Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 61 appraisals
Journal of medical Internet research
Digital Transformation in Health Care: Are We on the Right Track?
Health care digital transformation is gaining increasing attention, despite the observed challenges in its implementation. The envisioned benefits, together with the growing need for better health care, are motivating academia, organizations, regulatory agencies, and governments to develop more effective digital health care solutions. Through extensive debates among the authors, this paper discusses how digital transformation is being conducted in the health care sector. Our discussion relies on concepts from sociotechnical systems theory, categorizing it into 3 social (people, culture, and goals) and 3 technical (processes/procedures, infrastructure, and technology) dimensions. Drawing on both the literature discussed in this paper and our combined academic and practical experience-each author contributing over a decade of work in health care digital transformation-we examined current digitalization efforts from a sociotechnical standpoint. Overall, we argue that both social and technical dimensions present elements that have either encouraged or discouraged the progress of health care digital transformation. The identification of current trends on such (on- and off-track) elements allowed the formulation of 12 propositions across each sociotechnical dimension for future testing and validation. This approach can help establish better government policies, foster private initiatives, and shift regulatory guidelines to support a successful digital transformation in health systems. Finally, from a research perspective, we outline some opportunities for further interdisciplinary investigation in the field, promoting advances in the understanding of health care digital transformation.
22 July 2026
Read appraisal →PloS one
Self-positioning in space science communication: A corpus-assisted discourse study
This study incorporates text mining into critical discourse analysis to examine how government science agencies in China and the United States position themselves in space science communication on social media. Two specialized corpora have been built by collecting posts from government science agencies on Weibo from China and X (formerly Twitter) from the U.S. With the help of the text mining tool KH Coder, this study gives a corpus-assisted discourse analysis of the particular ways of positioning at different levels of discourse: (1) topics/themes, (2) addressing terms, and (3) those words that co-occur with self-addressing terms in a sentence. The findings reveal significant differences in their preferential ways of positioning. Chinese government science agencies present themselves as state-affiliated yet approachable institutions, blending achievements and operational efficiency with patriotism and collective pride. Their use of diverse addressing terms and co-occurrence patterns portrays them as experienced, supportive guides, balancing national identity with interpersonal closeness. In contrast, U.S. government science agencies emphasize professionalism, focusing on research, space exploration, mission execution, and audience engagement, with minimal reference to state affiliation. Their addressing terms are formal and standardized, with pride centred on mission success and discovery, highlighting expertise and scientific leadership. Their preferential ways of positioning are further explained in their respective contexts in order to present a proper understanding of these differences.
12 July 2026
Read appraisal →BMJ health & care informatics
Clarifying the relationship between biomedical and health informatics and digital health: expert perspectives.
The rise of digital health (DH) has sparked debates about its relationship with the long-established field of biomedical and health informatics (BMHI), with some questioning the need for new academic programmes in DH since much of the likely content is already covered in BMHI training programmes. At the International Medical Informatics Association panel, 'Shaping the Future of Biomedical and Health Informatics in the Digital Health Era' (MEDINFO 2025), only one-third of participants clearly distinguished the two fields, highlighting conceptual ambiguities and the influence of DH narratives. This article compiles the perspectives expressed by the panellists, all experienced BMHI scholars, presenting their distinct viewpoints on how the field can maintain its scientific identity while engaging with emerging DH initiatives.
24 June 2026
Read appraisal →Sexual health
A systematic review of barriers to cervical cancer screening among Thai women based on the social ecological model: a multi-level analysis of local evidence
BACKGROUND: Cervical cancer is a significant public health issue in Thailand. Despite the implementation of a national screening program, its coverage remains suboptimal. This study aims to systematically review local Thai evidence and comprehensively analyze the multi-level barriers affecting women's participation in cervical cancer screening using the social ecological model (SEM). METHODS: Systematic searches were conducted in PubMed, Scopus, CINAHL and ThaiJO databases from inception to 2025. Literature was screened according to pre-defined inclusion and exclusion criteria, resulting in the inclusion of 11 studies. The Newcastle-Ottawa scale and a mixed-methods assessment tool were used for rigorous quality appraisal. Thematic synthesis was used to categorize screening barriers into five levels: individual, interpersonal, community, health system and public policy. RESULTS: The key obstacles identified include: at the individual level, lack of knowledge, fear, shame and insufficient risk perception; at the interpersonal level, the influence of family and social norms; at the community level, cultural, religious and regional factors; at the health system level, poor service accessibility and system inefficiencies; and at the public policy level, incomplete health insurance coverage and insufficient awareness campaigns. Many of these obstacles show distinct characteristics of Thai society and culture. CONCLUSION: Barriers to cervical cancer screening among Thai women constitute a complex, multi-level system. Future intervention strategies must move beyond single-level health education, and should develop cross-level, culturally-appropriate, comprehensive measures. These include promoting HPV self-sampling, conducting community empowerment education, optimizing the health service system and strengthening policy support to systematically increase screening participation.
16 June 2026
Read appraisal →PloS one
Gauging support for autonomous delivery drones powered by artificial intelligence: A public survey in Singapore
There has been emerging empirical evidence supporting the role of artificial intelligence (AI) knowledge in public support for AI. These findings stand in sharp contrast to the long-standing cognitive miser view in determining technological acceptance. In this study, by contrasting models of knowledge deficit against cognitive miser using data from a large-scale public opinion survey about autonomous delivery drones in Singapore (N = 1,002), we investigated whether knowledge and various heuristic cues were associated with AI support. Our results revealed that objective knowledge was not a direct determinant of support, while various heuristic cues such as propensity to trust in AI, news media attention, perceived source credibility in scientists and authorities, as well as risk and benefit perceptions presented significant associations. Mediation analyses further revealed knowledge was indirectly associated with support through heuristic cues such as source credibility perceptions as well as risk and benefit perceptions. Our findings were largely consistent with the cognitive miser model in that AI acceptance is immediately associated with cognitive heuristics rather than knowledge. Theoretical and practical implications are discussed.
12 June 2026
Read appraisal →PloS one
Reinforcement learning for policymaking in epidemic control: A scoping review
BACKGROUND: Managing an epidemic demands policies that respond at the pace of the outbreak. Conventional rule‑based interventions struggle to keep up, prompting interest in reinforcement learning (RL) for designing non‑pharmaceutical interventions (NPIs). However, current evidence is fragmented across diverse models and reporting styles. OBJECTIVES: To systematically map how RL is applied for epidemic NPI design, describe modeling choices, algorithm architectures, evaluation practices, and identify trends and research gaps. METHODS: Peer-reviewed studies (2014-2025, English) that applied deep RL to select NPIs were retrieved from IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus, searched on December 23, 2025. Reference list scanning supplemented database results. Predefined data items (bibliographic details, epidemic and RL model characteristics, experiments, validation methods, outcomes) were charted and summarized descriptively. RESULTS: Of 512 retrieved records, 10 met the inclusion criteria, and three additional studies were identified via reference-list scanning, yielding 13. Five employed value‑based methods, four policy‑gradient, and four hybrid; one study additionally incorporated model-based planning. Six simulations relied on compartmental models, six on agent‑based models, and one on a hybrid model. Action spaces were predominantly discrete restriction levels. Five studies incorporated sequence-modeling techniques to include temporal context into a state space. Eleven studies designed reward functions as a trade-off between pandemic severity and socio-economic cost. According to the reviewed studies, RL policies across various settings outperform heuristic, rule-based, and historical baselines in reducing infections, deaths, or lockdown duration while limiting economic loss. CONCLUSIONS: RL shows promise for adaptive epidemic control. Comparison is hampered by simplified economic costs, inconsistent calibration rigor, varied evaluation metrics, and limited uncertainty or policy robustness analysis. Future work should establish common benchmark environments and reporting standards, incorporate empirically grounded economic and behavioral models, adopt uncertainty-aware and probabilistic RL, develop more sophisticated control spaces, investigate more advanced algorithms, and validate learned policies prospectively to enable real-world deployment.
9 June 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
General practice incentives: a review of the international literature.
BACKGROUND: In Australia, the general practice sector faces significant challenges, leading to a re-evaluation of its predominantly fee-for-service (FFS) funding models. AIM: To conduct a systematic review of reviews that evaluated the efficacy of funding models in general practice on quality outcomes in multidisciplinary primary and preventive care for people with complex chronic conditions, as well as the contextual factors that have influenced their implementation. DESIGN AND SETTING: Only systematic reviews and meta-analyses were included. Search terms covered funding mechanisms, primary care, and general practice. The review followed PRISMA guidelines for systematic reviews. METHOD: PubMed, Cochrane, Embase, CINAHL, PsycInfo, PAIS, and Web of Science databases were searched in November 2023 for publications from 2010 onwards. RESULTS: Twenty reviews were included. Blended payment models incorporating pay-for-performance (P4P) with either capitation payments or FFS were associated with small improvements in quality outcomes, particularly in multidisciplinary settings. Changes in intermediate health outcomes and process measures for people with complex chronic conditions were most evident in diabetes care but inconclusive for other health outcomes and conditions. Improvements were mainly observed in incentivised activities and in less rigorously designed studies, with ceiling effects and variation reported across healthcare systems. There was no evidence that introducing capitation payments as part of blended payments improved quality of care. CONCLUSION: Although blended payment models show promise, evidence for the effectiveness of models including P4P is highly variable. If adopted, careful evaluation of each incentive's impact on both quality and equity will be essential.
31 May 2026
Read appraisal →African journal of reproductive health
Reproductive health in the digital and social age: AI, education, law, and community-based interventions.
Reproductive health has increasingly emerged as a complex, multidimensional field that extends far beyond clinical care into the domains of education, technology, law, culture, and socio-economic systems. This special issue of the African Journal of Reproductive Health brings together fifteen diverse yet interconnected papers that collectively illuminate how reproductive health outcomes are shaped by dynamic interactions across disciplines, geographies, and social structures. The contributions - spanning China, Spain, and Pakistan - underscore a unifying premise: that sustainable reproductive health improvements depend on integrative approaches that harness innovation while remaining grounded in cultural and social realities. La santé reproductive s'affirme de plus en plus comme un domaine complexe et multidimensionnel qui dépasse largement le cadre des soins cliniques pour englober l'éducation, la technologie, le droit, la culture et les systèmes socio-économiques. Ce numéro spécial de l'African Journal of Reproductive Health rassemble quinze articles divers mais interconnectés qui, ensemble, mettent en lumière la manière dont les résultats en matière de santé reproductive sont façonnés par des interactions dynamiques entre disciplines, territoires et structures sociales. Ces contributions, provenant de Chine, d'Espagne et du Pakistan, soulignent un principe unificateur : l'amélioration durable de la santé reproductive repose sur des approches intégratives qui exploitent l'innovation tout en restant ancrées dans les réalités culturelles et sociales.
30 May 2026
Read appraisal →JMIR research protocols
Evaluating the Effectiveness of an Enhanced Early Childhood Development Program Integrated Into Primary Health Care in China: Protocol for a Cluster Randomized Controlled Trial
BACKGROUND: Early childhood development (ECD) programs refer to policies and programs aimed at protecting young children's rights to achieve their full potential. Parenting interventions are effective at improving children's cognitive development and overall well-being. However, there is limited evidence on how to effectively implement and integrate such programs into routine service delivery at scale. The Government of China launched the ECD program in 2013 and the ECD scale-up program in 2023. OBJECTIVE: Our study aims to design, implement, and evaluate an enhanced ECD program embedded in the primary health care system in order to provide ECD policy recommendations for future national scale-up. METHODS: This study involves a multicenter cluster randomized controlled superiority trial in 3 counties with different levels of socioeconomic development in China. Fifty-eight clusters will be randomly assigned in a 1:1 ratio to intervention and control groups, stratified by county. In each cluster, 18 families with children aged 6-23 months will be recruited. Our intervention aims to implement an enhanced ECD program in primary care facilities. Intervention group participants will receive usual care alongside enhanced ECD services, including improved facilities, capacity building, and improved ECD services (age-adapted clinical consultations on nutrition and stimulation, age-specific parenting group sessions, home visits, and referrals for children in need). Moreover, an electronic data platform will be used. Control group participants will receive usual care. Our primary outcome is children's overall development, as measured by the Global Scale of Early Development combined form at 12 months of follow-up. Evaluation data will be collected in both arms. Additionally, a process evaluation and an incremental cost-effectiveness analysis will be conducted. Primary analysis will follow the intention-to-treat principle, and it will use generalized estimating equations to assess the population average intervention effect, with adjustment for corresponding baseline outcomes, selected individual variables, county, cluster variables, and variables differing at baseline. Qualitative data will be collected through semistructured interviews and focus groups, guided by the Capability, Opportunity, Motivation, and Behavior framework to explore implementation-related factors. Data will be transcribed for analysis using NVivo 14 (Lumivero). Ethical approval has been obtained from the Institutional Review Board of Shanghai Children's Medical Center Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS: This study was funded in 2023 and registered in April 2024. The recruitment of 1044 participants was completed on September 14, 2024. Data collection was completed on January 31, 2026. CONCLUSIONS: We aim to design an enhanced ECD service delivery model that can be embedded into the routine primary health care system. The study findings will guide government policies and reforms for strengthening the health system to scale up ECD services. Furthermore, this project may provide policy evidence to support countries in establishing or expanding their ECD services.
29 May 2026
Read appraisal →Journal of medical Internet research
Rerouting Eye Care: How AI and Telemedicine Are Reshaping Ophthalmology Patient Journeys in the United Kingdom and Germany
Artificial intelligence (AI) as a medical device is now progressively entering routine ophthalmic care, yet its impact is still mostly evaluated in terms of diagnostic performance rather than how it reshapes patient care pathways. This viewpoint argues that careful pathway design is crucial to implementing AI in ophthalmology so that it translates into real, practical benefits for patients and services. We propose a framework that classifies AI- and telemedicine-enabled eye care pathways by the initial point of contact and the role of AI: from direct human assessment to grader- or telemedicine-based use to AI-first contact and fully autonomous AI gatekeeping. We apply this framework to 2 contrasting health systems, the United Kingdom and Germany, focusing on common retinal diseases and cardiovascular risk assessment from retinal images. In the United Kingdom, AI is being introduced mainly as a modular upgrade to standardized programs layered onto a gatekeeper model centered on community optometrists and general practitioners. In Germany, direct access to office-based ophthalmologists, opportunistic screening, and commercial retail offerings are producing more fragmented clinician- and market-driven AI adoption. Our comparison shows that the same AI technologies can generate very different patient journeys across health systems. Therefore, their value depends not only on diagnostic performance but also on intentional pathway design, including clear escalation rules and structured information transfer. This includes appropriate task shifting from ophthalmologists to trained staff or new roles, such as image grading, tele-ophthalmology triage, or pathway management. Without such design, AI risks duplicate testing, incomplete or poorly coordinated referrals, and further fragmentation of care.
29 May 2026
Read appraisal →Journal of medical Internet research
Artificial Intelligence in Patient-Centered Care and Macro-, Meso-, and Micro-Level Determinants of Rehumanization and Dehumanization: Qualitative Interview Study
BACKGROUND: Patient-centered care remains a foundational principle of modern health care. The digital transformation of health systems has accelerated the adoption of artificial intelligence (AI) across diagnostic, predictive, and communicative functions, with implications for efficiency and clinical workflows. At the same time, AI integration raises concerns regarding transparency, equity, accountability, and trust, positioning it as a potential driver of both rehumanizing and dehumanizing dynamics in health care practice. OBJECTIVE: This study examines how the adoption of AI in health care may influence patient-centered care, exploring its potential to promote rehumanization or contribute to dehumanization. The objective is to identify the factors that shape these outcomes at the macrolevel (policy and infrastructure), mesolevel (institutional practices), and microlevel (individual behaviors and interactions). METHODS: This study adopts an exploratory qualitative design informed by grounded theory principles, drawing on 20 semistructured interviews with health care leaders, clinicians, researchers, legal experts, and industry consultants who have substantial professional experience across European health care systems, with some participants also contributing experience from the US health care context. To enhance analytical rigor and transparency, the study applied the Gioia methodology, enabling inductive coding from first-order concepts to second-order themes and aggregate dimensions. This multistakeholder approach facilitated a nuanced examination of how AI integration is perceived and experienced across macro-, meso-, and microlevels of health care. RESULTS: The analysis identified key system-level factors shaping rehumanizing or dehumanizing outcomes of AI integration. At the macrolevel, 8 factors-including regulatory frameworks, policy priorities, and infrastructure-were identified as influencing whether efficiency pressures outweigh patient-centered values. At the mesolevel, 5 factors related to institutional strategies, workflows, and leadership shape how AI tools are embedded into care delivery. At the microlevel, 7 factors related to individual behaviors, trust, and doctor-patient interaction dynamics influence whether AI supports empathy and engagement or diminishes them. Rehumanizing potentials include reduced administrative burden, improved care pathways, clearer health communication, and enhanced decision-making, while risks include shorter consultations, reduced empathy, overreliance on automation, and erosion of professional identity. Without deliberate alignment with patient-centered principles, efficiency gains risk undermining the human dimensions of care. CONCLUSIONS: This study represents one of the first empirical examinations of how AI shapes health care practices through rehumanizing and dehumanizing dynamics. The findings demonstrate that outcomes depend not only on technical capabilities but also on regulatory frameworks, institutional strategies, and cultural adaptation. By systematically mapping influencing factors across macro-, meso-, and microlevels, the research provides actionable insights for decision-makers to ensure that efficiency gains remain aligned with patient-centered principles. Realizing AI's promise requires coordinated action to preserve empathy, trust, and interpersonal connection, ensuring that innovation strengthens rather than weakens the human dimensions of care.
28 May 2026
Read appraisal →Global health action
Integrating GIS and local knowledge for optimised medical supply prepositioning in flood-prone Nigeria: a strategic framework
BACKGROUND: Nigeria faces recurring flood disasters that severely disrupt its healthcare systems and delays medical relief efforts. Current medical supply prepositioning by the National Emergency Management Agency (NEMA) remains inadequate due to infrastructure limitations, funding constraints, and poor coordination. OBJECTIVE: To develop and validate a Strategic Medical Hub Locator Framework that integrates Geographic Information Systems (GIS) analysis with qualitative local knowledge to optimise medical supply prepositioning for flood emergencies in Nigeria. METHODS: We employed a mixed-methods approach, combining qualitative analysis of 22 GIS-based flood risk studies in Nigeria and structured expert survey responses from 34 professionals with flood response experience, including five with direct NEMA warehouse experience. The framework was grounded in Disaster Risk Reduction theory and supported by Protection Motivation Theory and Theory of Planned Behaviour. RESULTS: The framework identified eight strategic medical hub locations: six existing NEMA warehouses (Abuja, Lagos, Kano, Gombe, Maiduguri, Kaduna) with the strongest performance, and two new strategic sites (Akure and Uyo) to address service gaps in South-South and South-East regions. Abuja emerged as the highest-performing hub with a composite score reflecting good centrality, governance capacity, and operational readiness. The framework successfully bridged data silos between spatial flood risk patterns and operational constraints. CONCLUSIONS: Integration of GIS thematic analysis with qualitative local data provides a viable, replicable method for enhancing humanitarian medical logistics in flood-prone regions. The Strategic Medical Hub Locator Framework offers a structured decision-support tool that can inform evidence-based investment in Nigeria's disaster health infrastructure, while being adaptable to other Global South contexts. Key findings: Where primary geospatial data silos are present, GIS analysis can be enhanced by combining it with local qualitative insights, thereby bridging the gap between technical modelling and practitioner realities.Added knowledge: The paper offers a methodological contribution through the design of a Strategic Medical Hub Locator Framework that is replicable in contexts where primary data and complex modelling are not feasible.Global health impact for policy and action: This study provides policymakers and humanitarian actors with a proof-of-concept decision-support tool. The exploratory framework offers a scalable model that can be adapted to other flood-prone settings where primary data and practical knowledge are scattered, thereby strengthening disaster preparedness and health system resilience strategies.
27 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Systemic electioneering from the evangelical pulpit: Evidence from a computational analysis
Religious institutions' engagement in prohibited electoral advocacy is a growing concern for democratic governance. In the United States, such mobilization has been especially visible within the evangelical movement. We examine this phenomenon using a corpus of 88,546 sermons from predominantly evangelical churches, transcribed from 63,683 h of Sunday services spanning the 2020, 2022, and 2024 election cycles and a nonelection control period. Analysis of this corpus reveals that direct political advocacy and endorsements are widespread: 14.7% of churches engaged in this speech during the three months surrounding the 2024 election. This behavior follows a clear electoral rhythm, peaking on the Sunday before Election Day, when 3.5% of churches mobilized. Geographic analysis indicates that advocacy correlates with county partisan composition but lacks strategic targeting; rates in swing states are indistinguishable from those in safe states. Our results provide large-scale quantification of political speech from American pulpits that constitutes prohibited electioneering under current Internal Revenue Service (IRS) guidelines, revealing a pattern of base mobilization driven by local partisan context rather than national electoral strategy.
27 May 2026
Read appraisal →Current obesity reports
How Have Researchers Estimated the Impact of Excess Weight on Mortality? A Systematic Review.
OBJECTIVE: The aim of this review is to describe and reflect on the methodology of the studies that estimate the attributable mortality (AM) to excess weight. The purpose was to provide a methodological framework and serve as a starting point to improve the estimation of AM to excess weight. This will lead to more comparable estimates over time and across countries. METHODS: A systematic review was conducted considering MEDLINE (Ovid), Embase and Web of Science databases. Studies that had estimated AM to excess weight published until December 2024 were included. Data extraction was performed in an ad-hoc table based on the STREAMS-P tool, and a descriptive analysis of the methodology employed was performed. RESULTS: The search strategy yielded 2,163 results. Twenty-three studies estimating AM in four continents were included. The majority estimated AM to all-cause mortality (n = 9) and for cardiovascular diseases (n = 9), followed by cancer without specifying which type of cancer (n = 7). Variation was observed in the body mass index (BMI) cut-off points used to define overweight, obesity and excess weight. Thirteen studies applied the population attributable fraction (PAF) formula denoted by PAF = [P (RR-1)] / [1 + P (RR-1)]. Most of the relative risks (RR) derived from meta-analysis (n = 14), and only a minority studies (n = 6) used RR from national studies based on the population for which AM was estimated. CONCLUSION: This review emphasizes methodological constraints on AM estimation to excess weight. Causes of death causally associated with excess weight should be prioritized rather than relying solely on all-cause mortality. The ideal scenario would be to consider lag times; RR derived from the population under study and to use WHO BMI cut-offs and measured data rather than self-reported weight and height. The absence of this data should not prevent AM estimation if limitations are acknowledged. Expanding available estimates worldwide is essential to improve understanding of the mortality burden associated with excess weight.
26 May 2026
Read appraisal →Lancet (London, England)
Updated trends in the global prevalence and burden of mental disorders, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023
BACKGROUND: The 2023 iteration of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated prevalence, incidence, and health burden for 375 diseases and injuries, including 12 mental disorders. We assess past, current, and emerging trends in the prevalence and burden of mental disorders across sexes and age groups, for 21 regions, 204 countries and territories, and by Socio-demographic Index (SDI) quintile, from 1990 to 2023. METHODS: Mental disorders included in GBD 2023 were anxiety disorders, major depressive disorder, dysthymia, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, anorexia nervosa, bulimia nervosa, idiopathic developmental intellectual disability, and a residual category of other mental disorders. A literature review identified epidemiological data for each disorder. These were analysed via a Bayesian meta-regression to estimate prevalence by disorder, sex, age, location, and year. Disorder-specific prevalence was multiplied by disability weights representing the severity of health loss associated with each disorder to estimate years lived with disability (YLDs). Deaths due to anorexia nervosa were assessed with a Cause of Death Ensemble modelling strategy to estimate deaths by sex, age, location, and year, and then multiplied by the standard life expectancy at age of death to estimate years of life lost (YLLs). YLDs equalled disability-adjusted life-years (DALYs) for all mental disorders except anorexia nervosa (the only mental disorder considered as an underlying cause of death in GBD), for which DALYs represented the sum of YLDs and YLLs. We presented prevalence, deaths, YLDs, YLLs, and DALYs as counts, age-specific rates per 100 000 population, and age-standardised rates per 100 000 population. FINDINGS: We estimated 1·17 billion (95% uncertainty interval 1·06-1·31) prevalent cases of mental disorders globally in 2023, equivalent to an age-standardised prevalence rate of 14 210·7 cases (12 849·5-15 940·1) per 100 000 population. These estimates represented a 95·5% (75·0-121·2) increase in prevalent cases and 24·2% (11·4-41·4) increase in age-standardised prevalence rate between 1990 and 2023. All mental disorders showed increases in prevalent cases between 1990 and 2023, while notable increases were seen in age-standardised prevalence rates for anxiety disorders, major depressive disorder, dysthymia, anorexia nervosa, bulimia nervosa, schizophrenia, and conduct disorder. There were an estimated 171 million (127-228) DALYs due to mental disorders globally across sex and age in 2023, equivalent to an age-standardised DALY rate of 2070·5 DALYs (1519·1-2750·5) per 100 000 population. Mental disorders contributed to 6·1% (4·8-7·6) of all-cause DALYs in 2023, making them the fifth leading cause of global DALYs (up from 12th in 1990). DALYs were almost entirely composed of YLDs. Mental disorders were the leading cause of YLDs in 2023 (up from second in 1990), explaining 17·3% (14·8-20·6) of all-cause global YLDs. Leading causes of mental disorder DALYs were anxiety disorders (ranked 11th among the 304 diseases and injuries at Level 4 of the GBD cause hierarchy), major depressive disorder (15th), and schizophrenia (41st). Globally in 2023, mental disorder age-standardised DALY rates were higher among females (2239·6 [1643·7-3014·1] per 100 000) than among males (1900·2 [1399·8-2510·8] per 100 000), and peaked in the 15-19 years age group (2617·3 [1850·6-3696·8] per 100 000). All locations showed increased mental disorder DALY rates in 2023 compared with 1990, ranging across countries and territories from 1302·4 (952·7-1683·7) per 100 000 in Viet Nam to 3555·8 (2661·9-4715·0) per 100 000 in the Netherlands. Across SDI quintiles, DALY rates ranged from 1853·0 (1352·1-2469·3) per 100 000 for middle SDI to 2184·1 (1606·1-2890·3) per 100 000 for high SDI. INTERPRETATION: A significant health burden was imposed by mental disorders in all countries and territories in 2023, irrespective of the health resources available. In some instances, this burden has increased over time and is unevenly distributed across populations. Stronger surveillance systems, particularly in low-income and middle-income countries, are required. Additionally, we need more coordinated and inclusive policies to reduce the burden through early treatment and prevention, tailored to sex and age differences across locations. Responding to the mental health needs of our global population, especially those most vulnerable, is an obligation, not a choice. FUNDING: Gates Foundation, Queensland Health, and University of Queensland.
25 May 2026
Read appraisal →International journal of circumpolar health
Artificial intelligence/machine learning in Alaska Native healthcare systems: symposium perspectives.
Artificial intelligence and machine learning (AI/ML) utilisation in Alaska Native and American Indian (AN/AI) healthcare presents an opportunity to address persistent health disparities and improve the well-being of Alaska Native communities. The 2025 Artificial Intelligence in Alaska Native Health Care Systems symposium occurred in Anchorage, Alaska, with 31 individuals participating in facilitated conversations between various stakeholders and specialists at the Alaska Native Tribal Health Consortium, Southcentral Foundation, Maniilaq Association and Stanford University. The overarching goal of the symposium was to bring together partners within the Alaska Tribal Health System (ATHS) to promote: (1) AN/AI peoples' guidance in support of Tribal sovereignty for AI/ML implementation within the ATHS, (2) expanded technical capacity and (3) centralised guidance and expertise to lead future AI/ML research and implementation with community-defined benefits. During the event, small group discussions focused on the following topics: (1) common AI/ML applications, (2) concerns surrounding AI/ML, (3) current or potential policies guiding AI/ML use and (4) how to build trust in AI/ML models. Themes discussed were data democratisation, AI/ML development and use needs to occur under the principles of data sovereignty, and AI/ML quality improvement and research are needed and distinct. The symposium was an important step in considering how to use AI/ML to optimise AN/AI health and well-being through collaborative partnerships and services.
25 May 2026
Read appraisal →Medicine
Tech-based evaluation of healthcare quality during the COVID-19 pandemic
The coronavirus disease 2019 pandemic profoundly disrupted global health systems, exacerbating preexisting inequalities in access to and quality of care. Disparities in service accessibility, continuity, coordination, and comprehensiveness highlight the need for a multidimensional, data-driven approach to healthcare evaluation. We conducted a retrospective analysis using 52,490 responses from the 2021 Global Burden of Disease COVID-19 Health Service Disruption Survey. We developed a 4-dimensional evaluation framework aligned with World Health Organization principles for people-centered care. Following variable selection, standardized preprocessing, and manual labeling, we trained and assessed 6 machine learning (ML) algorithms. We further optimized the 2 best-performing models - support vector machine (SVM) and random forest (RF) - using interpolation-based data augmentation, cross-validated hyperparameter tuning, and particle swarm optimization. SVM showed the highest baseline performance (accuracy 0.93), whereas RF achieved competitive macro-F1 scores. Interpolation-based data augmentation improved the generalizability of the SVM model, increasing its accuracy to 0.96. The RF model benefited most from particle swarm optimization-based hyperparameter optimization. Model predictions revealed persistent inequalities in healthcare service quality: respondents with lower income, lower educational attainment, and rural residence were disproportionately classified as receiving lower-quality services, whereas urban residence and higher income were associated with higher-quality care. Our scalable ML framework successfully assessed healthcare service quality during the pandemic, with optimized SVM and RF models demonstrating robust performance on high-dimensional, imbalanced survey data. Post-pandemic recovery strategies must prioritize socioeconomically disadvantaged populations. Furthermore, integrating ML tools can enhance real-time quality monitoring within health systems.
24 May 2026
Read appraisal →Studies in health technology and informatics
Training Future Health and Information Technologies Professionals on the European Health Data Space.
The European Health Data Space (EHDS) furthers the EU's digital health transformation by enabling secure access to electronic health data by facilitating environments for health data sharing. The topic of EHDS is relatively new and remains underrepresented in most academic curricula in both medical and informatics fields. This study evaluates the impact of a structured educational intervention to improve awareness, understanding, and preparedness among future health and information technology professionals to engage with EHDS-related systems and policies. Conducted at the University of Oulu, the intervention included training sessions, practical examples, and interactive discussions on topics such as the primary and secondary uses of health data, legal frameworks, and technical infrastructure. The survey's results showed significant gains in students' conceptual understanding of EHDS, particularly in distinguishing between primary and secondary uses of health data. We found that students with a European background reported slightly higher familiarity with EHDS-related rights and frameworks. The findings underscore the importance of embedding EHDS education in academic programs to build a digitally competent, informed workforce capable of supporting EHDS implementation. This intervention contributes to the portfolio of learning materials, developed by the SUSA project to integrate digital health policy and governance into multidisciplinary higher education curricula across the EU.
24 May 2026
Read appraisal →PloS one
Audit data governance for disability-inclusive public services: A systematic review and integrative S-A-C framework
This study consolidates interdisciplinary evidence on how audit data governance shapes accountability and service equity in data-intensive public services, with specific sensitivity to disability-inclusive delivery. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, we conducted a protocol-driven systematic literature review of peer-reviewed studies and selected institutional and standards sources published between 2010 and 2025. We searched Scopus and complemented database retrieval with targeted searches of audit, digital government, accessibility, and data-governance portals. We applied a theory-led narrative synthesis to accommodate heterogeneity in designs, domains, and outcome reporting. Synthesizing 125 records, we find that governance effects are produced through three interacting dimensions-service delivery for people with disabilities (S), audit data governance (A), and data-driven culture (C)-that jointly condition transparency, contestability, and distributive impacts. Service delivery arrangements shape where data are generated, which encounters are recorded, and whether access needs, reasonable accommodations, and complaints become organizationally legible. Audit data governance conditions the credibility and usability of audit trails through provenance, metadata, retention, audit logging, and role-appropriate oversight access, shaping both oversight feasibility and privacy risk. Data-driven culture conditions how evidence is interpreted and acted upon, including data literacy, the standing of experiential evidence, and whether audit signals support learning or only compliance. Across the corpus, recurring tensions emerge between optimization and inclusion; transparency and privacy; standardization and reasonable accommodation; automated decisioning and meaningful contestability; and compliance-oriented auditing and learning-oriented governance. Drawing these mechanisms together, we propose an integrative framework linking S, A, and C to explain how auditability and disability inclusion are co-produced across the digital service lifecycle. The framework identifies leverage points for governance reform and evaluation and outlines implications for future empirical testing and for practitioners seeking trustworthy, oversight-ready, inclusive digital public services. A review protocol was prepared before the review; the review was not registered.
23 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
The Public Health Impacts of Flavored Cigar Bans: A Systematic Review and Meta-Analysis
INTRODUCTION: This review summarizes research on the public health impacts of flavored cigar bans, which are policies that restrict the sale of cigars containing characterizing flavors. METHODS: A systematic literature search was conducted using PubMed, Scopus, PsycINFO, Cumulative Index to Nursing and Allied Health Literature, and Applied Social Sciences Index and Abstracts. Studies were included if they evaluated the impact of a real-world or hypothesized flavored cigar ban. The primary outcome was tobacco use behavior. Secondary outcomes included retailer compliance, sales, and the tobacco industry response to flavored cigar bans. Two reviewers independently screened articles for inclusion, extracted data, and assessed study quality. We also conducted a meta-analysis using random effects models to pool estimates on tobacco use behavior after a flavored cigar ban. This review was registered with PROSPERO (CRD42023481603). RESULTS: Of 2,399 articles identified in the preliminary search, 55 were included in the review. Many real-world studies found declines in cigar use post-ban, specifically among youth. However, other studies found no significant changes in cigar use. Among studies examining sales, there were declines in flavored cigar sales but increases in sales of non-flavored cigars and other tobacco products. Among studies assessing hypothesized tobacco use behavior, pooled results indicate that 8% (95% CI: 2%, 27%) of flavored cigar users intend to quit tobacco altogether, 45% (95% CI: 39%, 50%) would switch to non-flavored cigars or cigarettes, and 41% (95% CI: 32%, 51%) would switch to other tobacco products. CONCLUSIONS: Flavored cigar bans may reduce cigar use and improve public health when policies are comprehensive and well-enforced. IMPLICATIONS: Overall, findings from this review suggest that flavored cigar bans may reduce tobacco use and have the potential to improve public health, but not all research is consistent. Several studies examining the impacts of flavored cigar bans on tobacco use found a decrease in cigar use among youth, but some found that there was no significant change in cigar use. Research on policy impacts among adults is limited. To maximize intended policy impact, monitoring of retailer compliance and the tobacco industry's response to flavored cigar bans is needed.
23 May 2026
Read appraisal →BMJ open
Australian key stakeholder views regarding implementation of atrial fibrillation screening: a qualitative evaluation
OBJECTIVE: To understand the issues impacting atrial fibrillation (AF) screening and what needs to be considered for a successful national screening programme in Australia. DESIGN: Qualitative design using semistructured interviews and thematic analysis. SETTING: Australian Health. PARTICIPANTS: Six broad stakeholder groups were identified: charities/patient support, healthcare providers, professional bodies, government, research (including Indigenous health) and industry. METHODS: Semistructured interviews were conducted with 25 representative participants. Iterative thematic analysis was used. Coding was driven by the research questions (the current context; is a national screening programme warranted and approaches to a national screening programme) and an inductive approach where novel groupings of information were identified. FINDINGS: The key findings are grouped into four areas. (1) Current opportunistic general practitioner-led screening is ad hoc and fragmented. ISSUES: poor remuneration; lack of health sector collaboration and prioritisation; consumers lack awareness. (2) Systematic screening of all in-scope patients not considered feasible and concerns over lack of evidence. (3) Alternative approaches to increase screening include innovative approaches inside and outside general practice and use of smart technology. (4) Recommendations: (a) Support general practices and address remuneration and workflows; (b) Ensure a clear pathway to treatment; (c) Address data security, management and integration and sensitivity issues with wearable devices; (d) Promote collaboration between key organisations; (e) Address research gaps and (f) Generate culturally appropriate consumer education to promote consumer demand. CONCLUSIONS: Most stakeholders were broadly supportive of AF screening but agreed that current approaches were fragmented and not sufficient. If the forthcoming research evidence supports screening effectiveness on major outcomes, stakeholders envisaged a semi-systematic approach tailored to specific health settings, rather than a formalised systematic national screening programme.
23 May 2026
Read appraisal →JMIR formative research
Patients' mHealth Apps Usage and Data Privacy, Security, and Confidentiality Concerns: Exploratory Study
BACKGROUND: The Technology Adoption Model (TAM) offers a potential framework for elucidating the relationships between data privacy or security concerns and behavioral intention, perceived usefulness (PU), and perceived ease of use (PEOU) of mobile health (mHealth) apps, particularly for patients' self-care management. In Saudi Arabia, limited information is available on these pertinent research areas despite the government's relentless efforts to bolster the use of mHealth apps. OBJECTIVE: This study applies the TAM and the psychosociocultural framework to explore the influence of patients' data privacy and security concerns on the PU, PEOU, and behavioral intention to use mHealth apps for self-care management in Saudi Arabia. METHODS: A cross-sectional study was conducted by recruiting patients using mHealth apps for self-care from various provinces in Saudi Arabia. Research instruments were developed based on the components of 2 theories: the psychosociocultural framework and TAM, which were then piloted, validated, and distributed to participants via Google Forms. Linear regression models were performed to test the hypothesized relationships. RESULTS: Overall, 567 patients using mHealth apps participated in the study. Slightly more than one-third (217/567, 38.2%; range 35.6%-41.4%) of the participants expressed a high level of concern regarding data privacy, confidentiality, and security, with significant predictors being female gender, higher educational qualifications, and younger age groups (<46 years). About 18% to 25% of the variance in PU, PEOU, and behavioral intention to use mHealth apps was explained by the tested factors. Patients were more likely to have higher PU following a unit decrease in data confidentiality (β=.31; P=.01) and security concerns (β=.47; P=.01). The PEOU of mHealth apps increased as users demonstrated less concern regarding data privacy (β=.18; P=.001), confidentiality (β=.24; P<.001), and security (β=.43; P=.02). Likewise, behavioral intention to use mHealth apps also increased significantly following a reduction in respondents' concerns toward data privacy (β=.18; P=.02), confidentiality (β=.24; P=.03), and security issues (β=.36; P=.01). CONCLUSIONS: Specific demographic factors and concerns regarding data security and privacy influence patients' PU, PEOU, and behavioral intention to use mHealth apps for self-care management. Targeting the age-, education-, and gender-based differences regarding the usage of mHealth apps. Health care providers and policymakers may consider age-, education-, and gender-based differences when developing strategies to improve the adoption of mHealth apps among the Saudi patient population.
22 May 2026
Read appraisal →Health policy and planning
Partial pictures: what routine health data can and cannot tell us about the quality of maternal and neonatal health services in Tanzania
Measurement of contact coverage of maternal and newborn health services may overestimate the benefits of the care that is provided, because the quality of care is not captured. It is therefore important to better understand the opportunities and challenges of capturing elements of care quality in routine health information systems (RHIS). This study explored healthcare workers' (HCWs), health sector managers' and policymakers' perspectives on the value and limits of routine health data to understand the quality of maternal and neonatal health services in Tanzania. We conducted qualitative research during two periods to capture perspectives across facility, district, regional and national levels. In Mtwara region in 2023, we conducted ethnographic observations at two hospital labour wards and 29 in-depth interviews with healthcare workers, hospital leaders, and district/regional managers. In 2025, we carried out 17 additional interviews with regional managers in Mtwara and with key national-level stakeholders. Our findings demonstrate that Tanzania's RHIS provides a valuable but partial picture of quality of care for maternal and neonatal health services. Care processes-including both provision and experience of care-are captured only to a limited extent. Using boundary object theory, we highlight how the same health information must serve diverse stakeholder needs. While there are opportunities to integrate more quality-of-care indicators, standardized RHIS cannot be expected to comprehensively capture the multifaceted nature of care quality. For quality measurement to support meaningful local health service improvement, a flexible, bottom-up approach is essential. However, the current emphasis on top-down oversight acts as a barrier for local-level data use. Relevant and feasible measurement of the quality of facility-based care requires a fundamental shift in current RHIS priorities-from systems that extract data for those 'up there' to platforms that also create value for those who provide care.
21 May 2026
Read appraisal →Health policy and planning
Pain, policy, and the opioid dilemma in sub-Saharan Africa: a systematic review of access, regulation, and health governance from 1990 to 2024
Sub-Saharan Africa (SSA) faces a dual opioid dilemma: widespread undertreatment of moderate to severe pain due to limited access to essential opioid analgesics, alongside increasing concern about misuse and diversion in selected contexts. Opioids are clinically indicated for severe cancer-related pain, advanced chronic illness, major trauma, surgery, and end-of-life care; yet, regional consumption remains disproportionately low relative to documented disease burden, indicating systemic under-provision rather than low need. This systematic review synthesizes evidence on opioid policy, regulation, access, and governance in SSA to examine how legal frameworks, institutional arrangements, political dynamics, and data systems shape medical availability. Following PRISMA and SWiM guidance, we reviewed peer-reviewed and gray literature addressing national drug control laws, regulatory implementation, procurement and supply systems, prescribing authority, and surveillance capacity; 33 studies met inclusion criteria. Across settings, restrictive or ambiguously interpreted legislation, multi-layered administrative controls, fragmented mandates across health and enforcement institutions, weak forecasting and distribution systems, concentrated prescribing authority, professional risk aversion, and chronic data gaps were consistently associated with constrained access. Concurrently, rising political and media attention to non-medical use, particularly of tramadol, has reinforced enforcement-oriented narratives that may further limit reform space. Persistent deficiencies in routine consumption data and unmet need assessment contribute to conservative import quotas and regulatory inertia. Addressing this imbalance requires proportionate, sequenced reform that strengthens data and forecasting systems, clarifies and aligns legal mandates with public health objectives, invests in workforce capacity and supply chains, and embeds safeguards against diversion while correcting avoidable under-treatment.
21 May 2026
Read appraisal →Scientific reports
Analysis, control, and forecasting the dynamics of SIRD models with saturated treatment and nonlinear incidence
This study addresses the challenge of managing complex epidemic dynamics exhibited by a novel Susceptible-Infected-Recovered-Deceased (SIRD) model. The introduced model incorporates a biologically and behaviorally mediated nonlinear incidence rate alongside a saturated treatment function, realistically capturing both physical transmission risks and the human behavioral responses during an outbreak. Our comprehensive analysis establishes the mathematical rigor of the system through positivity, boundedness, and a detailed investigation of equilibrium points and bifurcations (transcritical, saddle-node, and Hopf), which reveal the potential for complex, oscillatory, and chaotic epidemic patterns. To mitigate the disease burden, we apply Pontryagin's Maximum Principle to derive optimal time-dependent prevention and treatment control strategies. Furthermore, we introduce a highly efficient machine learning framework based on a Logistic-Map Reservoir Computer (LMRC) for accurate, real-time forecasting of these complex dynamics. Numerical simulations validate the theoretical findings and reveal the model's rich dynamical behaviors, including stable limit cycles and chaotic regimes driven by parameter variations. The integrated analytical, control, and machine learning approach provides a robust toolkit for public health policymakers, offering data-driven insights crucial for designing adaptive, cost-effective interventions that directly support the achievement of Sustainable Development Goal 3 (Good Health and Well-being).
21 May 2026
Read appraisal →Journal of medical Internet research
Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight
Artificial intelligence (AI) in health care is increasingly defined not by static algorithms but by adaptive intelligence-systems that evolve over time through interactions with data, clinicians, and clinical environments. This adaptive capacity creates a structural mismatch with regulatory frameworks built for technologies whose behavior remains static. As AI models drift, recalibrate, or degrade in real-world contexts, they dissolve the linear boundaries between design, deployment, and clinical interpretation. These temporal, epistemic, and organizational frictions expose responsibility gaps that cannot be resolved through incremental modifications to legacy oversight structures. Regulators across major jurisdictions are beginning to respond to these challenges, though with differing orientations. The United States advances mechanisms for predictable adaptation, including Predetermined Change Control Plans, real-world evidence frameworks, and life cycle-oriented quality management reforms. The European Union emphasizes precautionary, rights-based governance through the European Union Artificial Intelligence Act (AI Act) and modernized liability rules. South Korea, operating within a hyperconnected digital health ecosystem, has introduced the Digital Medical Products Act (DMPA), one of the world's first comprehensive statutory frameworks for learning medical AI. Despite philosophical differences, these regulatory trajectories converge on a shared insight: learning AI systems cannot be governed by static rules or episodic evaluation. This viewpoint proposes Co-Lifecycle Governance as a conceptual framework to synchronize regulatory oversight with adaptive intelligence. Rather than treating oversight as a discrete event, Co-Lifecycle Governance frames regulation as a continuous, synchronized process grounded in 4 pillars: continuous validation, agile change management, proactive performance surveillance, and distributed accountability. Each pillar functions as a structural antidote to the responsibility frictions that arise when AI systems evolve faster than expectations surrounding them. Together, these pillars provide a governance grammar capable of supporting safe, iterative model improvement while maintaining system-level trust. Drawing from the strengths of US predictability, European Union accountability, and Korean scalability, this paper outlines a hybrid convergence pathway that synthesizes predictability, accountability, and operational feasibility. Learning AI will not wait for governance to catch up; oversight must evolve in lockstep with adaptive intelligence. Co-Lifecycle Governance offers a foundation for regulatory systems that not only regulate learning AI but also learn with it-at the speed at which adaptive intelligence actually changes.
20 May 2026
Read appraisal →PloS one
A computational text analysis of recent African digital health strategies and their attention to vulnerable populations
BACKGROUND: Digital health offers opportunities to transform healthcare systems and improve access to care, particularly in low- and middle-income countries. However, there is concern that national digital health strategies may inadequately address the needs of vulnerable populations, risking the reinforcement of existing health inequalities. OBJECTIVE: This study investigates the extent to which recent African national digital health strategies consider and prioritize vulnerable populations particularly at risk of digital exclusion (women, children and the elderly). METHODS: We conducted a computational text analysis of 13 recent national digital health strategy documents from African countries to identify major topical priorities within the strategies and to systematically assess the frequency and context of references to vulnerable populations. RESULTS: Most strategies focus on broad goals such as health system strengthening, infrastructure development, and stakeholder coordination. While there are mentions of women and children, these references are often indirect and not accompanied by concrete plans or dedicated actions. References to the elderly are especially rare. Few strategies include specific measures to ensure digital inclusion or equity for these vulnerable populations. CONCLUSIONS: Recent African digital health strategies currently prioritize systemic and infrastructural development, but the needs of vulnerable populations are often overlooked or superficially addressed. To prevent the digital divide from widening, national strategies should embed explicit equity targets, actionable plans, and mechanisms for meaningful inclusion and participation of all population groups.
20 May 2026
Read appraisal →Disaster medicine and public health preparedness
Hospital Medical Surge Capacity Assessment and Planning Tools for Disaster Response: A Systematic Review and Meta-analysis
BACKGROUND: Hospital surge capacity is pivotal for disaster response, yet the effectiveness of assessment and planning tools remains underexplored. OBJECTIVE: This systematic review evaluates the reliability, validity, and applicability of surge capacity tools across disaster scenarios. METHODS: Following PRISMA guidelines, we analyzed 24 Q1 studies (PubMed, Scopus) using the PICO framework. Risk of bias was assessed via ROBINS-E, and meta-analysis (random-effects model) quantified pooled effect sizes (ES). Subgroup analyses compared simulation tools, surveys, and resource strategies. RESULTS: The combined ES was 0.74 (95% CI: 0.67-0.82), indicating moderate improvement in preparedness. Simulation tools (Group A) showed the highest consistency (ES = 0.81, I2 = 0%), while resource strategies (Group C) had wider variability (ES = 0.71, I2 = 74.42%). Egger's regression revealed publication bias (P = 0.004), and heterogeneity was substantial (I2 = 76.70%). CONCLUSIONS: Simulation-based tools are most effective for surge capacity, whereas resource strategies require contextual adaptation. Policymakers should prioritize standardized benchmarks and DSS integration, with future research addressing equity and real-world implementation gaps.
20 May 2026
Read appraisal →Computer methods and programs in biomedicine
Prompt-to-policy: Leveraging large language models to guide deep reinforcement learning in public health emergencies
Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncertain conditions. Traditional optimization methods often struggle with computational complexity and real-time, heterogeneous data. To address these challenges, we propose a hybrid intelligent agent combining an enhanced Double Deep Q-Network (D2QN-JDA) with large language models (LLMs). The D2QN-JDA improves learning stability and adaptability through joint state-action inputs, dynamic exploration rate, and adaptive reward normalization. The LLM component uses Retrieval-Augmented Generation (RAG) to integrate structured and unstructured data for real-time decision support. Experiments based on data from the Hong Kong COVID-19 outbreak show that the D2QN-JDA outperforms dynamic programming, greedy algorithms, genetic algorithms, and Q-learning, achieving reductions in cost. The LLM component also outperforms manual and regex methods in both single- and multi-point data extraction, enhancing accuracy, recall, F1 score, cost, and time. Our framework effectively addresses complex, multi-objective resource allocation in public health crises.
17 May 2026
Read appraisal →BMJ open
Transitioning from paper-based to electronic health management information systems in Africa: a scoping review protocol
INTRODUCTION: The rising shift from paper-based to electronic health management information systems (EHMIS) among global health systems has shown promising strides over the past decade. Yet, most African health systems have continued to use paper records with attendant gaps and challenges. Most African governments have now started transitioning from paper to EHMIS but lack adequate guidance to support this shift. There is therefore a need for harmonised regional guidance to ensure that such transitions are carried out systematically and take into account country-specific contexts. OBJECTIVE: The objective of this study protocol is to conduct a scoping review to generate evidence that will inform the development of a comprehensive guide to support countries in the WHO African Region in transitioning from paper-based to EHMIS. METHODS AND ANALYSIS: The review will follow established methodological guidance for scoping reviews as outlined by Arksey and O'Malley and further refined by Levac et al and the Joanna Briggs Institute, with reporting guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. A search strategy will be developed to systematically identify relevant studies from both published and grey literature sources including PubMed, Cochrane Library, Scopus and African Index Medicus. Other sources will include Google Scholar, Emerald Journal, the WHO Regional Office for Africa Library and websites of WHO, ITU and Ministries of Health. Reference lists of the retrieved published articles will be manually searched to identify additional relevant studies. Descriptive qualitative content analysis will be undertaken using the policy analysis framework and key findings will be summarised and presented using tables, charts and maps. ETHICS AND DISSEMINATION: This study does not involve the collection of primary data; therefore, ethical approval will not be required. On completion of the study, findings will be submitted for publication in a peer-reviewed scientific journal and will also be presented at national, regional and international conferences to support knowledge sharing and policy engagement.
16 May 2026
Read appraisal →The British journal of general practice : the journal of the Royal College of General Practitioners
Which transitions count? A systematic hermeneutic review of the literature on transitions of care.
BACKGROUND: Transitions of care are defined as critical moments along the care continuum for patients as they navigate changes in their health status or level of care. Researchers suggest that the existing research on transitions of care is largely descriptive and lacking in theoretical depth, which limits its application to different clinical contexts. AIM: We therefore aim to review the existing literature and theoretical frameworks on transitions of care, with a focus on primary care settings. METHOD: We undertook a systematic hermeneutic review - an approach that emphasises analytical reading, including identifying key concepts and building interpretations across a large and heterogeneous body of literature. Our initial search yielded 1640 articles, and we included 54 sources in our review. RESULTS: Despite interdisciplinary discussions, the main narratives of transitions of care centered around hospitals, with limited consideration for transitions in community settings. Moreover, using re-hospitalisation admission rates as the primary metric to measure the effectiveness of transitions of care initiatives fails to capture the negotiations required by patients and staff. This highlights the undercurrent of a positivist and biomedical approach, and the influence of broader health policy in shaping the methods and narratives used to define transitions. CONCLUSION: We consider this gap pertinent, given the increasing complexity of care delivered in community settings through remote chronic care models. In response, we suggest further engagement with theory to deconstruct our focus on settings and re-conceptualise transitions of care, which are also marked by changes in practices, materials, and people.
16 May 2026
Read appraisal →JMIR mental health
Adoption of Digital Mental Health Interventions in National Health Service England, Scotland, and Wales: Freedom of Information Questionnaire Study
BACKGROUND: Digital mental health interventions (DMHIs) have been widely promoted to improve access to mental health care within the UK National Health Service (NHS), particularly following the COVID-19 pandemic. In 2015, a total of 48 technologies were reportedly used in NHS services in England, but over the past decade, substantial changes to regulatory requirements, evidence standards, and procurement processes have reshaped the digital mental health landscape. There is limited clarity regarding which DMHIs are currently being formally procured and funded by NHS mental health services across the United Kingdom. OBJECTIVE: This study aimed to identify and describe the DMHIs currently procured, contracted, or paid for by NHS mental health service providers in England, Scotland, and Wales for adult common mental health problems and to compare current procurement practices with findings reported in 2015. METHODS: Freedom of Information requests were submitted to all NHS mental health trusts in England and all health boards in Scotland and Wales. Responses were collated and screened to provide an updated and extended record of which technologies are reportedly procured or paid for by services. RESULTS: In total, 19 different DMHIs were identified as being procured across mental health service providers for adult common mental health problems at the time of data collection. This demonstrates a substantial reduction in the number of technologies being adopted into practice compared to the 48 reported in England in 2015. The findings reveal several key insights, including that only 2 technologies have remained in use for a decade, and they shed light on the types of technologies being selected and the variations in procurement practices among the 3 national health services. CONCLUSIONS: Despite the expansion of the digital mental health marketplace, the number of DMHIs formally procured by NHS mental health services has markedly decreased over the past decade. This consolidation may reflect increased selectivity and the adoption of higher-quality products, driven by strengthened regulatory oversight, evidence standards, and national guidance. Although these developments may enhance safety and quality assurance, they also raise important questions about innovation, market sustainability, and equitable access to digital mental health care. Ongoing monitoring of procurement practices is needed to inform policy, service design, and the future development of DMHIs.
16 May 2026
Read appraisal →Journal of medical systems
When Chatbots Become Agents: The Next Phase of Healthcare AI
Healthcare artificial intelligence (AI) has moved beyond answering medical questions. In early 2026, OpenAI, Anthropic, and Google launched agentic systems that retrieve evidence, use tools, and execute multi-step workflows. These systems can integrate information across multiple clinical knowledge domains within a single workflow, but the evidence base for evaluating their reliability in clinical practice has not kept pace with deployment. This perspective argues that healthcare is adopting a technology evolving faster than anyone can evaluate, and that a brief window exists to shape integration before the architecture becomes entrenched. These systems share common architectural principles, yet only 19 prospective trials exist among over 4,600 clinical large language model studies, none evaluating agentic pipelines. The architecture is locking in, professional roles are being redefined, and builders hold a structural information advantage. Transparency, adaptive governance, and broader participation are needed before these choices become irreversible, and to ensure that the boundary between human clinical judgment and automated capability is drawn in a way that strengthens both. Every stakeholder in healthcare must engage with this shift now.
15 May 2026
Read appraisal →PloS one
Framing artificial intelligence in Chilean digital press before and after the launch of ChatGPT: From concern to optimism
This article examines the evolution of AI framing in Chilean national digital media before and after the launch of ChatGPT in 2022. Through topic modeling and emotion analysis of 1,466 articles published by six media outlets over periods that begin between 2000 and 2014, depending on the outlet, and extend in all cases until 2024, this study identifies significant thematic and affective changes. In the pre-ChatGPT period, coverage combined recognition of AI's capabilities with concerns regarding labor displacement, governance, and human identity, frequently referencing global corporations and state institutions. In the post-ChatGPT period, topics became narrower in scope, with fewer actors and greater emphasis on universities and cultural organizations implementing AI. The focus shifted from the development of AI systems to their applications, predominantly framed in positive terms. At the same time, negative sentiment was largely confined to epistemological uncertainty. This transformation aligns with national surveys indicating growing public optimism, diverges from Global North debates centered on regulation and privacy, and converges with East Asian framings that are oriented toward innovation and creativity. These findings suggest that the launch of ChatGPT coincided with a reinforcement of a socio-technical imaginary of AI as inevitable and beneficial, yet potentially limiting the diversity of public debate.
14 May 2026
Read appraisal →The New Zealand medical journal
The impact of cultural concordance between health professionals and patients: a narrative review
AIM: The aim of this article is to summarise international evidence on the impacts of cultural/ethnic concordance between patients and health professionals and to discuss the implications for New Zealand. METHOD: Database searches were conducted from August to September 2025 using Scopus, Embase and PubMed, using key terms related to health outcomes and cultural/ethnic concordance. RESULTS: A total of 25 relevant systematic reviews, meta-analyses and research studies were included for analysis. Overall, evidence is predominately from the United States of America and findings are variable. Cultural concordance is associated with improved communication quality, trust, satisfaction and perceived respect. Concordance is also associated with improved healthcare utilisation, medication adherence and uptake of preventative interventions. Some studies found concordance was associated with differences in clinical decision-making. Impact on clinical outcomes is mixed, with some studies finding concordance has a positive impact on caesarean section rates, diabetes management, surgical outcomes and addiction treatment, while others found no impact. CONCLUSION: Cultural concordance between patients and their health professionals matters, with evidence of impact on patient experience, communication quality, engagement and adherence, clinical decision-making and some clinical outcomes. Findings are inconsistent: the strength of association and groups which see the greatest benefit vary between studies. To reduce health inequities, we need to increase the diversity of the workforce and train all health professionals to provide non-racist, equitable care.
12 May 2026
Read appraisal →Therapeutic innovation & regulatory science
Applications and Challenges of Artificial Intelligence and Big Data in Drug Regulation
Artificial intelligence (AI) and big data are increasingly applied in drug regulation and have demonstrated significant potential worldwide. The U.S. Food and Drug Administration (FDA) has developed a relatively comprehensive approach through strategic frameworks, regulatory guidelines, and pilot programs. The European Medicines Agency (EMA) has promoted AI adoption via the Big Data Task Force, DARWIN EU®, and a multi-annual work plan, while Japan, Canada, and other countries have also advanced relevant initiatives and strengthened international cooperation. In China, smart regulation has been incorporated into the "14th Five-Year Plan" and subsequent strategies, with progress in establishing national regulatory data platforms, pharmaceutical traceability systems, and pilot AI applications. Nevertheless, AI in drug regulation remains at an exploratory stage, facing challenges such as limited model reliability and interpretability, insufficient data standards and interoperability, regulatory gaps, and ethical as well as public trust concerns. Future progress will depend on strengthening regulatory standards, enhancing data governance, improving regulatory capacity, and deepening international collaboration to achieve more scientific, intelligent, and efficient drug regulation.
9 May 2026
Read appraisal →Healthcare management forum
Navigating Privacy in Health Data Sharing: A Patient-Centric Approach to Health Information Exchange
Personal Health Information (PHI) sharing through Health Information Exchange (HIE) enhances patient safety in Canada, yet not all provinces and territories voluntarily disclose PHI on safety incidents to federal and pan-Canadian surveillance systems. A frequently cited barrier by healthcare organizations for HIE between different interoperable health databases is patients' and families' concerns for their privacy. This explorative qualitative study examined patients' and families' attitudes toward PHI sharing, including its secondary use in patient safety events. Rather than expressing reluctance, participants described support for HIE when privacy safeguards, such as defined sharing purposes and anonymous formats, were in place. These findings present a significant opportunity for health leaders and data custodians to use the research findings to create a patient-centric framework for the HIE of PHI.
6 May 2026
Read appraisal →PloS one
Auditing the impact of social media's policy shift on anti-vaccine discourse: A large language model-driven empirical study
The sudden termination of X's (formerly Twitter) misinformation policy on November 23, 2022, provides an opportunity to assess the effects of lifting content moderation restrictions on vaccine-related discourse. This study examines changes in the prevalence, thematic composition, and engagement of anti-vaccine discourse following X's policy shift, analyzing tweets from a seven-day period before and after the policy termination (November 16-30, 2022), excluding the announcement date itself from regression analyses. Using GPT-4o for stance classification, thematic categorization, and stance consistency assessment, with validation through external benchmarks and cross-annotator agreement, we find that anti-vaccine tweets increased significantly post-policy (OR = 1.60, 95% CI: 1.50-1.72), particularly via retweets, suggesting content amplification. Sensitivity analyses excluding highly retweeted content revealed that the policy change was also associated with increased creation of new anti-vaccine content. Thematically, health concerns over vaccination became more prominent, while conspiracy-related and anti-mandate narratives declined in relative prevalence. Stance consistency in quote tweets increased, indicating reinforced ideological alignment in anti-vaccine discourse. These results suggest that content moderation policies may constrain both the volume and amplification of anti-vaccine content, with policy removal associated with rapid shifts in discourse patterns.
3 May 2026
Read appraisal →European respiratory review : an official journal of the European Respiratory Society
Rural versus urban living and COPD: a systematic review
Chronic obstructive pulmonary disease (COPD) is a leading cause of global morbidity and mortality. Emerging evidence suggests disparities in COPD outcomes between rural and urban populations, but no prior review has synthesised these differences globally. Five databases (Medline, Embase, Emcare, CINAHL and Cochrane Central) were searched in May 2025. Eligible peer-reviewed studies directly compared rural and urban populations in at least one of four measures of COPD burden: prevalence, symptom burden, exacerbations or mortality. Study quality was assessed and narrative synthesis was conducted due to heterogeneity in outcome measures. Of 1339 screened studies, 32 met inclusion criteria, spanning 13 countries. COPD prevalence was higher rurally in 83% (15/18) of studies, with 11/15 demonstrating statistical significance. This pattern was consistent across geographical distributions. Total exacerbation rates were higher rurally in 60% (3/5) of studies, although hospitalisations varied significantly. 50% (6/12) of studies reported higher hospitalisation rates in urban areas and 5/12 studies reporting higher rates in rural areas. 86% (6/7) of studies demonstrated higher mortality rurally and symptom burden was higher amongst rural residents in 67% (4/6) of studies; however, the majority of these were conducted in the USA. This review highlights consistent rural-urban inequalities in COPD prevalence and outcomes, reflecting the impact of healthcare inequities, socioeconomic deprivation and environmental exposures on COPD burden in rural areas. Targeted interventions promoting equitable healthcare access, health education, transition to cleaner fuels and rural access to smoking cessation and pulmonary rehabilitation services are essential to mitigate these disparities and improve outcomes in rural populations.
3 May 2026
Read appraisal →JMIR formative research
Comparing Perceptions of ChatGPT Use in Health Attitude Contexts Among Users and Nonusers: Cross-Sectional Study
BACKGROUND: In light of the growing use of artificial intelligence (AI) in health care, individuals' access to and use of health information are transforming. ChatGPT, an AI chatbot, provides immediate responses to health queries, with the potential to influence health-related attitudes, thereby raising concerns related to privacy, reliability, and security. OBJECTIVE: This study aimed to investigate the perceived usefulness, risks, anxiety, and social influence of ChatGPT on health attitudes among users and nonusers in Saudi Arabia. METHODS: A cross-sectional study was conducted using an online survey based on a validated tool. In total, 337 participants aged 18 years and older responded to questions assessing their perceptions of ChatGPT on health-related attitudes. RESULTS: Data showed that 76.1% (194/255) of the respondents used ChatGPT, with the majority being younger and more highly educated. The main uses for health-related purposes were health education (43/194, 22.2%) and physical activity guidance (31/194, 16%). The analysis showed that users considered ChatGPT useful for health-related decisions, with 45.9% (89/194) finding it easy to learn and use, but concerns about privacy (106/194, 54.7%) and reliability (87/194, 44.9%) remained. Among nonusers, security risks (39/61, 63.9%) were the major barrier to using AI-based tools for health purposes, and 68.9% (42/61) found such tools attractive and engaging. There were no statistically significant differences between users and nonusers across all examined sociodemographic characteristics (P>.05). CONCLUSIONS: The study established the potential of ChatGPT in improving health decision-making and revealed cultural, privacy, and trust issues that may affect its implementation. These findings underscore the importance of strengthening the security of AI-based applications to enhance public acceptability of related health policies and to support the safe integration of tools such as ChatGPT into the health care system.
3 May 2026
Read appraisal →JMIR public health and surveillance
Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward
Public health emergencies such as pandemics, natural disasters, and epidemics may require rapid, high-stakes decisions often made by elected officials with limited public health training. Artificial intelligence (AI) holds significant promise to enhance the quality, transparency, and timeliness of governmental decision-making during such crises. This paper examines the potential of AI as a decision-support tool for elected officials while identifying key technical, logistical, ethical, and policy challenges. Technical considerations include model accuracy, data representativeness, and privacy protection, while ethical imperatives center on fairness, transparency, and accountability to prevent amplification of existing health disparities. The paper further explores workforce development needs, emphasizing AI literacy and cross-sector collaboration to enable informed use of AI insights. This viewpoint presents a novel AI Decision Support Lifecycle framework specifically designed for governmental public health emergency response, mapping six phases from problem definition through post-emergency evaluation. We provide stakeholder-specific recommendations for model developers, health agencies, and elected officials, and illustrate practical application through a detailed case example and use cases. Drawing on empirical evidence regarding digital health technologies and AI governance, we emphasize that technology deployment alone is insufficient. Successful implementation requires complementary investments in organizational capacity, data infrastructure, workforce training, community engagement, and continuous evaluation. AI integration also requires robust governance frameworks, continuous model evaluation, and alignment with existing crisis management structures. Policy recommendations highlight the importance of ethical AI frameworks, risk assessments, and public engagement to foster trust. Ultimately, AI can strengthen public health decision-making if developed and implemented responsibly within transparent and equitable systems.
2 May 2026
Read appraisal →JAAPA : official journal of the American Academy of Physician Assistants
Barriers to hearing aid adoption in hearing loss
More than one-fifth of the US population has hearing loss, a condition associated with higher risk of falls, depression, isolation, cognitive decline, dementia, and all-cause mortality. Treatment of hearing loss has been shown to correlate with decreased risk of cognitive decline, dementia, and mortality, as well as an improved quality of life. Surprisingly, however, fewer than 1 in 10 Americans with hearing loss use hearing aids. As the prevalence and burden of hearing loss grow, clinicians must understand factors influencing hearing aid use to increase treatment rates. This article reviews key barriers to hearing aid adoption-including low hearing health knowledge, inadequate screening, stigma, cost, and difficulty of use-and discusses recent changes that may influence these barriers.
27 Apr 2026
Read appraisal →PloS one
Resilient road safety modeling through spatially disaggregated explainable AI
Understanding spatial disparities in traffic accident severity is essential not only for improving transport safety but also for advancing sustainable and inclusive transportation systems. Road crashes represent a persistent public health and equity challenge, directly linked to the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-Being and SDG 11: Sustainable Cities and Communities). This study proposes an explainable, spatially segmented machine learning framework to examine urban-rural heterogeneity in crash outcomes, using disaggregated accident data from Kent, UK (2022-2024). By treating urban and rural systems as separate analytical units, the study captures risk heterogeneity that conventional pooled approaches often obscure. Among five tested models, Random Forest achieved the best performance and was further interpreted using SHapley Additive exPlanations (SHAP) to uncover how key factors differ in influence across spatial contexts. The results reveal a behavioral risk profile dominating in urban areas and infrastructure-driven risks in rural environments. These findings highlight the need for context-sensitive, evidence-based interventions that ensure transport equity across regions. Additionally, the study contributes to sustainable governance frameworks, enabling spatially adaptive risk mitigation, inclusive policy design and the long-term resilience of transport infrastructures.
27 Apr 2026
Read appraisal →Journal of medical Internet research
Exploring Attitudes Toward AI-Based Contactless Sensors in Health Among Five Stakeholder Groups: Qualitative Study
BACKGROUND: The rapid rise of artificial intelligence-based contactless sensors (AI-CS) is expected to significantly transform how patients are measured, monitored, and understood through a versatile, noninvasive approach to data collection and health assessment. However, there is a lack of empirical research specifically focusing on AI-CS in health. Moreover, existing studies tend to focus on medical or patient perspectives, while neglecting other stakeholders such as researchers, political actors, or the general public. OBJECTIVE: The study aims to provide an in-depth empirical ethical analysis and, through a multistakeholder approach, a uniquely comprehensive overview by addressing the research question: what are the attitudes of different stakeholders (patients, health care professionals, researchers, political stakeholders, and the general public) toward AI-CS and their applications in health? METHODS: We conducted a cross-sectional study with 104 participants using a semistructured interview guide. Interviews were analyzed using qualitative content analysis with ATLAS.ti software (ATLAS.ti Scientific Software Development GmbH), following a 3-component model of feelings, thoughts, and behavioral aspects. RESULTS: The results of the study provide an in-depth analysis of attitudes toward AI-CS in health among different stakeholders. Overall, the results show a high level of openness to AI-CS in health across all stakeholder groups. In terms of feelings and their correlation with behavioral aspects, 2 key trends emerged: first, greater experience and knowledge correlated with a reduced tendency to react emotionally. Second, participants with positive experiences with technologies were generally more open and positive toward contactless sensors. The combined findings on thoughts and behavioral aspects highlighted 3 key tensions-around contact(lessness) and the importance and ambivalence of touch, between protection and surveillance (particularly regarding path- and context-dependency) and between the benefits and challenges of unobtrusiveness (especially in relation to control and governance implications). In addition, the analysis revealed the need for information and consent about AI-CS and clarified possible technical implementations and fields of application. CONCLUSIONS: This study provides a comprehensive and empirically grounded ethical analysis of stakeholder attitudes toward AI-CS in health. The findings offer valuable guidance for the responsible development, implementation, and governance of AI-CS in health care contexts.
27 Apr 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Cumulative access to print books improves literacy achievement: Evidence from a five-year randomized trial in high-poverty schools
For more than a century, studies have shown that children who grow up in homes with more books achieve higher levels of academic success, yet it remains unclear whether books themselves improve learning or simply reflect broader socioeconomic advantages. Skill-development theory holds that greater access to books directly improves literacy through increased print exposure and reading practice, whereas the cultural capital account suggests books are indicators of broader family resources, including parental education, academic norms, and enrichment opportunities, that promote achievement independently of the books themselves. To provide causal evidence, we conducted a school-level randomized controlled trial of a program that builds children's home libraries. In 2018, we randomly assigned 60 high-poverty public elementary schools to treatment or control groups. Students in 30 treatment schools received four book distributions over 5 y, averaging about seven books per distribution, and prominently including high-interest and culturally relevant titles; students in 30 control schools received none. Tracking students from 2018-19 through 2022-23, we find a statistically significant intention-to-treat impact on reading achievement of d = 0.100 and a larger d = 0.207 advantage for those completing the full 5-y program. These impacts correspond to approximately 25 to 32% and 52 to 65% of a typical year's learning, respectively. The largest benefits are concentrated among students who received books across all distributions, indicating that cumulative exposure drives the strongest impacts. These findings provide evidence supporting skill-development theory and highlight a scalable strategy for improving literacy outcomes in high-poverty urban schools.
26 Apr 2026
Read appraisal →Biomedizinische Technik. Biomedical engineering
AI in healthcare: mapping Romania's transition from research to intellectual property & industry solutions
OBJECTIVES: Romania is aligning its healthcare AI ecosystem with the European Union's AI Act (2024/1689), through its own National AI Strategy. Despite a growing volume of research in medical AI, significant gaps remain in translating this output into patents and domestically developed high-risk medical devices. This study assesses Romania's research productivity, patenting activity, and commercial solutions in medical AI. METHODS: We conducted an analysis combining bibliometric data, patent information, and medical device databases, and analyzed 619 Romanian-authored articles on medical AI, 272 patent records, and identified domestic AI-powered software providers. RESULTS: Romania's publication output in medical AI has surged post-2018, primarily driven by academic institutions in Bucharest and Cluj. Patents are predominantly filed by multinationals, indicating a potential disconnect between research and industrial output. Six AI medical software solutions were identified, most under the low-risk classifications. CONCLUSIONS: While Romania is expanding its medical AI research, it faces barriers in converting academic output into innovation. The dominance of foreign corporations in patent filings, reliance on international funding for high-impact research, and the scarcity of domestically developed high-risk medical AI solutions highlight important gaps. Addressing these disparities is essential for national alignment in the AI medical innovation space.
26 Apr 2026
Read appraisal →Journal of medical Internet research
Perspectives Regarding the Privacy, Security, and Confidentiality of Data Collected via mHealth Apps in Saudi Arabia: Qualitative Analysis
BACKGROUND: As Saudi Arabia aims to enhance the adoption and utilization of mobile health (mHealth) apps in the health care sector, it is vital to identify the potential challenges that may be faced in the process and how to address them. OBJECTIVE: This study aimed to explore patients' and stakeholders' perspectives regarding the privacy, security, and confidentiality of data collected via mHealth apps in Saudi Arabia. METHODS: A qualitative research design was used by conducting an in-depth interview with 25 participants, comprising health care workers (HCWs), patients, and mHealth app developers. The interview questions focused on awareness of mHealth apps and issues relating to data security, privacy and confidentiality, benefits of mHealth apps, challenges faced, and features that may improve the utilization of such digital health technologies. Thematic analysis was performed using NVivo software (version 12; QSR International). RESULTS: Patients were relatively less informed regarding events associated with data privacy and security than HCWs. Specific factors were identified to influence patient awareness with HCWs, suggesting the need for patient education and collaboration with cybersecurity organizations. Participants posited that advanced security features, user-friendly features, online consultation for emergencies, remote monitoring features, and taking patient needs into account are important facilitators of mHealth apps. Shared experiences mainly revolved around the accessibility to information and reliability of mHealth apps, while the challenges were related to poor usability, technical difficulties, data security, and data breaches. Patient autonomy, remote monitoring, self-care management, medical adherence, and time and cost-saving summed up the perceived benefits of the mHealth app. CONCLUSIONS: These findings may assist policymakers in developing strategies to improve Saudi users' and patients' adoption of mHealth apps and address the concerns raised to benefit significantly from these advanced health care modalities.
25 Apr 2026
Read appraisal →JMIR formative research
Global Mapping of Telemedicine Regulation and Ethical Safeguards: Mixed Methods Exploratory Document Analysis
BACKGROUND: Telemedicine has become central to digital health strategies, yet the regulatory environment that shapes ethical safeguards and equitable access remains uneven and incompletely assessed across countries. Legal and normative instruments matter because they define requirements for privacy, consent, accountability, professional readiness, and barrier reduction. OBJECTIVE: This study aimed to map the current global landscape of normative instruments related to telemedicine and identify which ethical and social safeguards are explicitly addressed, with particular attention to equity. METHODS: We conducted a document analysis guided by the READ (ready the materials, extract data, analyze data, and distill findings) framework. From February 2024 to February 2026, we conducted a structured web-based search across all World Health Organization (WHO) member states with no language restrictions, using official government sources, statutory professional regulators, and institutional publication channels. Retrieval combined internal site searches, direct navigation, external search engine queries, and targeted snowball sampling to identify currently in-force instruments. Two researchers independently extracted and coded data using a predefined codebook. We operationalized 10 binary items covering regulatory presence and scope (questions 1 and 2), safeguards for data protection (question 3), consent and disclosure (questions 4 and 5), prior in-person prerequisites (question 6), monitoring (question 7), training requirements (question 8), and equity (questions 9 and 10). We summarized frequencies overall and stratified by WHO region and World Bank income group and conducted a qualitative thematic analysis of included normative instruments. RESULTS: Of the 194 WHO member states, 81 (41.8%) had at least one current normative instrument related to telemedicine in force. Among these, 72.8% (59/81) defined telemedicine or telehealth. Data protection provisions were most common (73/81, 90.1%), followed by mandatory informed consent (n=71, 87.7%) and monitoring mechanisms (n=65, 80.2%). Fewer countries required disclosure of telemedicine limitations (n=36, 44.4%) or mandated telemedicine-specific training (n=26, 32.1%). Prior in-person consultation requirements were uncommon (n=8, 9.9%). Equity-related safeguards were uneven: 51.9% (n=42) referenced justice, equity, or nondiscrimination, whereas 30.9% (n=25) included concrete barrier reduction provisions (eg, digital inclusion or accommodations for people with disabilities and minors). CONCLUSIONS: Telemedicine regulation is becoming more common, but both coverage and safeguarding content remain uneven. While privacy, consent, and monitoring are frequently addressed where regulation exists, disclosure, physician competency, and operational equity measures are less consistently specified. Strengthening telemedicine governance will require translating ethical commitments into enforceable standards that address digital determinants of access and protect groups at risk of exclusion.
25 Apr 2026
Read appraisal →The Cochrane database of systematic reviews
Delivery arrangements for rehabilitation services in health systems: an overview of systematic reviews
BACKGROUND: Cochrane Rehabilitation and the World Health Organization (WHO) Rehabilitation Programme have collaborated to produce four Cochrane overviews of systematic reviews synthesizing evidence from health policy and systems research (HPSR) in rehabilitation. Each overview focuses on one of the four HPSR pillars identified by the Cochrane Effective Practice and Organisation of Care (EPOC) taxonomy: delivery, financial, and governance arrangements; and implementation strategies. This overview addresses delivery arrangements, which Cochrane EPOC defines as how health services are organized and delivered, including who provides care, how care is coordinated and managed, and where services are provided. OBJECTIVES: This overview aimed to synthesize current evidence on delivery arrangements in rehabilitation from an HPSR perspective. Our series of four overviews has the following overarching objectives. • To offer a broad synthesis of existing evidence on health policy and systems interventions' effects. • To direct end-users, including policymakers, towards systematic reviews that may address their health policy questions. • To identify current research gaps and set priorities for future primary HPSR. • To pinpoint needs and priorities for new evidence syntheses where no reliable, up-to-date systematic reviews currently exist. METHODS: We searched Epistemonikos Health Systems Evidence databases and EPOC Group systematic reviews with no language limitations to identify reviews published between 2015 and 17 November 2024. We included Cochrane systematic reviews (CSRs) and non-CSRs of randomized controlled trials (RCTs) and non-randomized studies of interventions (NRSIs) evaluating the effectiveness of health policy and systems interventions for rehabilitation in health systems, specifically related to delivery arrangements as defined in the EPOC taxonomy. All four overview teams screened reviews and extracted data. We used AMSTAR 2 to critically appraise the reviews, and we analyzed the results descriptively. MAIN RESULTS: We included 25 systematic reviews. Three overlapped, and for 17 the AMSTAR 2 rating was low or critically low confidence. Five systematic reviews (2 CSRs and 3 non-CSRs) contributed to our synthesis. Most outcomes focused on patients, caregivers, or service use (e.g. access to rehabilitation). Equity-related outcomes were absent, and quality of care, adverse events, and our important outcomes were rarely reported. Below, we report the results of three of the five reviews judged to have moderate to high confidence for our outcomes of interest, in which authors conducted meta-analysis and assessed the certainty of the evidence. Who provides care One review analyzed advanced practice physiotherapy (APP) models, which may result in little to no difference in health-related outcomes measured by the Pain Disability Index and EuroQol 5-Dimension questionnaire after the intervention, compared with usual care in adults with spinal pain (standardized mean difference [SMD] 0.05, 95% confidence interval [CI] -0.32 to 0.42; 2 studies, 225 participants; low certainty). Information and communication technology We included two reviews in this category. One compared telerehabilitation with usual care in older adults, finding that telerehabilitation may have little or no effect on quality of life after seven to 20 weeks (SMD -0.09, 95% CI -0.23 to 0.40; 3 studies, 179 participants; low certainty). There was very low-certainty evidence on mobility after seven to 26 weeks (SMD 0.63, 95% CI -0.25 to 1.51; 5 studies, 302 participants), strength after 12 and 26 weeks (SMD 0.73, 95% CI -0.10 to 1.56; 4 studies, 226 participants), and balance after seven to 26 weeks (SMD 0.40, 95% CI -0.35 to 1.15; 3 studies, 199 participants). Another review on stroke survivors living in the community found that telerehabilitation compared with usual care probably has little or no effect on independence in activities of daily living (ADL) after 24 weeks (SMD 0.00, 95% CI -0.15 to 0.15; 2 studies, 661 participants; moderate certainty), self-reported quality of life after six to 24 weeks (SMD 0.03, 95% CI -0.14 to 0.20; 3 studies, 569 participants; moderate certainty), and depression after six to 24 weeks (SMD -0.04, 95% CI -0.19 to 0.11; 6 studies, 1145 participants; moderate certainty); and may have little or no effect on upper limb function after 12 weeks (SMD 0.33, 95% CI -0.21 to 0.87; 2 studies, 54 participants; low certainty) and mobility after six weeks (mean difference 0.01, 95% CI -0.12 to 0.14; 1 study; 144 participants; low certainty). This review also compared telerehabilitation with in-person rehabilitation and found that there may be little to no difference in independence in ADL, measured with the Modified Barthel Index at four to 12 weeks (MD 0.59, 95% CI -5.50 to 6.68; 2 studies, 75 participants; low certainty); balance, measured with the Berg Balance Scale at four to 12 weeks (MD 0.48, 95% CI -1.36 to 2.32; 3 studies, 106 participants; low certainty); and upper limb function, evaluated with the Fugl-Meyer Assessment (Upper Extremity) four weeks after intervention (MD 1.23, 95% CI -2.17 to 4.64; 3 studies, 170 participants; low certainty). AUTHORS' CONCLUSIONS: Current evidence on delivery arrangements in rehabilitation is limited, mostly of low certainty, and derived from high-income countries. Reviews covered five EPOC categories, but reliable evidence for our outcomes of interest was available for only two categories. Most evidence was on telerehabilitation. Compared with usual care, APP models may have little to no effect on health outcomes in adults with spinal pain. In people with stroke, telerehabilitation compared with usual care probably has little or no effect on independence in daily living, quality of life, and depression, and may have little to no effect on upper limb function and mobility. Compared with in-person care, telerehabilitation may have little to no effect on ADL, balance, and upper limb function. Further high-quality research using well-defined frameworks is needed, especially in low- and middle-income countries, to identify effective strategies and evaluate organizational, implementation, and equity outcomes. Future Cochrane overviews in HPSR should consider a broader range of study designs, such as observational, qualitative, and mixed-design evidence, to better capture evidence on delivery arrangements in rehabilitation. FUNDING: PC, CK, and SN were supported and funded by the Italian Ministry of Health (Ricerca Corrente). The funder played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. REGISTRATION: Protocol (2025): DOI 10.23736/S1973-9087.24.08833-6.
25 Apr 2026
Read appraisal →PloS one
Forecasting under-five stunting in Ethiopia using classical and machine learning time series models
BACKGROUND: The prevalence of under-five stunting in Ethiopia remains above 30%, which, according to World Health Organization (WHO), constitutes a major public health concern. Stunting has long-term consequences for child growth, development, and overall health. Accurate forecasting of its prevalence is therefore essential to guide policymakers and inform targeted interventions. This study aimed to forecast the prevalence of under-five stunting in Ethiopia for the period 2025-2030 using historical data and time series modeling. METHODS: Annual under-five stunting prevalence data for Ethiopia from 2000 to 2024 were retrieved from the WHO Global Health Observatory. Time series forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), were developed and evaluated. Model performance was assessed using time series cross-validation with mean absolute error (MAE), mean absolute percentage error (MAPE), and R² as evaluation metrics. The best-performing model was applied to forecast stunting prevalence for 2025-2030. RESULTS: The ETS model demonstrated the best predictive performance (MAE = 1.09, MAPE = 2.71%, R² = 0.903) and was selected for forecasting. Forecasting indicated a gradual decline in under-five stunting prevalence in Ethiopia from 33.95% in 2025 to 31.95% in 2030. The projected prevalence for 2029 is 32.35% (95% CI: 26.90-37.79%), above the national target of 19%, and the 2030 forecast remains well above the SDG target of ending all forms of malnutrition. CONCLUSION AND RECOMMENDATION: Under-five stunting in Ethiopia is projected to remain above national and SDG targets by 2030, indicating current nutrition efforts are insufficient; national program evaluation and evidence-based policy adjustments are recommended.
24 Apr 2026
Read appraisal →BMJ open
From legalisation to practice: implementation of newly legalised health interventions in healthcare settings - a systematic review.
OBJECTIVES: Over the last decade, a growing number of health interventions (eg, medical assistance in dying and mitochondrial donation) have become legalised or decriminalised globally. Newly legalised health interventions share characteristics that are distinct from other health interventions, making their implementation more challenging. They are often highly emotive, controversial and associated with strong opinions and ethical dilemmas, with some of them being high-stake and irreversible. This study aimed to identify, systematise and map the factors that affect the implementation of health interventions that have recently been legalised. DESIGN: A systematically conducted review. DATA SOURCES: PubMed, Scopus, EMBASE and CINAHL were searched to identify studies published between 2014 and 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included studies if they evaluated the implementation of health interventions that were newly legalised or newly decriminalised. DATA EXTRACTION AND SYNTHESIS: Data were extracted and synthesised through descriptive analysis. Both deductive and inductive thematic analyses were applied to map the barriers, facilitators and implementing strategies that influence the implementation of newly legalised health interventions in healthcare settings. RESULTS: The search strategy yielded 1510 publications, of which 78 were included in this review. Findings showed that several newly legalised health interventions, including medical assistance in dying (n=56 studies); medical abortion (n=13); assisted human reproduction (n=3); psychedelic-assisted therapies (n=3); use of medical cannabis (n=2) and use of biosimilars (n=1) were addressed. The analysis identified a total of 880 diverse barriers, facilitators and strategies in five domains across system, organisational and individual levels: (1) patients/service users/consumers; (2) healthcare providers; (3) healthcare organisation; (4) legal processes and (5) system. These were further divided into 27 themes of barriers, 18 themes of facilitators and 17 themes of strategies. CONCLUSIONS: Implementing newly legalised health interventions is complex. Our findings can support the development of an implementation plan for the spread and scaling of future health interventions, maximising the impact of interventions and making them accessible to more people and health organisations.
24 Apr 2026
Read appraisal →PloS one
Successful student learning outcomes in Moroccan higher education: Causal configurations of pedagogical, motivational, and ICT conditions using fuzzy-set Qualitative Comparative Analysis (fsQCA)
As the demand for better educational quality and improved student performance grows, institutions face increasing challenges in making their teaching methods more effective to ensure successful learning outcomes. Most practical recommendations tend to focus on isolated effects, but learning success usually results from interconnected factors that work together. Drawing on constructivist Learning Theory, this study examines pedagogical and motivational factors that can positively impact learning results in higher education within Moroccan universities. The main goal is to identify and evaluate the nonlinear individual effects and the interactive causal configurations involving multiple conditions, such as teacher motivation, pedagogical leadership, self-efficacy, instructional innovation, ICT use, and student motivation, on learning outcomes. Empirical data were gathered through a questionnaire completed by 349 Moroccan university students, using measurement scales for key variables. The analysis employed the fuzzy-set Qualitative Comparative Analysis (fsQCA) method, which helps identify different combinations and causal pathways that lead to high academic achievement. Findings suggest that excellent learning outcomes are not caused by a single factor but by the interaction of several interdependent conditions. Different "recipes" can produce similar strong results. Certain combinations, especially those with strong teacher motivation, effective leadership, and strategic ICT use, proved to be reliable configurations, showing that technology is most impactful when integrated into supportive pedagogical and motivational environments. These results can help redefine and evaluate integrated educational policies, considering the complex interactions among individual, pedagogical, and technological factors. This approach promotes more effective and equitable learning environments through coherent bundles of mutually reinforcing interventions, instead of isolated efforts. Ultimately, this research enables educators and policymakers to develop more targeted strategies, utilize resources more efficiently, and improve overall educational effectiveness.
23 Apr 2026
Read appraisal →JMIR formative research
Public Perceptions of AI in Medicine and Implications for Future Medical Education: Cross-Sectional Survey
BACKGROUND: The integration of artificial intelligence (AI) into clinical practice is contingent on public trust. This trust often depends on physician oversight, yet a significant gap exists between the need for AI-competent physicians and the current state of medical education. While the perspectives of students and experts on this gap are known, the views of the US general public remain largely unquantified. OBJECTIVE: This study aimed to assess US public perceptions regarding AI in medicine and the corresponding emergent needs for medical education. We specifically sought to quantify public trust in different diagnostic scenarios, concerns about physician overreliance on AI, support for mandatory AI education, and priorities for the future focus of medical training. METHODS: We conducted a cross-sectional, web-based survey of adults in the United States in November 2025. Participants (N=524) were recruited via SurveyMonkey Audience. We calculated descriptive statistics, frequencies, proportions (percentages), and 95% CIs for all main survey items. RESULTS: A total of 524 participants completed the survey. Most (n=329, 62.8%; 95% CI 58.6%-66.9%) placed the most trust in a physician's diagnosis based on their expertise alone; only 7.8% (n=41; 95% CI 5.5%-10.1%) trusted an AI-first diagnostic model. Trust was highly contingent on training: 93.9% (n=492) of participants rated formal physician training on AI limitations as "essential" or "very important." Widespread concern about physician overreliance on AI was reported, with 81.1% (n=425) being "very concerned" or "extremely concerned." Consequently, 85.1% (n=446) agreed or strongly agreed that training on AI use, ethics, and limitations should be mandatory in medical school. When asked about future educational priorities, 70.2% (n=368; 95% CI 66.3%-74.1%) believed that medical education should focus on human-centered skills (eg, empathy and communication) over clinical skills. CONCLUSIONS: The US public expressed conditional trust in medical AI, strongly preferring physician-led and critically supervised models. These findings reveal a clear public mandate for medical education reform. The public expects future physicians to be mandatorily trained to appraise AI, understand its limitations, and refocus their professional development on the human-centered skills that technology cannot replace.
18 Apr 2026
Read appraisal →Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Crowdsourced versus large language models forecasting: evidence for the accuracy-correlation effect
Over the past quarter century, crowdsourced forecasting has largely outperformed individual forecasters. Today, large language models (LLMs), aggregating human knowledge at scale, constitute a new form of collective intelligence (CI). A central question is how LLM predictive accuracy associates with human-AI correlation, and whether this relationship exceeds what would be expected if both merely track the same underlying truth. We investigate this through the accuracy-correlation effect (ACE), which posits that as algorithmic systems improve, they increasingly correlate with human predictions, potentially diminishing human value in hybrid ensembles. Using 76 model × prompt forecast sets from 16 LLMs on 580 resolved ForecastBench questions, we computed LLM accuracy and correlations with two human aggregates (superforecasters, general public), separately for databases (n = 526) and prediction markets (n = 54) questions. Linear mixed-effects models show a robust positive association between LLM accuracy and human-AI correlation that substantially exceeds independent-errors predictions. Correlations were lower for superforecasters than for the general public, and weaker for markets than for data questions. These results support ACE while indicating that increasing correlation reflects more than improved signal tracking alone, suggesting that simultaneous increases in accuracy and correlation may reduce optimal human weights in data-rich settings, while human judgement retains critical value in contextual reasoning scenarios. This article is part of the theme issue 'The evolution of collective intelligence'.
17 Apr 2026
Read appraisal →African journal of reproductive health
Mapping research fronts of reproductive medicine using the NHANES database: A bibliometric and thematic analysis
Reproductive health, infertility, and perinatal diseases significantly challenge human health development and continuity, requiring multidisciplinary approaches for enhanced academic understanding and practical solutions. This study used bibliometric analysis to examine related articles in the web of science (WoS) database, employing tools including bibliometrix and VOSviewer for systematic literature analysis. To capture the broader context of high-impact research, we first analyzed 1,026 highly-cited and hot papers in reproductive medicine, which revealed evolving thematic trends and global collaboration patterns. Then we examined 232 literatures using NHANES datasets for reproductive health research to identify research gaps and opportunities for further research in this field. The analysis revealed key trends, areas of concentration, and potential knowledge gaps in the literature, while the thematic analysis of reproductive health studies utilizing the NHANES database further underscored the wealth of data amenable to multidisciplinary investigation. Future research directions to explore new dimensions of reproductive health, improving health policies and clinical practices in this field using NHANES database are also suggested. The study concludes that multidisciplinary research using comprehensive datasets like NHANES is crucial for advancing understanding of reproductive health, infertility, and perinatal outcomes. La santé reproductive, l'infertilité et les maladies périnatales constituent un défi majeur pour le développement et la continuité de la santé humaine, nécessitant des approches multidisciplinaires afin d'améliorer la compréhension académique et de proposer des solutions pratiques. Cette étude a utilisé une analyse bibliométrique pour examiner les articles pertinents dans la base de données WoS, en employant des outils tels que Bibliometrix et VOSviewer pour une analyse systématique de la littérature. Afin de saisir le contexte plus large de la recherche à fort impact, nous avons d'abord analysé 1 026 articles hautement cités et à tendance chaude en médecine reproductive, ce qui a révélé les tendances thématiques évolutives et les modèles de collaboration internationale. Nous avons ensuite examiné 232 publications utilisant les jeux de données NHANES pour la recherche sur la santé reproductive, afin d'identifier les lacunes de recherche et les opportunités pour de futures études dans ce domaine. L'analyse a mis en évidence les tendances clés, les domaines de concentration et les lacunes de connaissances potentielles dans la littérature, tandis que l'analyse thématique des études sur la santé reproductive utilisant les jeux de données NHANES a en outre souligné la richesse des données susceptibles d'être étudiées de manière multidisciplinaire. Les orientations futures de la recherche visant à explorer de nouvelles dimensions de la santé reproductive, à améliorer les politiques sanitaires et les pratiques cliniques dans ce domaine à l'aide des jeux de données NHANES sont également proposées. L'étude conclut que la recherche multidisciplinaire utilisant des jeux de données complets tels que NHANES est cruciale pour faire progresser la compréhension de la santé reproductive, de l'infertilité et des issues périnatales.
17 Apr 2026
Read appraisal →International journal of medical informatics
Prioritising digital health technologies in Australian community pharmacy: a delphi study identifying barriers, enablers, and policy implications for implementation
OBJECTIVE: To determine priority digital health technologies for Australian community pharmacies and identify the key barriers, enablers, and policy/funding factors that can inform future implementation planning through expert consensus. METHODS: A two-round Delphi study was conducted with 31 experts representing pharmacy, academia, policy, and digital health. In Round 1, participants identified priority technologies, barriers, and enablers. In Round 2, 27 participants ranked five technologies, nine policy options, and six financial models. Consensus was assessed using descriptive statistics and interquartile ranges (IQRs). RESULTS: E-prescriptions and My Health Record (MyHR) were ranked as top priorities (mean = 1.70 and 2.22; IQR ≤ 1.0). Key barriers included financial constraints, interoperability issues, and digital literacy gaps. Telehealth incentives received the strongest agreement among participants, while reimbursement-based funding and government support were rated as the most supportive financial models for implementation. Broader enablers, such as a national medicine repository and stronger cross-disciplinary collaboration, were also endorsed. CONCLUSION: Digital health adoption in community pharmacy requires prioritisation of core technologies, improved system integration, workforce training, and practical funding mechanisms. These findings offer guidance for policymakers, pharmacy leaders, and digital health stakeholders aiming to embed digital tools more consistently and effectively into pharmacy practice.
15 Apr 2026
Read appraisal →BMJ open
Regional disparities in insurance-covered cognitive behavioural therapy in Japan: a nationwide cross-sectional study using National Database Open Data, 2015-2023
OBJECTIVES: Cognitive behavioural therapy (CBT) is recommended as a first-line treatment for depression and anxiety disorders, but its utilisation under Japan's national health insurance remains poorly understood. This study aimed to describe CBT utilisation patterns, quantify regional disparities across prefectures and analyse temporal trends from fiscal year (FY)2015 to FY2023. DESIGN: This was a nationwide repeated cross-sectional study. SETTING: Japan's National Database of Health Insurance Claims and Specific Health Checkups Open Data (NDB Open Data), FY2015-2023. PARTICIPANTS: All patients who received insurance-covered CBT in FY2023, with a longitudinal comparison across FY2015-2023. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcomes were annual CBT claims and patient counts. The secondary outcomes included prefecture-level distribution, population-adjusted utilisation rates per 100 000 population, distribution by sex and age, monthly trends and temporal changes over 9 years. Regional variation in physician-delivered CBT was assessed using the coefficient of variation (CV) and extremal quotient (EQ). RESULTS: In FY2023, the total CBT claims numbered 38 045 with 8299 patients, representing only 0.14% of an estimated 6.03 million psychiatric patients. Physician-delivered CBT accounted for 99.6% (37 886 claims), whereas nurse-delivered CBT introduced in 2016 remained at 0.4% (159 claims). 13 of the 47 prefectures (27.7%) had zero or fewer than 10 claims. The population-adjusted physician-delivered CBT claims ranged from 370.96 per 100 000 in Okayama to 0.99 per 100 000 in Kumamoto, yielding an EQ of 375-fold. The CV among the 34 prefectures with measurable physician-delivered CBT was 174.8%. Despite indication expansions in 2016 and 2018, claims decreased by 9.9% from 42 216 in FY2015 to 38 045 in FY2023. CONCLUSIONS: Insurance-covered CBT in Japan remains severely underused, with significant regional disparities. Incremental policy measures, including indication expansions and nurse-delivered CBT, have failed to improve access. Fundamental system reforms, potentially including dedicated psychological therapy services, are needed to ensure equitable access to evidence-based psychological treatments.
15 Apr 2026
Read appraisal →JMIR mHealth and uHealth
Trust and Use of Recommendations for Health Apps Among European Residents: Cross-Sectional Survey
BACKGROUND: There is growing recognition of the role of health apps in addressing health care system challenges, yet app quality and evidence vary widely, and consumers have little decision support at the point of download. OBJECTIVE: This study aimed to explore which sources of recommendations European residents use and trust when choosing health apps and whether residents support government review and rating of health apps. METHODS: We conducted a cross-sectional online survey (December 7, 2022, to February 16, 2023) in 26 languages targeting residents of the European Economic Area, the United Kingdom, and Ukraine. The survey contained 11 questions covering demographics, types of apps used, sources of advice used and trusted, and views on government review and rating. We included only fully completed responses (N=1228). Descriptive statistics are presented as counts and percentages. For subgroup analyses, we dichotomized trust responses ("I trust" vs other responses) and tested associations with gender (Fisher exact test), age group, and education level (chi-square test); an adjusted significance threshold was applied (P<.004) to account for multiple testing. We followed the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines for reporting online surveys and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) recommendations for observational studies. RESULTS: A total of 1228 respondents from 33 countries completed the survey; 1110 (90.4%) reported using one or more health apps. COVID-19 apps (763/1228, 62.1%) and activity apps (737/1228, 60.0%) were the most frequently used, whereas disease management (86/1228, 7.0%), diagnostic (77/1228, 6.3%), and treatment apps (70/1228, 5.7%) were the least used. Sources used to choose apps included family and friends (434/1228, 35.3%), health professionals (412/1228, 33.6%), and government or health authorities (358/1228, 29.2%). The most trusted sources were health professionals (987/1228, 80.4%), pharmacists (750/1228, 61.1%), and government or health authorities (736/1228, 59.9%). No statistically significant differences in trust by gender were observed (all P>.0038). Some differences by age and education were observed for select sources (eg, government or health authorities: χ21=8.546; P=.003; family and friends: χ2=19.133; P<.001). Overall, 1060 (86.3%) of 1228 respondents supported government review and rating of health apps (either directly or by commissioning another organization). CONCLUSIONS: In this large multilingual European survey, most respondents reported experience with health apps and placed greatest trust in health professionals, pharmacists, and government or health authorities, yet professional recommendations were used less often than informal sources. There is clear public support for government-led review and rating schemes to guide consumer choice. Efforts to make trustworthy, easy-to-find information available at the point of download and to support health care professionals in recommending high-quality apps could help bridge the gap between trusted and used sources.
10 Apr 2026
Read appraisal →BMC Geriatr
Smart aging: integrating AI into elderly healthcare
Artificial Intelligence (AI) is transforming geriatric healthcare by improving early disease detection, optimizing patient management, and enhancing clinical decision-making. AI-driven tools, including machine learning (ML) algorithms, predictive analytics, and assistive robotics, are increasingly utilized to address challenges associated with aging, such as frailty, multimorbidity, polypharmacy, and fall prevention. These technologies can facilitate personalized medicine, improve diagnostic accuracy, and support healthcare administration by streamlining workflows and resource allocation. Despite its promising applications, the integration of AI in geriatric care presents several challenges, including issues related to data privacy, algorithmic bias, ethical considerations, and the digital literacy of older adults. Ensuring the inclusivity of AI models by incorporating diverse and representative datasets is essential to avoid disparities in healthcare delivery. Additionally, the role of AI must be carefully regulated to complement, rather than replace, human clinical expertise. This paper provides a comprehensive overview of AI applications in geriatric medicine, discussing its benefits, limitations, and future directions. By leveraging AI responsibly, healthcare systems can improve patient outcomes, reduce hospital readmissions, and promote aging in place. However, addressing existing challenges through interdisciplinary collaboration, policy development, and continued research is crucial to fully realizing AI's potential in elderly care.
1 Dec 2025
Read appraisal →Gerontologist
The AI-aging-enterprise: a political economy of aging and artificial intelligence
The current discourse on artificial intelligence (AI) in gerontology remains mostly on an interventionist level and focused on solving problems faced by individuals, leaving the wider social conditions that shape the relationship between aging and AI out of view. The considerable accumulation of power, particularly for technology development companies, in the development and implementation of AI, however, calls for a deeper and more complex analysis of the relationships between AI, older adults and the "aging enterprise." Building on the classical political economy of aging, and expanding it with concepts from material gerontology, we propose a "political economy of aging and AI" as a conceptual tool to analyze the relationships between (older) individuals, social and political structures, and (technological) materialities. We exemplify how such a political economy perspective enables a more complex consideration of the current discourse on (a) AI solutions, (b) AI ethics, and (c) AI policies. We close by summarizing our understanding of a political economy of aging and AI that centers materialities as the critical-gerontological focus of analysis and by outlining calls for action toward strengthening the power of older adults in the emerging AI-aging-enterprise.
1 Nov 2025
Read appraisal →J Pain Symptom Manage
First-Ever Global Ranking of Palliative Care: 2025 World Map Under the New WHO Framework
CONTEXT: Despite progress in policy and advocacy, global palliative care development remains highly uneven, with large segments of the population lacking access to quality services. A new WHO framework has provided an updated methodology for assessing national palliative care systems. OBJECTIVES: To assess the current state of palliative care development worldwide using the WHO framework and to produce the first global ranking of countries based on their performance across 14 indicators. METHODS: A cross-sectional, mixed-methods study was conducted across 201 countries and territories between 2023 and 2025. Trained national consultants conducted a structured survey based on WHO indicators. Scores were assigned across six domains: policy, essential medicines, service delivery, education, research, and community empowerment. Responses were validated and analyzed to produce a Global Development Score and to classify countries into four development levels: Emerging, Progressing, Established, and Advanced. RESULTS: Of the 201 countries assessed, 40% were classified as Emerging and 28% as Progressing, representing half the global population. Only 14% reached the Advanced level, and 17% were classified as Established. Significant gaps in access to essential medicines and specialized education persist-even in some high-income settings. Despite limited resources Thailand, Uganda,Chile, and Uruguay stand out as regional examples of advanced development. CONCLUSION: This study presents the first global ranking of palliative care development based on WHO indicators. The results highlight persistent disparities and offer a tool for targeted improvement. The Global Development Score enables countries to benchmark progress, identify gaps, and develop strategic responses to expand access and alleviate serious health-related suffering.
1 Nov 2025
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