 primary care units: 3,918 never-treated comparison units, 1,002 low-dose units (1-4 cumulative telehealth interactions), and 1,905 high-dose units (≥5 interactions). Secondary analyses explored whether estimated effects were more consistent with immediate care-flow reorganization or workplace learning.</p><p><strong>Results: </strong>Any exposure to the program was associated with a statistically significant reduction in referral rates (-0.356 percentage points, no-covariate specification; <i>p</i> < 0.001). Similar estimates were observed across low- and high-exposure groups, with no evidence that effects increased with cumulative exposure.</p><p><strong>Conclusions: </strong>Participation in the TeleNordeste program was associated with meaningful reductions in referrals to specialized care. Although immediate case resolution during tele-interconsultations provides a plausible explanation for this association, workplace learning cannot be confirmed or excluded with the available data. In the absence of a clear dose-response gradient, these mechanisms cannot be disentangled, and the results indicate that program participation itself can strengthen primary care case resolution and reduce reliance on specialist referrals. Interpretation of mechanisms is limited by potential under-registration of referrals and by the use of primary care units, rather than individual physicians, as the unit of analysis. Future evaluations would benefit from collecting physician-level identifiers and strengthening audits of referral-registration completeness, thereby enabling a more detailed assessment of how tele-interconsultations affect clinical practice and referral decisions over time.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1890968"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and infodemic management: a governance framework for digital public health decision-making.","authors":"Angelo Cianciulli, Emanuela Santoro, Antonietta Pacifico, Roberta Manente, Marika Finizio, Giuseppina Speziga, Mario Capunzo, Giovanni Boccia","doi":"10.3389/fdgth.2026.1830005","DOIUrl":"10.3389/fdgth.2026.1830005","url":null,"abstract":"<p><strong>Background: </strong>Digital communication ecosystems have profoundly transformed the circulation of health information. During public health crises, large volumes of information-including misinformation and disinformation-can spread rapidly across online platforms, generating complex information environments commonly described as infodemics. These dynamics can influence public risk perception, undermine trust in health institutions, and affect adherence to preventive measures. At the same time, advances in artificial intelligence (AI) and digital technologies have created new opportunities for monitoring information flows and identifying emerging misinformation patterns in real time. However, the translation of AI-generated insights into coordinated public health decision-making processes remains limited.</p><p><strong>Methods: </strong>This study developed a conceptual governance framework aimed at explaining how AI-generated insights derived from digital information ecosystems can be translated into evidence-informed public health responses. The framework was developed through a secondary conceptual analysis of the complete evidence extraction database generated from a previously published scoping review including 63 studies. The methodological approach combined thematic synthesis of empirical evidence with conceptual modeling techniques commonly used in public health systems research.</p><p><strong>Results: </strong>The synthesis of the literature identified several thematic domains describing how digital technologies and artificial intelligence are used to monitor and respond to health misinformation within digital communication ecosystems. Building on these domains, this study proposes a governance-oriented conceptual framework integrating digital data ecosystems, artificial intelligence analytics, interpretive public health expertise, and institutional governance mechanisms.</p><p><strong>Conclusions: </strong>Artificial intelligence technologies offer powerful tools for monitoring complex digital information environments, yet their integration into public health decision-making requires governance models capable of linking analytical insights with institutional responses. The proposed framework contributes to the emerging field of digital public health governance by providing a structured conceptual model for translating AI-generated insights into coordinated public health actions during infodemic events. Although the framework remains preliminary and requires formal validation, it provides a structured conceptual basis for future empirical implementation and governance research.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1830005"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13598089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The perceived fit of guided iCBT in specialised mental healthcare services: a qualitative study of healthcare professionals' perspectives.","authors":"Beate Standal, Robin Maria Francisca Kenter, Monika Knudsen Gullslett, Tine Nordgreen, Inger Lise Teig","doi":"10.3389/fdgth.2026.1955151","DOIUrl":"10.3389/fdgth.2026.1955151","url":null,"abstract":"<p><strong>Background: </strong>Therapist-guided internet-delivered cognitive behavioural therapy (guided iCBT) is an effective treatment for common mental disorders and has the potential to improve access to care. However, its integration into routine practice remains limited. This study explored healthcare professionals' perceptions of the suitability of guided iCBT in specialised mental healthcare and examined how perceived fit influences implementation across clinical and organisational contexts.</p><p><strong>Methods: </strong>Semi-structured interviews and participatory observations were conducted at three specialised mental health clinics in Norway that offered guided iCBT to adults with anxiety and depression. Thirty-one healthcare professionals were recruited through purposive and snowball sampling. Data were analysed using a combination of inductive and deductive approaches. The deductive analysis was informed by Lau et al.'s framework of contextual fit, which conceptualises fit as alignment between an intervention and professional, organisational, and external contexts.</p><p><strong>Results: </strong>Healthcare professionals' perceptions of fit varied across contextual levels and influenced the implementation of guided iCBT. At the intervention level, participants identified a perceived mismatch between the evidence base for guided iCBT and limitations in its technological design, which was viewed as insufficiently aligned with contemporary therapeutic practices. At the professional level, differing professional values and therapeutic norms shaped perceptions of appropriateness and acceptability. At the organisational level, limited readiness for implementation and concerns about patient complexity in specialised care settings contributed to doubts about the intervention's suitability. At the external level, tensions emerged between evidence-based expectations and prevailing clinical assumptions favouring face-to-face care, further affecting perceptions of fit and legitimacy.</p><p><strong>Conclusions: </strong>The findings suggest that implementing guided iCBT in specialised mental healthcare depends on aligning the intervention with the clinical, organisational, and broader system contexts in which it is introduced. Efforts to improve technological usability, strengthen organisational readiness, engage clinicians, and address assumptions about legitimate forms of therapy may enhance perceived fit and support sustained implementation of guided iCBT.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1955151"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597905/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Caring over computing: an ethical and sociotechnical perspective on digital technologies for social connectedness in dementia care.","authors":"Teis Arets, Giulia Perugia, Maarten Houben, Wijnand IJsselsteijn","doi":"10.3389/fdgth.2026.1944047","DOIUrl":"10.3389/fdgth.2026.1944047","url":null,"abstract":"<p><p>People with dementia in residential care often experience reduced social connectedness. Person-centered care approaches foreground meaningful social interactions to support well-being, but staff and time pressures increasingly constrain opportunities for such care. Digital technologies offer possibilities to help care professionals facilitate social connectedness, despite increasing care pressures. Yet, the meaningful integration of such technologies into the complex sociotechnical setting of residential dementia care is not straightforward. To responsibly design and position digital technologies for dementia care, this study empirically explores care professionals' perspectives on social connectedness and the sociotechnical role of conventional digital technologies in residential dementia care. We conducted five workshops with 15 care professionals from three care organizations to pursue this objective. Our findings show that fostering social connectedness is a relational process involving continuous interpretation, negotiation, and adaptation to residents' individual needs, personal values, and institutional constraints. Digital technologies were used by care professionals to support communication, interaction, and shared activities, yet remained largely peripheral to everyday care routines and were primarily used reactively in response to observed behavior. Drawing on an ethics of care framework, we derive ethical and sociotechnical implications for the responsible design and positioning of digital technologies in residential dementia care.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1944047"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Governing medical AI under the EU AI Act: an integrated compliance-by-design framework.","authors":"Qaiser Khan, Abdul Raffay Saeed","doi":"10.3389/fdgth.2026.1932256","DOIUrl":"10.3389/fdgth.2026.1932256","url":null,"abstract":"<p><p>Artificial intelligence (AI) is becoming an increasingly important component of healthcare, supporting diagnosis, clinical decision-making, medical imaging, triage, and patient-facing services. While these technologies offer significant opportunities to improve healthcare delivery, they also raise important questions about patient safety, fairness, privacy, transparency, cybersecurity, and accountability. This paper examines the regulatory framework governing medical AI in the European Union, focusing on the interaction between the Artificial Intelligence Act (AI Act), the Medical Device Regulation (MDR), the In Vitro Diagnostic Medical Device Regulation (IVDR), the General Data Protection Regulation (GDPR), and the European Health Data Space (EHDS). Drawing on doctrinal and conceptual regulatory analysis, it argues that many AI-enabled medical systems should be regulated through continuous lifecycle compliance rather than by relying primarily on a one-time assessment at market entry. The analysis identifies the training and validation dataset as a particularly important point of regulatory convergence, especially among the EHDS, GDPR, and AI Act data-governance requirements, and examines how responsibility is allocated across the general-purpose AI value chain under Article 25. Building on this analysis, the paper proposes a compliance-by-design framework that translates overlapping legal requirements into a set of auditable governance artefacts covering system classification, regulatory mapping, data governance, validation, human oversight, conformity assessment, post-market monitoring, and change control. The framework is illustrated through two contested use cases and complemented by recommendations tailored to the responsibilities of different stakeholder groups. The analysis incorporates Regulation (EU) 2026/1744 (Digital Omnibus on AI), while recognising that supporting guidance, harmonised standards, and implementation of the EHDS continue to evolve and should be checked when the framework is applied. The framework is conceptually derived and illustrated through constructed use cases rather than empirically validated; future work should assess its usability, consistency, and discriminating power through structured expert assessment and Delphi-based consensus methods.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1932256"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fed-XLM-R: a privacy-preserving federated framework with adapter-scoped differential privacy for mental health triage-level intent classification on resource-constrained edge devices.","authors":"Karthiga M, Emerson Raja Joseph, Subhash Patil, K Saranya","doi":"10.3389/fdgth.2026.1856286","DOIUrl":"10.3389/fdgth.2026.1856286","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Mental health triage-level intent classification in linguistically diverse and resource-constrained clinical environments requires models that simultaneously meet strict patient data privacy regulations, cross-lingual generalization to low-resource languages, and real-time inference on edge hardware. No existing federated NLP system has jointly demonstrated this combination. This work proposes Fed-XLM-R, a unified federated learning framework designed to close that gap.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Fed-XLM-R integrates XLM-RoBERTa with lightweight bottleneck adapter layers, adapter-scoped differentially private stochastic gradient descent (DP-SGD), proximal regularization for straggler-robust aggregation (FedProx), and INT8 post-training quantization for edge deployment. Gradient perturbation is confined exclusively to the 0.87% trainable adapter subspace rather than the full 270-million-parameter model. The framework was evaluated in a simulated 10-client federation on a public benchmark, with intent classification trained and evaluated on English data. A cross-lingual diagnostic was additionally run on the XNLI benchmark across five languages (English, Spanish, Hindi, Swahili, Arabic) against an mBERT baseline, and canary phrase memorization analysis was conducted across all 50 federated rounds to assess data extraction risk.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;At the clinically meaningful privacy budget of &lt;i&gt;ε&lt;/i&gt; = 1.0 (&lt;i&gt;δ&lt;/i&gt; = 10&lt;sup&gt;-5&lt;/sup&gt;, Fed-XLM-R achieves 96% accuracy retention, yielding an overall accuracy of 0.923, F1-score of 0.918, and AUC-ROC of 0.956, within 0.5% of a centralized baseline while providing formal differential privacy guarantees. Canary phrase analysis confirms the DP-SGD mechanism prevents training data extraction. The XLM-R backbone retains strong discriminative capacity in low-resource languages relative to mBERT on the XNLI diagnostic. Adapter-only parameter transmission reduces cumulative communication cost by 40.7-fold at the 85% accuracy threshold, at a per-round volume compatible with standard mobile data networks. INT8 quantization reduces inference latency from 1,974 ms to 523 ms on low-end CPU hardware, a 3.8-fold speedup.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;These findings characterize the mechanism's behavior under controlled conditions in a simulated federation rather than demonstrating field deployment. The cross-lingual results support the feasibility of future multilingual extension but do not themselves constitute multilingual intent classification. The three-category label scheme (Anxiety/Depression/Normal) supports triage-level intent routing but has not been validated against standardized clinical instruments; the system is not proposed as a clinical diagnostic or screening tool. Overall, Fed-XLM-R demonstrates that privacy-preserving, communication-efficient, and edge-deployable federated NLP is achievable without substantial accuracy loss, offering a practic","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1856286"},"PeriodicalIF":5.2,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM and CQL computable phenotypes for myocarditis adverse event feature and outcome determinations using patient FHIR data: a feasibility study.","authors":"Fraser W Gaspar, Seth B Roberts, Francis X Campion, Lynda Hoeksema, Jessica Kasirsky, Raymond Duncan, Matthew Sonesen, Renier Estiandan, Khyzer B Aziz, Julie A Riddler, Jessica Zhou, Hussein Ezzeldin","doi":"10.3389/fdgth.2026.1903755","DOIUrl":"10.3389/fdgth.2026.1903755","url":null,"abstract":"<p><strong>Background: </strong>Post-market surveillance for adverse events of special interest is often constrained by key diagnostic elements being captured primarily in unstructured clinical notes and reports. This feasibility study evaluated large language model (LLM) and clinical quality language (CQL) methodologies to perform phenotype feature extraction and outcome determination on Fast Healthcare Interoperability Resources (FHIR) data.</p><p><strong>Methods: </strong>Two health systems identified patients with an mRNA COVID-19 vaccination and an inpatient myocarditis diagnosis within 42 days. Patient FHIR data was retrieved via a Health Information Exchange and converted into narrative text (\"textified\"). Llama3.3 70B Instruct answered yes/no Brighton Collaborative-based myocarditis feature questions; answers were written back as FHIR Observation resources to enhance each patient bundle. Three methods were compared: simple CQL (structured data only), LLM (textified FHIR data), and enhanced CQL (CQL on enhanced bundles). Three clinicians provided gold-standard determinations. An optimization set (<i>n</i> = 10) supported pipeline refinement; performance was evaluated in a test set (<i>n</i> = 25).</p><p><strong>Results: </strong>In the test set, simple CQL performed poorly for identifying cardiac and non-specific symptoms [Matthews Correlation Coefficient (MCC): 0.08; F1: 0.02] but excellently for cardiac biomarkers (MCC:0.83; F1: 0.88). The LLM achieved excellent performance identifying symptoms and imaging features (e.g., echocardiogram abnormalities MCC: 0.86) but weaker performance for biomarkers (MCC: 0.54). Enhanced CQL had excellent feature identification performance across most domains. Both LLM and enhanced CQL correctly determined the myocarditis outcome for 23 of 25 patients.</p><p><strong>Conclusions: </strong>In this small feasibility study, structured and unstructured FHIR data provided the computable phenotypes using an LLM with sufficient clinical information for myocarditis feature identification and outcome determination.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1903755"},"PeriodicalIF":5.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-reported first-use usability of the noncommercial MyTARGET mobile application among adults with inflammatory bowel disease: a cross-sectional survey.","authors":"Daria Vasilyeva-Singer, Timo Rath, Christopher Kretzschmar, Johannes Knitza, Markus F Neurath, Raja Atreya, Till Orlemann","doi":"10.3389/fdgth.2026.1957062","DOIUrl":"10.3389/fdgth.2026.1957062","url":null,"abstract":"<p><strong>Background: </strong>Mobile health applications may support self-management in inflammatory bowel diseases (IBD), but evidence on first-use experiences with disease-specific mobile health applications is limited.</p><p><strong>Objective: </strong>To evaluate first-use usability of the noncommercial MyTARGET mobile application among adults with IBD in Germany and identify priorities for refinement.</p><p><strong>Methods: </strong>In this single-center cross-sectional usability survey, adults with Crohn's disease or ulcerative colitis installed MyTARGET on their own smartphones, received a 15-minute standardized introduction and independently explored the application for 45 min during one outpatient session. Participants completed a 13-item questionnaire adapted from the German Telehealth Usability Questionnaire on a 7-point Likert scale. Recommendation intention was assessed using the Net Promoter Score (NPS).</p><p><strong>Results: </strong>All 200 participants completed the questionnaire (mean age 39.1 years; SD, 14.0; 48.0% female); 127/200 (63.5%) had Crohn's disease and 73/200 (36.5%) had ulcerative colitis. Mean overall usability was 6.00 (SD, 0.57; 95% CI, 5.92-6.08). User-friendliness (mean, 6.65; SD, 0.61) and ease of learning (mean, 6.41; SD, 0.59) received the highest ratings, whereas error handling/troubleshooting (mean, 5.59; SD, 0.93) and perceived time savings were lower. The Net Promoter Score was +16 points. Overall usability correlated with recommendation score (Spearman <i>ρ</i> = 0.606; adjusted <i>p</i> < 0.001) and usefulness, interface quality and satisfaction/intention to use again showed stronger associations than user-friendliness.</p><p><strong>Conclusions: </strong>MyTARGET showed high patient-reported first-use usability in this selected outpatient sample. The findings identify aspects that could inform future refinement and suggest that ease of use alone may not determine willingness to recommend an IBD app.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1957062"},"PeriodicalIF":5.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Homeless-experienced patients' perspectives on improving patient portal Use.","authors":"Mikayla O Castellon, Talia J Panadero, Elizabeth M Moore, Sonya E Gabrielian","doi":"10.3389/fdgth.2026.1861993","DOIUrl":"10.3389/fdgth.2026.1861993","url":null,"abstract":"<p><strong>Background: </strong>Persons who have experienced homelessness (PEHs) face profound health disparities and barriers to digital health access. Among Department of Veterans Affairs (VA) patients experiencing homelessness, only 36% (vs. 70% of all VA users) are enrolled in the VA's digital patient portal: My HealtheVet (MHV). MHV allows patients to access health information and communicate with providers.</p><p><strong>Objective: </strong>We conducted an exploratory qualitative study to identify PEHs' perspectives on MHV engagement and inform equity-focused strategies to support portal adoption.</p><p><strong>Methods: </strong>We conducted semi-structured interviews with 17 PEHs at VA Greater Los Angeles who enrolled in MHV and participated in the Department of Housing and Urban Development-VA Supportive Housing (HUD-VASH) program, which combines housing subsidies with case management for Veterans experiencing homelessness. Interviews explored MHV enrollment, uses, barriers and facilitators to engagement, and recommendations to improve adoption. Data were analyzed using rapid qualitative methods.</p><p><strong>Results: </strong>PEHs valued MHV for secure messaging, medication management, and medical record access. Technical challenges, including login difficulties, hindered engagement. Housing stability in HUD-VASH facilitated MHV use via reliable technology, whereas homelessness itself was a barrier. Peer networks supported MHV adoption through knowledge exchange. Participants recommended simplifying interfaces, integrating MHV training into HUD-VASH intake, increasing technical support from VA staff, and providing devices to address gaps.</p><p><strong>Conclusions: </strong>MHV utilization may improve health engagement among PEHs. However, its benefits are limited by persistent digital inequities. Targeted strategies-such as peer-led onboarding, interface simplification, and integration of MHV support within housing services-may improve uptake and promote equitable access to digital healthcare.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"8 ","pages":"1861993"},"PeriodicalIF":5.2,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13593823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}