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Background Digital transformation in healthcare is guided globally by digital health maturity models and accreditation-linked standards. In India, the National Accreditation Board for Hospitals & Healthcare Providers (NABH) introduced landmark Digital Health Standards (DHS) for hospitals (2025 draft). This provides a national framework, but its positioning within the complex global landscape is unclear, making it challenging for stakeholders to benchmark progress and plan strategically. Objectives The primary objective was to map the features, domains, and assessment approaches of prominent international digital health maturity models and accreditation-linked standards. The secondary objective was to conduct a comparative analysis of these frameworks against the NABH-DHS to identify areas of convergence, divergence, and critical gaps. Methods A scoping review adhering to PRISMA-ScR guidelines was conducted. Peer-reviewed databases (PubMed, Embase, Scopus) and grey literature sources were searched from inception to 26 July 2025. We included sources describing national or international maturity models or accreditation standards for healthcare provider organizations. Data were charted using a customized form, synthesized narratively (SWiM), and comparatively analysed using a conceptual crosswalk matrix and thematic gap map. Results 38 sources were included, comprising systematic/scoping reviews ( n  = 8), official reports/standards ( n  = 17), and primary studies ( n  = 13). Key international frameworks mapped include HIMSS EMRAM, NHS England’s WGLL, WHO-PAHO IS4H, JCI, and Australian models. The NABH-DHS (structured across 8 chapters) shows strong alignment with these frameworks in core domains: Leadership, Governance, Clinical & Patient Safety, and Information & Data Management. However, the comparative analysis identified emerging gaps vis-à-vis global best practices. These include absent or minimal coverage for explicit AI governance, advanced cybersecurity maturity (e.g., alignment with NIST CSF 2.0), granular interoperability maturity assessment, a dedicated health-equity lens, and the systematic integration of Patient-Generated Health Data (PGHD). Conclusions The NABH-DHS provide a robust and comprehensive foundation for core digital assurance in India, converging well with international best practices on foundational elements. Our mapped findings are presented as external policy guidance. We recommend that future NABH revisions incorporate pragmatic, light-weight requirements to address the identified gaps (AI governance, advanced cybersecurity, interoperability metrics, and equity). This can be achieved through annexes or tiered ‘Digital Plus’ badges that reference mature external frameworks (e.g., ISO/IEC 42001, NIST CSF 2.0), ensuring a future-proof, phased implementation that safeguards patient trust as the Indian digital health ecosystem matures.

Digital health interventions (DHIs) have the potential to improve health care and health promotion. However, there is a lack of guidance in the literature for the development, refinement, and prioritization of key performance indicators (KPIs) for the evaluation of DHIs. This paper presents a 4-stage process used in the Gravitate Health project based on stakeholder consultation and consensus for this purpose. The Gravitate Health consortium, which comprises private and public partners from across Europe and the United States, is developing innovative digital health solutions in the form of Federated Open-Source Platform and G-lens to present users with individualized digital information about their medicines. The first stage of this was the consultative process for the development of KPIs involving stakeholder (Gravitate Health project leads) consultations at the planning stages of the project. This resulted in the formation of an extensive list of KPIs organized into 7 categories. The second stage was conducting a scoping review, which confirmed the need for extensive stakeholder consultation in all stages of the KPI development, refinement, and prioritization process. The third stage was a period of further consultation with all consortium members, which resulted in the elimination of 1 category of KPIs. The fourth stage involved using the Delphi technique for refining and prioritizing the remaining 6 categories of KPIs. It is unusual to use this methodology in a nonresearch exercise, but it provided a clear consultative framework and structure that facilitated the achievement of consensus within a large consortium of 250 members on a substantial list of KPIs for the project. Consortium members ranked the relevance and importance of each KPI. The final list of KPIs provides substantial indicators sensitive to the needs of a broad group of stakeholders that are being used to capture real-world data in developing and evaluating DHIs.

Medical image authentication plays a vital role in secure healthcare industries, where assuring the integrity and authenticity of diagnostic images is critical for safe clinical decisions. This study presents a robust, dual watermarking framework that embeds a machine-readable QR code and a hospital logo into medical images using a hybrid frequency-domain method combining Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). A lightweight Convolutional Neural Network (CNN) decoder is developed for efficient watermark extraction, optimized through a novel enhanced loss function that integrates Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Sobel edge loss. The encoder-decoder framework ensures imperceptibility, low computational cost, and resilience to standard signal and geometric attacks. The model is tested against Salt & Pepper noise, median filtering, rotation, and cropping to validate robustness. The proposed scheme achieves high watermark extraction fidelity with a Peak Signal-to-Noise Ratio (PSNR) ranging from 64.87 to 68.75 dB and Normalized Correlation (NC) values consistently reaching 1.0 under several attacks, demonstrating an average improvement of 28–35% in PSNR and 12–15% in NC. Furthermore, the lightweight CNN demonstrates a small model size of 0.65 MB with real-time inference capability, making it suitable for embedded and resource-constrained medical devices. The results confirm that the proposed dual watermarking method maintains visual quality, structural integrity, and security of medical images while ensuring efficient and accurate watermark retrieval.

Digital health governance frameworks have primarily focused on prospective safeguards, including informed consent at the point of data collection, lawful processing, and data security. Comparatively less attention has been devoted to the long-term circulation of legacy clinical materials, particularly pediatric clinical images reused across educational and digital infrastructures. This viewpoint examines governance challenges associated with the prolonged educational and digital reuse of pediatric clinical images without identifiable evidence of consent. Drawing on a longitudinal case spanning more than 3 decades (1991-2026), this article illustrates how clinical images may continue circulating across textbooks, educational repositories, conference materials, e-books, and online teaching platforms long after their original creation and publication context. The case is informed by archival educational materials, institutional correspondence, publisher communications, and formal regulatory findings, including a decision issued by the Polish Patient Rights Ombudsman confirming continuing violations related to dissemination of intimate pediatric clinical images without identifiable consent. This article argues that current digital health governance frameworks remain insufficiently equipped to address persistence, traceability, provenance, and coordinated withdrawal of legacy clinical materials once they enter distributed educational ecosystems. Fragmented accountability across health care institutions, publishers, educational systems, libraries, repositories, and digital platforms may allow sensitive clinical materials to remain accessible despite regulatory intervention or removal requests. The article further discusses how publicly accessible educational materials may become incorporated into downstream artificial intelligence and machine learning ecosystems through digitization, aggregation, web scraping, and secondary dataset reuse. In this context, unresolved historical consent deficiencies may become embedded within artificial intelligence-enabled infrastructures without effective provenance tracking or remediation mechanisms. To address these limitations, this viewpoint proposes a lifecycle-oriented governance framework emphasizing long-term consent traceability, provenance-aware dissemination systems, verification checkpoints before reuse or republication, periodic review of legacy educational archives, and coordinated cross-platform withdrawal procedures.

To the Editor: The review by Yu et al. (May 30 issue) 1 misses a key point: with which specific human values should an artificial intelligence (AI) model be aligned? The question is essentially about fairness. 2,3 The alignment of AI with human values is not about adhering to a particular set of human values but about ensuring that the model respects and encompasses a wide range of different values. Currently, the values that are embedded in AI systems are primarily those of the few large conglomerates that develop these models. These commercial interests do not necessarily conform to the diverse values . . .

The rapid expansion of national digital health infrastructures has transformed the way citizens interact with healthcare systems. However, the long‐term success of these platforms depends not only on technological implementation but also on users’ perceptions and continued engagement. This study investigates how the usability of a national mobile health application influences users’ behavioral intention through the mediating role of e‐satisfaction. Drawing on usability and technology acceptance literature, the study proposes a research model in which the perceived usability of a mobile health application positively affects users’ behavioral intention both directly and indirectly via e‐satisfaction. Empirical data were collected from users of the Turkish national digital health platform e‐Nabız through a structured survey. The proposed research model was tested using structural equation modeling techniques. The results indicate that mobile health application usability significantly enhances users’ e‐satisfaction, which in turn positively influences behavioral intention, including recommendation intention and continued preference intention. Interestingly, the analysis revealed that even negative usability perceptions were associated with increased e‐satisfaction, likely reflecting the quasimandatory nature of the platform, where functional necessity and perceived value can outweigh usability difficulties. The findings also demonstrate the mediating role of e‐satisfaction in the relationship between usability and behavioral intention. By focusing on a large‐scale national digital health platform, this study contributes to the growing literature on digital health adoption by highlighting the importance of usability‐driven user experience in quasimandatory digital health systems. The results provide practical insights for policymakers and digital health system designers seeking to improve citizen engagement and long‐term utilization of national digital health infrastructures.

Digital health technologies can potentially increase the efficiency and quality of pediatric palliative care (PPC), yet their use in home-based PPC remains limited. Limited digital health care literacy and inadequate training can reduce confidence and foster negative attitudes, whereas positive experiences and basic digital health care literacy may encourage adoption. This study aims to explore the use of digital health technologies by Norwegian health care personnel in home-based PPC and examine the association between their digital health care literacy and their attitudes toward digital health. A cross-sectional study was conducted from September 2023 to May 2024, with an online survey targeting health care personnel involved in home-based PPC through primary or specialist health care services. Data were collected using selected items from the Norwegian Healthcare Personnel Survey on eHealth 2022, the Digital Health Care Literacy Scale (DHLS), and the Information Technology Attitude Scales for Health (ITASH), alongside demographic characteristics. Higher DHLS scores indicate greater digital health care literacy, while higher ITASH scores reflect more positive attitudes toward digital health technologies. Pearson correlation, ANOVA, and multiple linear regression analyses were conducted to comprehensively explore the relationships and associations among the variables. Health care personnel (n=148) from diverse health care services responded to the survey. Half of the respondents (72/144, 50%) had experience with real-time video consultation, while phone calls were the primary communication method (138/145, 95.2%). Additionally, 55.6% (79/142) of the respondents had limited or minimal access to electronic health records from other health care services. Health care personnel perceived digital health technologies for remote PPC as a supplement (126/135, 93.3%) rather than a replacement for in-person care. Mean digital health care literacy was 18.29 (SD 3.8) on a scale from 0 to 23. On a scale from 1 to 4, the highest recorded scores pertained to attitudes toward digital health technologies in supporting care (mean 3.17, SD 0.39) and the perceived need for training (mean 3.16, SD 0.43). A statistically significant association was found between the respondents' level of digital health care literacy and their attitudes toward digital health technologies in supporting care (β=0.030, 95% CI 0.014-0.047; P<.001). This study examined the use of digital health technologies by Norwegian health care personnel in home-based PPC, their digital health care literacy, and attitudes toward digital health. Despite positive attitudes and high digital health care literacy, use of digital health technologies was limited, suggesting that inadequate digital health solutions may hinder effective implementation. Addressing these barriers is crucial to enhancing the implementation of digital health in home-based PPC. Future research should focus on integrating digital health technologies into existing infrastructure and workflows while exploring their impact on personalized care to ensure high-quality home-based PPC.

The National Health Service 10-year health plan emphasizes an increasing shift toward digital health care delivery. However, there is limited research on how best to support, engage, and include individuals who are digitally excluded. As health care services become more digitally driven, evidence-based interventions are needed to address digital exclusion and ensure equitable access to care, particularly for people living with long-term conditions. This study aimed to evaluate the feasibility and acceptability of providing digital literacy training alongside a digital health intervention (DHI; Ex-Tab intervention), compared with providing a DHI alone. Kidney Beam, a DHI designed to promote physical activity and improve quality of life in people with chronic kidney disease (CKD), was used as an exemplar DHI. This mixed methods, single-site pilot randomized controlled trial recruited 40 adults with CKD who were digitally excluded. Digital exclusion was defined as lacking access to a Wi-Fi-enabled digital device or having a Digital Health Care Literacy Scale (DHLS) score of <7 (range 0-21). Participants were randomized 1:1 to receive either the Kidney Beam Ex-Tab intervention or Kidney Beam alone (control). The intervention group received a Wi-Fi-enabled iPad on loan with Kidney Beam preinstalled, digital literacy training, and ongoing support to access the 12-week Kidney Beam program (twice weekly live exercise and education sessions). The control group received sign-up instructions for Kidney Beam only. Feasibility outcomes were assessed against a priori progression criteria and included screening, recruitment, retention, adherence, safety, and acceptability. Secondary outcomes included the Kidney Disease Quality of Life Questionnaire, Chalder Fatigue Questionnaire, and Patient Health Questionnaire-4. Outcomes were measured at baseline and 12 weeks. Acceptability and user experience were explored through semistructured interviews with participants from both groups at 12 weeks (n=25). Between September 2023 and September 2024, a total of 169 individuals were screened and 40 were enrolled (median age 66.5 years; 20 male individuals; median DHLS score: 4). Twenty-one participants were randomized to the Kidney Beam Ex-Tab group and 19 to the Kidney Beam alone group. Of the 40 participants, 35 (88%) completed the 12-week follow-up (intervention: n=18; control: n=17). All prespecified feasibility criteria for recruitment, retention, adherence, and safety were met. Qualitative findings indicated that the tablet loan and digital literacy training were acceptable and highly valued, enhancing confidence, motivation, and DHI engagement. Providing loaned devices was particularly important for overcoming access barriers, especially for participants unable to afford their own device. Providing Wi-Fi-enabled devices and digital literacy training alongside a DHI was feasible and acceptable for people with lower digital literacy levels. The findings support progression to a future definitive multicenter trial or implementation study and offer transferable insights for the design of digital inclusion strategies for other long-term health conditions.

IntroductionData quality in electronic health records (EHRs) is central to data-informed healthcare. Health professionals play a key role in ensuring data quality yet the complexities of clinical data practices remain poorly understood. Previous reviews have focused on specific documentation domains or professions, leaving a gap in understanding the broader individual, organisational, technological and contextual factors influencing data quality in hospital settings. This scoping review aims to identify and map factors that promote or hinder data quality in EHRs among health professionals in hospital settings.Methods and analysisThe review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews and be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR) checklist. Peer-reviewed studies will be identified through comprehensive searches in PubMed, Scopus, Web of Science, CINAHL and Google Scholar. Two independent reviewers will screen titles, abstracts and full texts and extract data using the JBI Extraction Form. Data will be charted and mapped according to the six dimensions of the Digital Health Data Quality Dimension and Outcome (DQ-DO) framework—accuracy, completeness, consistency, contextual validity, currency and accessibility—and analysed across professional groups and hospital contexts.Ethics and disseminationEthical approval is not required for this scoping review as it is based on publicly available data. The findings will be disseminated through peer-reviewed publication and presentations at relevant academic and clinical conferences.RegistrationThe protocol has been registered in the Open Science Framework: https://doi.org/10.17605/OSF.IO/YQ2DX