Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 16 appraisals
Journal of tissue viability
Clinical decision support tools in wound management: A scoping review of existing and emerging tools.
AIM: This scoping review aimed to identify and map clinical decision support tools (both digital and non-digital) used by clinicians for wound management in acute, primary, and community health settings globally, with a focus on the use of artificial intelligence, and the barriers and enablers to implementation. MATERIALS AND METHODS: Studies published between January 2015, and August 2024 were identified through systematic searches of Scopus, Embase, MEDLINE, and CINAHL, conducted in accordance with JBI methodology and reported using the PRISMA-ScR guidelines. Eligible studies included quantitative, qualitative, and mixed-methods research examining non-digital and AI-supported clinical decision support tools for wound management across healthcare settings. RESULTS: Seventeen studies were included, two evaluating AI-supported clinical decision support tools. Most clinical decision support tools had structured wound assessment and treatment planning, though evidence of clinical effectiveness was limited, with only one tool fully validated. Adoption by nurses was influenced by experience, trust, training, and workflow integration, with senior nurses less likely to rely on clinical decision support tools. AI-enabled tools, and a convolutional neural network-based model, improved assessment consistency, documentation, and workflow efficiency. Key barriers included concerns about trust, clinical autonomy, and usability. CONCLUSIONS: Both traditional and AI-supported clinical decision support tools are used for chronic wound management across acute, primary, and community care, but evidence of effectiveness and validation remains limited. The absence of experimental studies highlights the need for rigorous evaluation, clinician education, and strategies to support integration of AI-enabled clinical decision support tool into routine practice.
2 Aug 2026
Read appraisal →JMIR mHealth and uHealth
Perceptions of Adults Aged 50 Years and Older Regarding the Use of Wearable mHealth Technologies to Promote Physical Activity: Systematic Review and Meta-Ethnography
BACKGROUND: Despite advances in wearable mobile health (mHealth) technologies and their associated apps designed to promote physical activity, and the importance of adapting them to users, little is known about older adults' perceptions of these technologies. OBJECTIVE: This review aimed to synthesize and analyze qualitative evidence exploring the perceptions of adults aged 50 years and older regarding areas to improve, barriers to, and facilitators of wearable mHealth technologies (activity trackers and companion apps) to promote physical activity. METHODS: A qualitative systematic review and meta-ethnography was conducted. Comprehensive searches were performed across 8 databases (MEDLINE, Scopus, Web of Science, CINAHL, The Cochrane Library Plus, PsycINFO, ProQuest, and ÍnDICEs-CSIC) for articles published in English or Spanish between January 2013 and January 2024. The synthesis followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and ENTREQ (Enhancing Transparency in Reporting the Synthesis of Qualitative Research) guidelines. RESULTS: Ten articles met the inclusion criteria and were synthesized using meta-ethnography. Three main themes emerged: (1) barriers to promoting physical activity caused by wearable mHealth technologies: personal barriers (physical aspects, perceptions about technology, and personal preferences), technological barriers (functionality, content, design, alarms, availability, and accessibility), and environmental barriers (season of the year); (2) personal facilitators (consideration that these apps improve health, perceptions about technology, and personal preferences), technological facilitators (functionality, content, and design), relational facilitators (technological and social support), environmental facilitators (seasonal variations), and health care professionals (support and monitoring by health care services); and (3) personal areas (perceptions about technology and personal preferences), technological areas (functionality, content, and design), and relational areas (technological support). CONCLUSIONS: Although older adults acknowledge the potential of wearable mHealth technologies to promote physical activity, their effective engagement is hindered by distinct personal, technological, and environmental barriers. Bridging the digital divide requires designers to prioritize user-centered, age-friendly interfaces that are integrated with continuous support from health care professionals. To promote genuine health equity, future research must rigorously report intersectional demographics to ensure that mHealth interventions mitigate, rather than inadvertently exacerbate, existing disparities.
30 July 2026
Read appraisal →Medicine
A bibliometric analysis of global trends in AI-driven digital health technologies for diabetes management
BACKGROUND: Digital health technologies are increasingly applied in diabetes care, enabling continuous monitoring, personalized support and remote interventions. Meanwhile, artificial intelligence (AI) is enhancing the precision and effectiveness of these tools. This study aims to map global research trends and thematic developments in AI-driven digital health technologies for diabetes management and to explore their future directions. METHODS: We collected data from the Web of Science Core Collection, including articles and reviews published up to July 12, 2025, using CiteSpace, VOSviewer, and Microsoft Excel to analyze countries/regions, institutions, journals, references, authors, and keywords. RESULTS: A total of 673 publications were included in the analysis. Global publications on AI-driven digital health technologies for diabetes increased steadily, with the USA leading in output. The University of London ranked as the most productive institution. Sensors and diabetes care were the most frequently published and cited journals in this field. Herrero P was among the most prolific authors. The most cited article was "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs." "diabetes" was the most frequently occurring keyword. Keyword cluster analysis identified 3 primary research hotspots: AI-enabled monitoring, digital health interventions, and AI-based diabetic retinopathy screening. CONCLUSIONS: This study summarizes the evolution of AI-driven digital health technologies in diabetes care. Although challenges remain in data security, standardization and validation, these technologies hold increasing potential for accurate diagnosis, real-time monitoring and personalized care.
26 July 2026
Read appraisal →International journal of qualitative studies on health and well-being
Between duty and constraint: a qualitative systematic review of healthcare providers' ethical challenges and moral stressors in caring for undocumented migrants
PURPOSE: Healthcare providers play a critical role in delivering care to undocumented migrants, who face systemic barriers to healthcare access. While research has documented undocumented migrants' legal and policy barriers, less is known about the ethical challenges and moral stressors experienced by providers. This systematic review synthesizes qualitative studies on providers' experiences when delivering care to undocumented migrants. METHODS: The review followed PRISMA guidelines. PubMed, Embase, CINAHL, and the Cochrane Library were searched for relevant qualitative studies. Studies were screened by title, abstract, and full text using predefined inclusion and exclusion criteria. Quality assessment was conducted using the CASP checklist. Data were synthesized using the Qualitative Analysis Guide of Leuven (QUAGOL), integrating Graneheim and Lundman's approach to qualitative content analysis. RESULTS: The systematic search identified 37 qualitative studies. Analysis revealed 58 themes and subthemes, organized under five key concepts: experiences, perceptions, attitudes, practices and coping mechanisms, and ethical challenges. Providers reported moral distress, emotional exhaustion, and professional dilemmas arising from legal constraints, resource limitations, and conflicting obligations. Ethical tensions centered on beneficence vs. non-maleficence, professional duty vs. legal compliance, and the moral dilemma of deservingness. Across these findings, the synthesis identifies ethical burden-shifting as a central analytical contribution: restrictive systems transfer the moral and practical consequences of exclusionary arrangements onto providers and undocumented migrants at the point of care. CONCLUSION: This review shows that providers' ethical challenges are not only individual clinical dilemmas but structurally generated moral stressors. Addressing these challenges requires structural interventions, including policy reforms that reconcile professional ethics with legal constraints and institutional support to mitigate moral distress.
10 July 2026
Read appraisal →JMIR research protocols
Service Robots as Work Support for Health Personnel in Long-Term Care: Protocol for a Scoping Review
BACKGROUND: Demographic shifts are increasing the global demand for long-term care services, coinciding with a worldwide shortage of health care personnel. Service robots, designed to perform tasks in both professional and personal use, are perceived as a potential solution to alleviate health care personnel's workload and enhance the quality of care. However, the existing literature is fragmented and heterogeneous, with a limited emphasis on the role of service robots in supporting residents rather than health care personnel. Furthermore, there is a lack of consistent definitions of service robotic technologies and a scarcity of studies on implementation models and frameworks. OBJECTIVE: This scoping review aims to map and synthesize evidence regarding the implementation of service robots as work support for health care personnel in long-term care settings. METHODS: A comprehensive 3-step search will be conducted in Embase, MEDLINE, APA PsycInfo, CENTRAL, Scopus, and CINAHL, along with gray literature databases and institutional repositories. Eligible sources encompass empirical studies and gray literature involving service robots, health care personnel, residents aged 65 years or older, and stakeholders such as informal caregivers within institutional long-term care. Exclusions apply to studies on home care, medical or industrial robots, and nonrobotic technologies. Data will be extracted and analyzed using the Joanna Briggs Institute methodology, with findings presented in tables, diagrams, and narrative summaries to identify gaps and inform future research and implementation strategies. RESULTS: The project has been funded for a 4-year period starting in April 2025. This protocol was developed in October 2025 and subsequently registered in November 2025. A comprehensive search strategy was formulated and completely conducted on October 24, 2025. The screening of 4884 titles and abstracts was completed in December 2025, resulting in the retrieval of 64 (1.3%) full-text articles for eligibility assessment. Subsequent phases, including data extraction, analysis, evidence synthesis, and presentation of results, will be conducted sequentially. The scoping review is expected to be finalized by June 2026. CONCLUSIONS: This scoping review is expected to delineate the extent and characteristics of the existing evidence on service robots as work support for health personnel in long-term care settings. It will highlight the key reported outcomes and challenges encountered in implementation studies, as well as the theoretical frameworks, models, and concepts applied to address these issues. TRIAL REGISTRATION: Open Science Framework QWK58; https://osf.io/qwk58/. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/89435.
10 July 2026
Read appraisal →JMIR nursing
WeChat-Based Nursing Interventions in Women's Mobile Health: Systematic Review
BACKGROUND: Mobile health (mHealth) technology offers new approaches to improve women's health by providing personalized monitoring and real-time guidance. As one of the most widely used social media platforms in China, WeChat has shown great potential in mHealth practice, yet systematic evidence on its application in women's health care remains insufficient. OBJECTIVE: This study aims to systematically review WeChat-based nursing interventions in women's mHealth in order to clarify the application status, intervention modalities, target populations, and effectiveness outcomes. METHODS: Searches were conducted in IEEE Xplore, Web of Science, PubMed, Scopus, ACM Digital Library, and Cochrane Central Register of Controlled Trials between January 2011 and December 2024. Two independent reviewers screened studies; extracted data on study design, intervention forms, target diseases, and outcome indicators; and assessed methodological quality. The Cohen κ coefficient was used to evaluate interreviewer agreement. Publication trends, institutional collaborations, author contributions, and research hotspots were analyzed using VOSviewer (Leiden University) and InCites (Clarivate) for bibliometric analysis. RESULTS: A total of 31 eligible studies published from 2014 to 2024 were included. Most studies were randomized controlled trials (n=27). Intervention modalities mainly included WeChat groups (n=22), official accounts (n=18), applets (n=4), and private chats (n=9), mostly used in combination. The top focused health issues were prenatal care (n=5), breast cancer (n=5), gynecological cancer (n=5), and gestational diabetes mellitus (n=4). Six studies adopted multidisciplinary teams. Cohen κ was 0.71, indicating substantial agreement. Publications grew rapidly after 2018, peaking in 2021 and 2024. A total of 40 institutions participated, with Xi'an Jiaotong University having the highest citation impact. Most studies were at high risk of bias due to a nonblinding design. CONCLUSIONS: WeChat-based nursing interventions improve personalized health information access, self-management ability, treatment compliance, and real-time doctor-patient communication for women. This is the first systematic review to evaluate WeChat mHealth interventions in women's health, filling the research gap. Future research should focus on improving methodological quality, exploring cross-cultural adaptability, conducting long-term follow-up, and integrating wearable devices and electronic health records to further optimize WeChat-based women's health services.
7 July 2026
Read appraisal →PloS one
Delayed health-seeking behavior and its associated factors among cancer patients in Ethiopia: A systematic review and meta-analysis, 2025
BACKGROUND: Delayed Health-seeking behavior among cancer patients is a major contributor to late diagnosis, poor prognosis, and high mortality, particularly in low-resource settings like Ethiopia. However, evidence on the magnitude and determinants of delayed care-seeking remains fragmented. OBJECTIVE: This systematic review and meta-analysis aimed to estimate the pooled prevalence of delayed Health-seeking behavior among cancer patients in Ethiopia and to identify associated factors influencing delays. METHODS: This study employed a systematic review and meta-analysis design to assess delayed Health-seeking behavior and its influencing factors among cancer patients in Ethiopia. A systematic search was conducted in PubMed, Scopus, Web of Science, CINAHL, AJOL, Google Scholar, and Ethiopian University repositories until April 27, 2025. The data were extracted from March 10-20 and analyzed from March 21-30, with report generation till April 27, 2025, using R software. Meta-analysis was performed using a random-effects model, with forest plots illustrating pooled prevalence and associated factors. Heterogeneity was assessed using the I² statistic, and study quality was evaluated using a validated tool. RESULTS: Seven studies conducted across multiple regions of Ethiopia were included in the final analysis with a total of 2,641 participants. The pooled prevalence of delayed Health-seeking behavior among cancer patients was 54% (95% CI: 39%-68%). Meta-analysis of associated factors showed that rural residence was significantly associated with delayed Health-seeking behavior, with patients residing in rural areas having more than threefold higher odds of delay (AOR = 3; 95% CI: 1.81-4.19), poor knowledge about cancer was strongly associated with delay, with nearly seven times higher odds among patients with poor knowledge compared to those with adequate knowledge (AOR = 6.63; 95% CI: 2.21-11.05), lack of cancer awareness was also a significant predictor of delayed Health-seeking behavior (AOR = 2.63; 95% CI: 1.75-3.51), and patients without pain were over three times more likely to delay Healthcare(AOR = 3.38; 95% CI: 2.44-4.67) were factors associated with delayed Health-seeking behavior. CONCLUSIONS: Our review showed that half of the cancer patients in Ethiopia experienced delayed health-seeking behavior. Delayed care-seeking was associated with rural residence, poor knowledge, limited awareness of cancer, and absence of pain symptoms. Targeted interventions, including public awareness campaigns, expansion of healthcare services in rural areas, and financial support initiatives, are urgently needed to reduce delays and improve early cancer diagnosis and outcomes. PROSPERO REGISTRATION NUMBER: CRD420251037845.
4 July 2026
Read appraisal →Advances in skin & wound care
Mobile Health App Needs Among Patients With Diabetic Foot Ulcers in China: A Qualitative Study From the Perceptions of Patients, Caregivers, and Health Care Professionals
OBJECTIVE: To explore the specific needs of patients with diabetic foot ulcers (DFUs), caregivers, and health care professionals (HCPs) for a mobile health (mHealth) app, aiming to inform the design and development of effective mHealth service solutions. METHODS: This descriptive qualitative study was conducted from June to September 2024 in the wound care clinics of 2 local hospitals. Participants included patients with DFUs, caregivers, and HCPs directly involved in their care. Interview data were analyzed, synthesized, and refined using content analysis. RESULTS: Five key themes emerged: the pressing need to implement mHealth app services, convenient and personalized access to information, continuous and specialized health guidance, a multidisciplinary approach to disease management, and free access alongside privacy and legal protections. CONCLUSIONS: This study provides valuable insights for the design and development of an mHealth app for DFU. During the development process, it is essential to consider user needs and ensure the app meets the expectations of patients and related groups for personalized, continuous, and specialized health guidance; free access; privacy protection; and multidisciplinary collaboration. A balance should be struck between convenience and security of the app's features, to encourage user engagement and enhance the app's value and effectiveness.
2 July 2026
Read appraisal →Journal of medical Internet research
Using a Large Language Model to Support Thematic Analysis of Patient Experiences in Chronic Illness Management: Comparative Qualitative Study
BACKGROUND: Qualitative health research often focuses on how patients experience and manage chronic illnesses, a topic that has been extensively studied in the literature. With the emergence of large language models (LLMs), such as Claude (Anthropic PBC) and ChatGPT (OpenAI), new opportunities are arising to support and scale the thematic analysis of narrative health data. However, their role and added value compared to traditional human-led approaches remain underexplored, particularly in complex clinical contexts such as multimorbidity. OBJECTIVE: We aim to evaluate the methodological contribution of LLM-assisted analysis by examining its ability to replicate and extend established qualitative insights, in comparison with traditional thematic analysis. METHODS: Semistructured interviews were conducted with 30 individuals living with two or more chronic illnesses. Transcripts were analyzed using both manual thematic coding and Claude 3.5 Sonnet. A structured comparison was conducted to identify shared and unique themes across the two approaches. The analysis examined thematic overlap, differences in subtheme identification, and variation in the level of detail between the methods. RESULTS: Both approaches identified similar core themes related to the patient experience, including health care navigation and challenges, support systems and family dynamics, and emotional challenges and coping. Manual analysis produced more contextually detailed interpretations, while the LLM approach identified a larger number of subthemes. Each method also revealed distinct themes: the manual analysis included themes such as faith, caregiving roles, and a proactive mindset, whereas the LLM identified themes such as future planning and multiple health conditions. The findings show both similarities and differences between the two approaches. The LLM analysis also demonstrated efficiency in processing large volumes of qualitative data. CONCLUSIONS: A hybrid approach that integrates artificial intelligence-assisted and human-led thematic analysis can enhance both analytical depth and scalability. These findings support the use of LLMs as a complementary tool in qualitative research, while highlighting the importance of combining automated pattern detection with human interpretation.
30 June 2026
Read appraisal →Journal of medical Internet research
Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review
BACKGROUND: Effective health care communication is crucial in the medical field. However, effective communication in clinical practice still faces numerous obstacles, and large language models (LLMs) offer various possibilities for improving the quality of medical communication. To date, there are no published reviews on the use of LLMs in health care communication. OBJECTIVE: This review sought to summarize the applications and challenges of LLMs in health care communication and to identify directions for future research. METHODS: A comprehensive literature search was conducted in PubMed, Embase, Web of Science, and the Cochrane Library from January 2018 to November 2025. The search and selection process followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideline and the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) checklist. Eligible studies used LLMs to facilitate health care communication among the public, patients, and clinicians. Following rigorous data extraction and cross-checking, we conducted a quantitative analysis of characteristics of the included literature. Furthermore, using communication accommodation theory as a framework, we identified application patterns of LLMs in health care communication and summarized current challenges and future directions. RESULTS: Ninety-six studies were included in this review, all published between 2023 and 2025, summarizing 4 patterns of LLM application in health care communication: transforming medical information (n=30), facilitating dynamic interaction (n=38), empowering communication capabilities (n=10), and optimizing clinical workflows (n=18). The role of LLMs in health care communication is undergoing a paradigm shift from "static information processing" to "dynamic intelligent interaction." Although they show great promise for practical applications, current evaluation methods and dimensions exhibit significant heterogeneity. Furthermore, LLMs still face multiple challenges in their practical application in health care communication, including technical reliability issues, social trust and adoption, interaction and access barriers, and clinical integration challenges. CONCLUSIONS: Unlike previous studies that merely touched upon the challenges and future directions, this scoping review uses communication accommodation theory to systematically map the application patterns and developmental landscape of LLM-mediated health care communication. Health care communication powered by LLMs holds significant innovation potential and is currently still in the early stages of rapid development. Future research should focus on optimizing model performance, strengthening ethical governance frameworks, enhancing human-machine collaboration models, and ensuring responsible application of LLMs in health care through rigorous empirical validation.
28 June 2026
Read appraisal →BMC palliative care
Factors associated with psychological distress among end-of-life care volunteers: a systematic review of quantitative and qualitative evidence
BACKGROUND: Volunteers are integral to end-of-life care, providing emotional, spiritual, and practical support. However, they often face emotionally demanding situations with limited training and supervision compared to professionals. Given the limited and fragmented literature on psychological distress experienced by end-of-life volunteers, this systematic review aimed to synthesise existing quantitative and qualitative evidence to identify factors associated with psychological distress. METHODS: We conducted a systematic literature review including qualitative and quantitative evidence. Five databases (MEDLINE, EMBASE, PsycINFO, Cochrane Database and Web of Science) were searched for original studies, complemented by citation and reference searches. Study quality was assessed using the Qualsyst tool. Quantitative findings were synthesised using an algorithm to evaluate evidence strength, and qualitative data were integrated through thematic meta-synthesis. RESULTS: Twenty-six studies (20 quantitative and 6 qualitative studies) met inclusion criteria. Quantitative research examined 49 volunteer-related, 18 service-related, and one volunteer-patient-interaction-related factor associated with anxiety, death anxiety, depression, burnout, and/or perceived stress. Moderate-strength evidence indicated that death anxiety was negatively associated with better health and well-being but unrelated to age, volunteer experience, or training. Furthermore, depression was negatively associated with volunteer training. Qualitative evidence was scarce, but highlighted additional patient-, interaction-, and service-level mechanisms. CONCLUSION: This review identifies a small, methodologically diverse evidence base on factors associated with psychological distress in end-of-life care volunteers. Quantitative evidence suggests a potential protective association between training and depression, though substantial heterogeneity limits firm conclusions. Limited qualitative evidence revealed patient-, interaction- and service-level factors that are rarely captured quantitatively. Robust theory-guided longitudinal studies are needed to better understand distress and resilience in this under-researched group.
28 June 2026
Read appraisal →PloS one
Artificial Intelligence in emergency department triage: A scoping review
BACKGROUND: Triage in emergency departments (ED) is a critical process for prioritizing care and ensuring clinical safety. However, current triage systems often exhibit vulnerabilities that compromise the efficiency and quality of healthcare delivery. Artificial Intelligence (AI) has emerged as a promising innovation to support decision-making and optimize patient flow in these high-pressure environments. OBJECTIVE: To map the available evidence regarding the implementation and performance of artificial intelligence in emergency department triage. METHOD: This scoping review followed the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR guidelines. A comprehensive search was conducted across 13 databases (CINAHL, Cochrane Library, PubMed Central, SciELO, Web of Science, SCOPUS, Science Direct, VHL, Embase, and several regional dissertation repositories), with no language or time restrictions. Two independent reviewers performed the selection process using the Rayyan platform, with discrepancies resolved by a third evaluator. Data were synthesized using the PAGER framework, categorizing findings into Patterns, Advances, Gaps, Evidence for practice, and Recommendations for research. RESULTS: Nineteen studies met the inclusion criteria. AI was primarily implemented through Machine Learning (ML) algorithms, including Deep Learning architectures. Natural Language Processing (NLP) was frequently employed to process unstructured clinical data, with recent studies exploring the potential of Large Language Models (LLMs). Overall, ML-based models consistently outperformed traditional triage systems in predictive accuracy. These techniques were mainly utilized for automated classification, predicting clinical severity, and enhancing patient prioritization by integrating both objective and subjective assessment data. CONCLUSIONS: The findings indicate that AI has significant potential to enhance emergency triage by streamlining service flows and providing robust clinical decision support. However, the current evidence remains heterogeneous and largely exploratory. Key challenges include variability in model performance, a lack of external validation, and studies often limited to specific populations. Consequently, many current tools still lack the necessary reliability for safe, large-scale clinical implementation.
27 June 2026
Read appraisal →European journal of emergency medicine : official journal of the European Society for Emergency Medicine
Effect of a liquid crystal vein locator on intravenous catheterization in young children: a randomized trial
BACKGROUND AND IMPORTANCE: Peripheral intravenous catheterization (PIVC) in young children is notoriously challenging because of factors like poor patient cooperation and small vessel diameter. Repeated attempts cause distress and delay critical emergency treatment. OBJECTIVES: This study aimed to evaluate the effect of a novel liquid crystal display-based near-infrared vein locator on PIVC performance in children aged under 3 years. DESIGN: This is a prospective, randomized controlled trial. SETTINGS AND PARTICIPANTS: A total of 105 children aged 0-3 years requiring PIVC were enrolled in a pediatric emergency department. INTERVENTION: Participants were randomly assigned to undergo PIVC using either a liquid crystal vein locator (study group, n = 50) or the traditional landmark-based method (control group, n = 55). OUTCOME MEASURES AND ANALYSIS: The primary endpoint was the first-attempt success rate. Secondary endpoints included the number of puncture attempts, vein localization time, and total insertion time. Multivariable logistic and Poisson regression models were used to adjust for covariates, such as vein difficulty and patient demographics. Analyses included effect sizes with 95% confidence intervals (CIs). MAIN RESULTS: The baseline characteristics of both groups were comparable. The first-attempt success rate was significantly higher in the study group compared to the control group (86.0% vs. 54.5%; absolute difference: 31.5%; 95% CI: 15.2-47.8%). The study group also demonstrated a significantly shorter median vein localization time (16.5 vs. 60.0 s) and median total insertion time (40.0 vs. 185.0 s). In addition, the device significantly reduced the median number of puncture attempts (adjusted rate ratio: 0.77; 95% CI: 0.66-0.90). Multivariable analysis revealed that device assistance was an independent predictor of first-attempt success (adjusted odds ratio: 3.35; 95% CI: 1.13-9.95). CONCLUSION: In this study, the use of a liquid crystal vein locator was associated with a significantly higher first-attempt success rate, fewer puncture attempts, and reduced procedural times for PIVC in young children.
24 June 2026
Read appraisal →Vaccine
Impact of standing orders on vaccine uptake: A systematic review.
OBJECTIVE: We sought to evaluate the impact of standing orders on vaccine coverage in clinical settings. METHODS: Two reviewers independently screened and included studies that evaluated use of standing orders either alone or in combination with other interventions and collected data on vaccine coverage. We extracted effect sizes for studies that used only standing orders as an intervention and had a comparison group. RESULTS: The search yielded 56 eligible studies, 22 of which evaluated standing orders-only interventions. Standing orders increased vaccine uptake by a median of 13 percentage points (IQR 2-20 percentage points). Eight of these studies had a comparison group including 1 randomized trial; 5 were at low risk of bias. The overall findings were similar those for seasonal influenza vaccine (median increase of 12 percentage points; IQR, 6-24; 7 studies) and pneumococcal vaccines (14 percentage points; 2-20; 5 studies), the two most studied outcomes. Multi-component studies paired standing orders with interventions for providers frequently (34%-63% of studies), patients often (20%-49%), and systems least often (3%-14%). CONCLUSIONS: Standing orders show one of the largest effects among vaccine uptake interventions. Future research should focus on randomized trials, childhood vaccination, and behavioral aspects of implementation in healthcare systems.
21 June 2026
Read appraisal →Journal of global health
Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals
BACKGROUND: Accurate assessment of pressure injury staging is essential for guiding appropriate care, reducing patient suffering, alleviating the healthcare burden, and improving quality of life. We aimed to develop a deep learning-based model for pressure injury recognition and to translate the best-performing model into a preliminary smartphone application. METHODS: We conducted a multicentre retrospective study using pressure injury images collected from 59 hospitals. We applied to the data set three artificial intelligence-based image analysis models, Mask R-convolutional neural network (CNN) with ResNet-18, Mask R-CNN with Swin Transformer, and Segmenting Objects by Locations, version 2. We assessed model performance using standard evaluation metrics, including precision-recall curves, average recall (AR), average precision (AP), and mean average precision (mAP). The model achieving the best overall performance was selected for subsequent translation into a smartphone-based tool. RESULTS: We included a total of 1903 pressure injury images, with 1713 used for model training and 190 for validation. Among the models evaluated, Mask R-CNN with Swin Transformer demonstrated the highest overall performance (mAP = 0.894, AP50 = 0.900, AP = 0.757, AR100 = 0.792), surpassing the corresponding metrics of the other two models. The model, based on Mask R-CNN with a Swin Transformer, was integrated into a smartphone for preliminary translation. CONCLUSIONS: The proposed deep learning-based system showed promising performance in pressure injury staging and may provide meaningful support for clinical decision-making. Future studies should focus on further validation and refinement using larger and more diverse data sets to enhance their applicability in routine clinical practice.
20 June 2026
Read appraisal →JMIR research protocols
Design, Development, and Validation of a Chatbot to Support Health Care Professionals Experiencing Workplace Aggression: Protocol for a Mixed Methods Study
BACKGROUND: Workplace violence against health care professionals has increased worldwide, leading to negative psychological, professional, and organizational outcomes. Despite existing prevention and reporting programs, underreporting and lack of accessible, confidential support persist. Digital health tools, including chatbots, may offer scalable support, guidance, and follow-up for affected professionals. OBJECTIVE: This study aims to design, develop, and validate a chatbot (Sanidad Segura) to assist health care professionals who experience workplace aggression and evaluate its usability, readability, and exploratory indicators of perceived usefulness and support in a pilot study. METHODS: This study will follow a mixed methods design conducted in two main phases: (1) design, development, and content validation of the chatbot based on literature review, institutional protocols, and expert consensus; and (2) pilot-testing, including usability and readability assessment using standardized instruments, as well as feasibility and acceptability evaluation among health care professionals working in emergency and critical care settings in Almería (Spain). The study is aligned with the Medical Research Council framework for complex interventions, incorporating development and feasibility stages. Quantitative data will be collected using the System Usability Scale and Inflesz readability scale. Qualitative data will be collected through semistructured interviews and analyzed using thematic analysis to explore user experience and identify barriers to and facilitators of use. RESULTS: The study has been funded for a 2-year period starting on December 18, 2024. Quantitative outcomes will include usability scores (System Usability Scale), readability scores (Inflesz), and participants' sociodemographic characteristics. Qualitative findings will identify themes related to usability, user experience, and suggestions for improvement. Integration of quantitative and qualitative findings will be conducted through triangulation to provide a comprehensive understanding of the usability, acceptability, and readability of the chatbot. CONCLUSIONS: This study addresses the increasing incidence of workplace violence against health care professionals through the development of a new chatbot (Sanidad Segura). This intervention seeks to facilitate the identification, support, and follow-up of affected individuals while minimizing the adverse effects of such events on their physical and psychological well-being, social interaction, and professional performance. Sanidad Segura will enable confidential case reporting and provide access to tailored medical, psychological, and legal resources, as well as information about institutional support services. This project represents a crucial step toward implementing an integrated digital framework for the detection, management, and prevention of workplace violence in health care settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/92511.
19 June 2026
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