Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 130 appraisals
Acta obstetricia et gynecologica Scandinavica
ChatGPT in urogynecology: Comparing large language model responses to human experts
INTRODUCTION: Large language models (LLMs) are increasingly used in healthcare, including urogynecology, where stigma may limit open discussion. LLM-based chat platforms may provide a less intimidating and more accessible way for patients to obtain information, but their reliability requires evaluation. This study compared the quality of ChatGPT-generated responses in urogynecology with those provided by a consultant urogynecologist, focusing on understandability, helpfulness, and reassurance. MATERIAL AND METHODS: A cross-sectional survey was conducted among urogynecology patients. After informed consent, participants reviewed responses to six common questions, each answered by ChatGPT and a single consultant. A blinded third-party consultant verified clinical accuracy. Patients rated responses using a 5-point Likert scale across three domains (maximum score 15 per response). Wilcoxon signed-rank tests were used for comparison. RESULTS: A total of 203 patients participated (median age 56 years, interquartile range 46-66). ChatGPT responses received higher total ratings than consultant responses (76 [67-85] vs. 72 [63-80], p < 0.01). Scores were higher for understandability, helpfulness, and reassurance (all p < 0.01). ChatGPT was preferred in four of six questions, one showed no difference, and one favored the consultant. Subgroup analyses showed no significant variation based on patient characteristics. CONCLUSIONS: In this exploratory study, women rated ChatGPT's responses as clearer and more reassuring than consultant answers. These findings reflect patient perceptions in a limited setting and should be interpreted with caution. While LLMs may have a supportive role in patient education, their use must remain secondary to expert clinical care and subject to careful oversight.
3 Aug 2026
Read appraisal →International journal of medical informatics
Machine learning models for predicting readmission after stroke: A systematic review and meta-analysis
BACKGROUND: Hospital readmission following stroke poses a significant challenge for healthcare systems. Machine learning (ML) offers the potential to improve prediction models for readmission risk, surpassing traditional statistical methods. However, the performance of ML models in such context has not been systematically evaluated. We aim to evaluate the performance of ML models in predicting post-stroke hospital readmission, as well as identifying the most important readmission predictors. METHODS: A comprehensive systematic literature review and meta-analysis was conducted.Studies included were those utilizing ML models for stroke readmission prediction. The primary outcome was the predictive performance of ML models for all time stroke readmission as reported by the Area Under the Receiver Operating Characteristic Curve (AUROC). Pooled AUROC values were calculated using a random-effects model. RESULTS: A total of eleven studies involving 380,254 patients were analyzed. Mean age of the patients ranged between 64.5 and 79.7 years. In total, 49 ML models were reported with Logistic regression (LR) and Random Forest being the most used. The number of input predictive variables used for model training ranged from 6 to 10,047. The overall pooled AUROC for predicting hospital readmission was 0.74 (95% CI: 0.69 to 0.78). For 30-day readmission, the pooled AUROC was 0.73 (95% CI: 0.67 to 0.79) while for 90-day readmission, it was 0.75 (95% CI: 0.69 to 0.80). The most frequently reported variables as being top predictors of readmission were Length of stay (LOS) (3 studies), Age (2 studies), National Institutes of Health Stroke Score (NIHSS) (2 studies), HbA1c (2 studies), Homocysteine blood level (2 studies). CONCLUSION: Machine learning models demonstrate moderate predictive performance in stroke readmission risk prediction. Future research should focus on validating and refining existing models and adopting unified methodological approaches to aid in drawing more accurate conclusions.
1 Aug 2026
Read appraisal →PloS one
Development of a scale for measuring the perception of artificial intelligence among mental health consumers
BACKGROUND: Artificial Intelligence (AI) has emerged as a transformative force revolutionizing various sectors, including healthcare, particularly the mental health field. However, the acceptance and integration of AI technologies in different healthcare systems can be influenced by various factors, including cultural, social, and individual aspects. Nevertheless, there is a need for a valid and reliable tool for assessing AI's perception among healthcare consumers. AIM: To develop and validate a tool for the perception of AI among healthcare consumers and apply the tool to assess AI's perception among mental health consumers in the Jordanian healthcare system. METHOD: A cross-sectional descriptive correlational design was utilized in the study. Data was collected from a convenience sample of 431 mental health consumers visiting mental health clinics of the International Medical Corps and university hospitals in Jordan. Structured interviews were conducted using an AI Perception (AIP) questionnaire developed by the authors. The questionnaire's content validity was assessed by an expert panel. Using Principal Component Analysis (PCA), the construct validity of the tool was evaluated, and its internal consistency was examined using Cronbach's alpha. Descriptive statistics were used to assess the levels of AI perception among participants. RESULTS: The final AIP tool consisted of 20 items across 4 domains and has demonstrated strong internal consistency across its four domains: AI acceptance and readiness (α = 0.92), AI perceived importance (α = 0.92), AI perceived risk (α = 0.9), and AI perceived challenges (α = 0.85). The construct validity of the four-domain structure of the tool was supported by PCA. Additionally, the mean scores for each domain indicated the average level of agreement with AI perception items among participants. Specifically, the mean score for AI acceptance and readiness was [Formula: see text]). AI perceived importance was (2.18 [Formula: see text]0.83), AI perceived risk was (2.58[Formula: see text]0.92), and AI perceived challenge was (2.78 [Formula: see text] 0.87). CONCLUSION: The findings of this study resulted in developing a valid and reliable 20-item tool to assess AI's perception among mental health consumers. The tool can be used to assess the predictors of AI's readiness among mental health consumers. Therefore, aiding policymakers and other stakeholders in understanding the AI adoption barriers from the perspective of end-users. In addition, this study developed the AIP tool that can be validated and used among other populations in future research.
31 July 2026
Read appraisal →Journal of radiation research
Prioritizing radiotherapy infrastructure in depopulating societies: a 2050 blueprint from Akita, Japan.
In super-aged and depopulating societies, repeated travel requirements for radiotherapy (RT) can amplify geographic inequities in access to advanced techniques. The aim of this study was to determine optimal placement of intensity-modulated radiotherapy (IMRT)-capable facilities in Akita Prefecture-a leading-edge case of Japan's demographic transition-using p-median optimization under population projections through 2050. Using 1-km mesh population data for 2020 and official projections for 2030, 2040 and 2050, we calculated one-way automobile travel times from each mesh centroid to RT facilities with the Open Source Routing Machine based on OpenStreetMap road data. A 60-min travel-time threshold defined access coverage. IMRT-capable facilities were identified from official insurance-designation lists. Optimal placement was determined using a population-weighted p-median model, with the number of facilities prespecified as one per 200 000 population. In 2020, 10 external-beam RT facilities provided near-universal 60-min coverage (99.99%; mean 12.8 min), whereas the single IMRT-capable facility covered only 70.3% (uncovered 285 273; mean 42.5 min). Optimized placement with five IMRT-capable facilities increased coverage to 99.99% (uncovered 89) and reduced mean travel time to 14.2 min. Under population projections, optimized placement maintained >99% coverage through 2050 with three facilities, and three core locations were consistently selected across all time horizons. These temporally stable core locations can serve as priority targets for long-term IMRT infrastructure investment in aging and depopulating regions.
27 July 2026
Read appraisal →PloS one
"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research.
Developing artificial intelligence (AI) algorithms for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other stakeholders together. By understanding AI as 'sociotechnical' where the social and the technical nature of the work and the models are inseparable, we explore the AI development workflow and how stakeholders navigate the challenges and tensions of sharing and generating knowledge across disciplines. We conducted an inductive thematic analysis of 13 semi-structured interviews with participants in early stages of AI-in-healthcare research consortia in the UK. Our findings identify that participants needed to adapt both the tools used for sharing and the information communicated according to their audience, particularly when working with those with a clinical or patient perspective. We identify the novelty of participating in AI research, how AI knowledge is shared, and the inclusion of clinician and patient stakeholder perspectives as key areas within collaborative AI practices in healthcare. These findings highlight that bringing AI into the mix can introduce new obstacles to interdisciplinary work.
24 July 2026
Read appraisal →PloS one
Textual overlap rather than domain alignment: A comparative study of fine-tuning strategies for specialised machine translation with large language models
General-purpose large language models (LLMs) may struggle in specialised machine translation, but the conditions under which fine-tuning improves translation performance remain unclear. This study compares full-parameter fine-tuning (FPFT) and parameter-efficient fine-tuning (PEFT) for Chinese-English political discourse translation using a purpose-built corpus and the Qwen3-14B model. Translation performance was assessed on three 50-item test sets using BLEU, ROUGE-L F1, METEOR, and BERTScore F1, together with BLEU pass-rate likelihood-ratio G2 tests, paired t-tests, and paired Cohen's dz for item-level score differences. The results reveal a clear contrast between unseen in-domain evaluation, maximum-overlap benchmarking, and semantically related but non-fine-tuned evaluation. On Test Set A and Test Set C, neither fine-tuning strategy produced a statistically significant BLEU pass-rate advantage over the base model, and paired tests across the continuous metrics did not show consistent fine-tuning gains. On Test Set B, which was sampled from the fine-tuning corpus, both fine-tuned models substantially outperformed the base model across all four metrics, with FPFT achieving the highest scores and PEFT providing a more computationally efficient alternative. These findings indicate that textual overlap between training and deployment data, rather than broad domain similarity alone, strongly conditions the observed benefit of fine-tuning. The study offers an empirically grounded framework for selecting fine-tuning strategies in specialised machine translation.
22 July 2026
Read appraisal →Journal of robotic surgery
Technical advancements in robot-assisted radical prostatectomy: systematic review and bibliometric analysis
Robot-assisted radical prostatectomy has transformed the surgical management of localized prostate cancer, yet its research landscape remains incompletely mapped. We conducted a systematic review and bibliometric analysis of 578 articles and conference papers from Scopus and Web of Science (2005-2026), following PRISMA guidelines, and analyzed publication trends, geographic and institutional distribution, author networks, journal output, keyword co-occurrence, and thematic evolution. The results reveal exponential growth in research, with an inflection around 2015-2016 and accelerated momentum after 2020, reflecting the widespread adoption and maturation of robotic programs. The United States leads in publication volume, followed by Italy and the rapidly growing contribution of China, while The University of Turin (Italy) emerges as the most productive institution due to its pioneering Retzius-sparing approach; the Journal of Robotic Surgery remains the leading publication venue. Thematic analysis demonstrates a progression from early feasibility studies to the standardization of nerve-sparing and continence-preserving techniques, advancing toward single-port surgery, augmented reality navigation, and AI-assisted intraoperative decision-making. Emerging technologies such as augmented reality, the NeuroSAFE protocol, and deep learning models for outcome prediction are particularly transformative, and geographic disparities are highlighted as critical priorities for future research.
21 July 2026
Read appraisal →JMIR nursing
Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illness Care: Umbrella Review
BACKGROUND: Artificial intelligence (AI)-based nursing interventions are increasingly being used to manage chronic illnesses; however, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis. OBJECTIVE: This umbrella review aimed to synthesize the evidence regarding the effectiveness of AI-based nursing interventions for chronic illness care and their subsequent impact on health care outcomes in clinical settings. METHODS: We conducted an umbrella review and prospectively registered the protocol. A systematic search of 5 electronic databases (PubMed, CINAHL, Cochrane Library, Scopus, and Web of Science) was performed to identify systematic reviews and meta-analyses published in English between 2021 and 2025. The methodological quality of the included studies was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. RESULTS: Eight high-quality systematic reviews were included, with machine learning identified as the predominant technology. Three primary outcome domains emerged: predictive, psychosocial, and hospital utilization. Due to measurement heterogeneity, the results were synthesized narratively. Our findings demonstrated that AI-based nursing interventions are effective in predicting adverse clinical events, unplanned hospital utilization, and health care costs. However, evidence regarding psychosocial outcomes remains insufficient. CONCLUSIONS: This review provides systematic evidence supporting the utility of AI in chronic illness management, particularly for improving predictive and utilization outcomes. These findings offer actionable insights for nursing leaders to integrate AI into clinical practice and education. Future research should prioritize rigorous empirical designs to further strengthen the evidence base for AI-driven nursing care.
17 July 2026
Read appraisal →BMC health services research
Computational forecasting using Swedish data in the vaccination phase of the COVID-19 pandemic: a systematic literature review deliberating modelling relevance for public health and healthcare.
BACKGROUND: The benefits of computational forecasting in the later phase of the COVID-19 pandemic when vaccines and clinical pharmaceutical interventions were available have seldom been assessed. We aimed to evaluate computational forecasting research applied to Swedish populations in the vaccination phase of the pandemic. METHODS: A systematic search was performed on March 8, 2024 in the electronic databases PubMed, Scopus, Cochrane library, Embase, Love platform and Epistemikos. An updated version of the Risk of bias Opinion Tool (ROBOT) was used to assess the quality of evidence reported in the papers identified in the search. The articles fulfilling the quality criteria were assessed for suitability for meta-analysis. Data were extracted from the selected articles for synthesis of characteristics, and a thematic analysis was used for a qualitative synthesis of the contents. RESULTS: Of 2034 unique publications identified in the database search, 6 articles satisfied the selection and quality criteria. Variability in the reporting of forecasting performance results was found to make a quantitative meta-analysis of forecast performance infeasible. The data synthesis showed that statistical modeling using Bayesian calibration was the most common methodological approach. No external model validation was reported, but 5/6 articles included internal model corroboration data. The primary theme resulting from the qualitative synthesis of article content was design or refinement of computational models with demonstration of model use in health service practice as a secondary theme. None of the articles referred to health service policymaking as the primary research context. CONCLUSION: Computational forecasting research using Swedish population data from the vaccination phase of the COVID-19 pandemic was deployed in a model design context. While methodological knowledge was developed, most of the research was not initiated to solve the public health and healthcare problems at hand. Our results indicate that the alignment between computational forecasting research and policymaking needs in the vaccination phase of pandemics can be enhanced.
13 July 2026
Read appraisal →Depression and anxiety
A Systematic Review of Community Pharmacy-Led Depression Services: Service Components, Outcomes, and Implementation Barriers and Facilitators
BACKGROUND: Depression is the most common mental ill health condition, and its prevalence is increasing. Despite this, its treatment is variable often due to a lack of capacity within healthcare systems to support this vulnerable population. Community pharmacy staff could offer additional support. This systematic review identifies depression services led by community pharmacy staff, their service components, outcomes, and barriers/facilitators to their implementation. METHODS: Four bibliographic databases were searched (Medline, EMBASE, PsycINFO, and CINAHL) from 2000 onwards. Title/abstract and full-text screening was conducted. Data on the service components were mapped to the Template for Intervention Description and Replication (TIDieR). Clinical, humanistic, economic, and service outcomes were charted. Barriers and facilitators were mapped to the Consolidated Framework for Implementation Research (CFIR). Quality assessment was performed using the Quality Assessment with Diverse Studies (QuADS) tool. RESULTS: Fifty studies were included. Seventeen studies identified general attitudes regarding community pharmacy services for depression, which were generally supportive. The majority (n = 33) explored an implemented depression service focusing on depression advice/education (n = 15), screening (n = 12), medication adherence (n = 4), medication review (n = 1), and disease therapy management (DTM) (n = 1). Clinical outcomes were the most commonly reported types of outcomes, with varied results. Key facilitators were linked to the pharmacy 'inner setting', including accessibility of community pharmacies, the use of private consultation rooms, and skills/training of staff. Barriers to service delivery related often to the external 'outer setting', especially societal stigma, low public awareness of pharmacy roles, funding constraints, and limited collaboration with other healthcare professionals. CONCLUSION: This international review identified a range of different services that community pharmacy staff can deliver to support people with depression, ranging from supporting diagnosis, health literacy, and management plans. The accessibility of community pharmacies for depression service delivery warrants further investigation. However, limited empirical evidence of clinical and economic outcomes and reported implementation barriers may complicate broader implementation.
13 July 2026
Read appraisal →BMC medical informatics and decision making
A systematic review of patient decision aids for hypertension
BACKGROUND: Hypertension is a major cause of premature death and a modifiable risk factor for cardiovascular disease. Effective management includes both pharmacological treatment strategies, including antihypertensive medications, and non-pharmacological treatment strategies, such as lifestyle changes. Shared decision making (SDM) between patients and healthcare professionals is essential to determine the most appropriate treatment for hypertension. Patient decision aids (PDAs) facilitate SDM by providing information on treatment options and helping patients to clarify their values and preferences. However, there is no comprehensive comparison of PDAs for hypertension. OBJECTIVE: This systematic review aimed to identify and evaluate PDAs for arterial hypertension using the International Patient Decision Aid Standards (IPDAS) criteria. METHODS: A comprehensive search of bibliographic databases (PubMed, Embase) and gray literature (Google Scholar, Google) was performed in October 2023 and updated in April 2026. Studies and tools were included if they presented a PDA for patients with arterial hypertension, met the IPDAS definition of a PDA and were in German or English. Two researchers screened the literature, extracted data and assessed the quality of the PDAs using the IPDAS Minimal Criteria. Data were synthesized narratively. RESULTS: Of 1,874 records screened, five PDAs met the inclusion criteria. These PDAs were developed between 2019 and 2024 and originated from the United States, United Kingdom, and Germany. All PDAs addressed the two option areas of medication and lifestyle but differed in the number of individual treatment options presented (range: 2-12 options) and the level of detail in which the options were addressed. All five PDAs were accessible online. Common design features of the PDAs included an option grid format for presenting treatment options with associated benefits and adverse effects, visuals and graphics to enhance comprehension, and value clarification. Quality assessments showed varying levels of compliance with the IPDAS criteria, with overall scores ranging from 49/116 to 87/116. CONCLUSIONS: Few PDAs exist despite the prevalence of hypertension. PDAs varied in quality and format, highlighting gaps in the development process. Further research is needed to evaluate the effectiveness of PDAs for hypertension and to determine which formats are most effective in supporting SDM in different patient populations. CLINICAL TRIAL NUMBER: Not applicable.
13 July 2026
Read appraisal →BMJ open
Shared decision making in pain care: a systematic review study protocol of decision aids
INTRODUCTION: Shared decision making (SDM) in healthcare is an ethical imperative and essential to patient-centred care. SDM is particularly useful when several preference-sensitive treatment options exist and in the setting of chronic conditions or longitudinal management. Chronic pain management embodies these characteristics, yet SDM often remains insufficient in this population. Decision aids are designed to facilitate SDM by helping patients and clinicians understand treatment options, clarify patient values, and guide collaborative decision processes. Despite their importance, the use of decision aids in pain management is inconsistent and their reported effectiveness has been variable. However, the current landscape of decision aids for pain management has not been described. This lack of understanding of what tools exist, how they are structured, what decisions they address and how they were developed makes it difficult to advance implementation efforts or identify meaningful gaps in available resources. As such, the purpose of this systematic review is to identify and characterise decision aids for pain management across the lifespan. Specifically, this review will describe the clinical decisions addressed, decision aid structures and delivery formats, development processes and outcomes used to evaluate the impact of existing decision aids. METHODS AND ANALYSIS: Electronic searches were performed in PubMed, CINAHL and Ovid Embase from inception through January 2026. Studies will be assessed for quality using the Mixed Methods Appraisal Tool. Data will be extracted and presented with the aim of describing the: (a) content and structures of pain-related decision aids, (b) development processes used to create existing pain-related decision aids and (c) outcome measures used to evaluate the impact of decision aids in clinical care. ETHICS AND DISSEMINATION: This review does not require ethics approval. Findings will be disseminated to clinicians, researchers and patients through journal publications, conference presentations and in collaboration with patient partners. PROSPERO REGISTRATION NUMBER: CRD420251085288.
11 July 2026
Read appraisal →BMJ open
The Cyber Paranoia and Fear Scale-Updated (CPFS-U): development and implications for digital health engagement
OBJECTIVES: To update and revalidate the Cyber Paranoia and Fear Scale to reflect current technological contexts and examine its relevance to digital health readiness and engagement. METHODS: Using an online community sample (n=433), exploratory factor analysis was conducted to examine the factor structure of the revised item pool. Items were refined through consultation with Patient and Public Involvement and Engagement groups to ensure contemporary relevance and clarity. RESULTS: Analysis supported a four-factor structure representing artificial intelligence (AI) and digital dependence, technological risk awareness, perceived data vulnerability and surveillance-related mistrust. The updated Cyber Paranoia and Fear Scale-Updated (CPFS-U) demonstrated good internal consistency and supported construct validity. Cyber-paranoia and fear were conceptually and empirically distinct from general paranoia and anxiety, highlighting the specific cognitive and emotional responses elicited by digital technologies. CONCLUSIONS: The CPFS-U offers a psychometrically robust, modernised measure for understanding individuals' responses to digital and AI-based technologies. Its application in digital health research and practice can inform risk communication, user engagement strategies and the design of trustworthy digital interventions. By identifying individuals who may disengage due to online mistrust, the CPFS-U has the potential to inform more inclusive and psychologically informed digital health systems.
8 July 2026
Read appraisal →JMIR research protocols
Evaluation and Comparison of Latent Health Risk Prediction Models for Clinical Triage: Protocol for a Mixed Methods Study
BACKGROUND: Clinical triage requires integrating multiple information sources to identify patients at risk of deterioration. Tools capturing global health assessments beyond disease-specific scores are being developed using either bottom-up aggregation of simple indicators or top-down machine learning from large datasets. Their alignment with expert clinical judgment remains poorly characterized. OBJECTIVE: This study evaluates 2 latent health measurement approaches: Frailty Index-laboratory, a transparent bottom-up tool aggregating laboratory abnormalities via deficit accumulation theory, and ETHOS-ARES (Enhanced Transformer for Health Outcome Simulation-Adaptive Risk Estimation System), a transformer-based foundation model generating multidimensional patient representations from electronic health records. We assess whether each tool's severity rankings align with clinical consensus and whether they offer utility in triage decisions. METHODS: In this 3-phase mixed methods study, at least 30 clinicians across hospital specialties reviewed 20 emergency department presentations derived from Medical Information Mart for Intensive Care IV-Emergency Department. Phase 1 compared unaided clinician severity and urgency judgments against model outputs using Spearman rank correlation, with a Turing-inspired indistinguishability test assessing whether model rankings fell within the distribution of clinician assessments. Phase 2 allocated clinicians to receive Frailty Index-laboratory or ETHOS-ARES outputs, measuring anchoring effects via within-person pre-post comparisons and exploring clinical utility through semistructured interviews analyzed using the Framework Method. RESULTS: Ethics approval was granted in June 2025 (KCL Research Ethics Office; MRSP-24/25-48707). Recruitment began in October 2025 (32 clinicians recruited as of manuscript submission), with data collection expected to be completed in January 2026 and analysis planned for March or April 2026. CONCLUSIONS: This study will quantify model-clinician agreement, measure anchoring effects, and generate qualitative insights on utility, trust, and adoption. The findings will inform the implementation of latent health measurement tools in clinical practice and provide a framework for the early-stage evaluation of artificial intelligence-based clinical decision support systems.
5 July 2026
Read appraisal →Physiotherapy theory and practice
Physical therapists' perspectives on a large language model-powered knowledge translation tool for guideline adherence: A qualitative focus group study.
BACKGROUND: Clinical practice guidelines support healthcare professionals in making evidence-based decisions, yet guideline adherence among physical therapists remains inconsistent. To address this gap, a prototype digital knowledge translation tool powered by a large language model (LLM) was developed, with content based on two exemplary high-quality German national guidelines. OBJECTIVE: To (1) explore the experiences of German physical therapists using the tool, (2) assess their perspectives on its utilization in clinical practice, and (3) compare perceptions between outpatient and inpatient settings. METHODS: Six focus group interviews were conducted: three in a university hospital inpatient setting and three in outpatient physical therapy practices. Discussions were analyzed using qualitative content analysis with inductive and deductive coding. RESULTS: Twenty physical therapists (11 inpatient, 9 outpatient) participated. Overall experiences were positive, though prolonged response times were criticized. Utilization was thought to depend on time availability and workplace digitization. The tool's potential assisting with clinical questions was highlighted. No considerable differences in experiences across settings were noted. Inpatient therapists envisioned using the tool between sessions for personal knowledge enhancement, whereas outpatient therapists anticipated utilization during sessions for patient education. CONCLUSION: LLM-based knowledge translation tools may contribute to improving guideline adherence among physical therapists. Successful implementation requires assessment of digital infrastructure, relevance to clinical needs, and users' digital literacy. Further research should evaluate the quality of LLM-generated summaries to ensure validity and trustworthiness, and optimize the tools' usability regarding speed and content. Development should also prompt ethical considerations about their role in clinical decision-making and patient care.
2 July 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Large language models accurately identify decision reasons in verbal reports.
Understanding the reasons behind human choices under risk is a central goal of decision scientists, but traditional methods relying on behavioral data are limited by strict invariance assumptions. We introduce a scalable analytical framework using large language models (LLMs) to analyze verbal reports and identify articulated reasons for choice between monetary lotteries. A validated LLM accurately identified predefined decision reasons in participants' free-text reports, aligning with their actual choices in 95% of trials. Our analysis reveals that the reasons behind people's decisions vary systematically and are driven more by the structure of the choice problem than by individual differences. Crucially, reasons identified from verbal reports yield more parsimonious and informative representations of decision processes compared to those inferred from choices alone; furthermore, problem-specific reason profiles achieve out-of-sample prediction accuracy that is competitive with established computational models. This work demonstrates that verbal reports are a rich data source and our analytical framework can unlock their potential, delivering results that challenge the field's foundational invariance assumptions and pave the way for more context-sensitive and interpretable models of human decision making.
1 July 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 →Familial cancer
When screentime fails: initiative to improve completion of hereditary cancer genetic testing after telemedicine counseling.
Telemedicine has broadened access to genetic counseling while maintaining high levels of patient satisfaction. However, emerging evidence suggests lower completion rates of genetic testing following virtual visits compared with in-person encounters. This study evaluated genetic testing completion rates among patients who underwent cancer risk assessment via telemedicine and assessed the effect of a structured follow-up telephone reminder on genetic testing completion. We conducted a retrospective review of a quality improvement initiative - Genetic Engagement via Navigation and Encouragement (GENE CALL) - designed to address incomplete genetic testing following telemedicine counseling. The study included all patients who underwent virtual cancer risk assessment between September 2023 and July 2024 at a single urban academic cancer genetics program. Patients who expressed interest in genetic testing but had not scheduled specimen collection were contacted by telephone as a reminder. During the call, patients were asked about ongoing interest in testing and barriers to scheduling; they were also offered assistance completing specimen collection. Among 647 patients evaluated via telemedicine, 469 (72.5%) expressed interest in genetic testing. Of these, 290 (61.8%) had scheduled or completed testing, while 179 (38.2%) had not. Among those who had not scheduled testing, 120 (67.0%) were successfully reached by telephone, and 98 (81.7%) confirmed ongoing interest in completing testing. At 3-month follow-up, 48 (40.0%) of the 120 contacted patients had completed testing, compared with 13 (22.0%) of 59 patients who were not reached (p = 0.017). Telephone follow-up is an effective strategy to improve genetic testing completion after virtual counseling.
27 June 2026
Read appraisal →BMJ open
Effectiveness of peer support interventions to improve mental health outcomes after miscarriage: a systematic review and call for high-quality evidence.
OBJECTIVES: Peer support is being integrated into the new maternal mental health services in England to further the development of the recovery approach in relation to loss. Psychological support after miscarriage (pregnancy loss prior to viability) is often overlooked, despite significant psychological morbidity. This systematic review explored the effectiveness of peer support interventions to improve mental health outcomes after miscarriage. DESIGN: Systematic review DATA SOURCES: A comprehensive systematic search across nine databases (MEDLINE, CINAHL, APA PsycINFO, Web of Science (all databases), EMBASE, CENTRAL, LENS.org, British Nursing Index and Health Management Information Consortium) was conducted in June 2025. Grey literature was identified through website searching, contact with topic experts and a national call for evidence. ELIGIBILITY CRITERIA: Study designs with a quantitative evaluative component or mixed-methods studies reporting effectiveness were eligible if they involved women and/or partners who had experienced miscarriage and been offered a peer support intervention. Any peer support versus control (no treatment, wait list and usual care) or peer support versus another psychosocial intervention was eligible for inclusion. Studies that report any of the following broad groups of outcomes (whether validated measures or by self-report) were eligible for inclusion: (a) personal recovery, (b) mental health recovery, (c) health service use and (d) social outcomes. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers used standardised methods to search and screen for eligible studies. RESULTS: Of the 4342 titles screened, 100 potentially relevant full-text papers were retrieved and screened resulting in seven randomised controlled trials and two controlled trials identified across 10 papers. Of these, seven did not evaluate a peer-led intervention, one reported only on women who had experienced pregnancy loss later than 24 weeks gestation, and one reported a peer support intervention for those who experienced pregnancy loss at any age of gestation but did not disaggregate data for those who experienced miscarriage. Thus, no studies were eligible for inclusion. This indicates a significant gap in the current literature. The inconsistencies and limitations in existing research approaches are explored in detail. CONCLUSIONS: This systematic review has identified an evidence gap as there is currently no robust evidence for the effectiveness of peer support interventions after miscarriage. Given the drive for the inclusion of peer support in new maternal mental health services, there is therefore a need for targeted intervention research to provide reliable evidence to determine effective peer support interventions for this population. PROSPERO REGISTRATION NUMBER: CRD42024518248.
25 June 2026
Read appraisal →Journal of medical Internet research
Policy Considerations for National Virtual Hospitals: Global Evidence and the Seha Virtual Hospital Model
Health systems worldwide face growing pressure from population aging, multimorbidity, and rising emergency admissions, prompting reconsideration of traditional inpatient care models. In response, digitally enabled models such as tele-intensive care unit (tele-ICU) programs, hospital-at-home services, virtual wards, and other remote specialist pathways have expanded, particularly after the COVID-19 pandemic accelerated telemedicine adoption and cross-site virtual staffing. However, nationally coordinated, multispecialty virtual hospitals remain uncommon worldwide, and robust evidence on their system-level effects is still limited. As a result, policy discussions about national virtual hospitals must often draw on evidence from related virtual-care models rather than from mature national implementations. This viewpoint synthesizes representative international evidence from tele-ICU systems, hospital-at-home programs, virtual wards, telestroke networks, and other condition-specific virtual-care pathways, and examines Saudi Arabia's Seha Virtual Hospital (SVH) as a national case study to identify policy lessons relevant to the design, governance, and evaluation of national virtual hospitals. Across settings, these models suggest that remote and digitally supported care can achieve outcomes comparable to in-person hospital care when patient selection is appropriate, escalation and transfer pathways are explicit, monitoring intensity matches clinical risk, and multidisciplinary teams are integrated into local workflows. Tele-ICU programs have reported reductions in intensive care mortality and length of stay under well-structured organizational models, while hospital-at-home and virtual-ward programs have shown comparable safety, reduced hospital usage, and improved patient experience among selected patient groups. Telestroke networks likewise demonstrate outcomes comparable to specialist in-person care in acute stroke pathways. Nevertheless, the evidence base remains heterogeneous and strongly context-dependent. Much of the literature is short-term, with limited consistent evidence on long-term outcomes, caregiver burden, cost-effectiveness, workforce implications, and digital equity. SVH illustrates the emerging implementation of a centralized national virtual hospital model. Launched in 2022 under Saudi Arabia's Vision 2030 Health Sector Transformation Program, SVH operates as a national telehealth hub embedded within the country's broader digital-health ecosystem and links hospitals across the Kingdom to specialized clinical expertise. Its service portfolio includes urgent and critical care consultations, specialized virtual clinics, multidisciplinary case discussions, and supportive diagnostic services. Early reports indicate rapid operational expansion, broad institutional participation, and national-scale feasibility. However, independent comparative evidence evaluating SVH's effects on mortality, readmissions, length of stay, cost-effectiveness, equity, and workforce sustainability remains limited. National virtual hospitals should therefore be understood as evidence-generating health-system innovations rather than fully validated care models. Sustainable scale-up requires embedding rigorous prospective evaluation within implementation, aligning financing mechanisms with substitution of inpatient care, establishing clear governance and regulatory frameworks, and addressing digital inclusion and workforce sustainability. These considerations can help guide policymakers and health-system leaders in the accountable, equitable, and evidence-informed development of national virtual hospital programs.
25 June 2026
Read appraisal →JMIR formative research
Clinician Decision-Making Around Offering Home Video Telehealth Visits: Qualitative Study
BACKGROUND: Use of in-home video telehealth rapidly expanded in response to the COVID-19 pandemic, including at Veterans Affairs (VA), a forerunner in telehealth. Despite this uptick, differences in use by patient age and rurality created a digital divide that persists to this day. While clinicians frequently cite patients' older age and lack of technical skills as barriers to in-home video telehealth, it remains unclear how clinicians decide whether to offer video visits to patients and to what extent these beliefs may hinder offering video visits to older adults. Gathering perspectives from clinician users of in-home video telehealth may illuminate opportunities to ensure continued access to care through solutions such as telehealth. OBJECTIVE: This study aimed to examine clinician decision-making around the offer of in-home video telehealth to understand how interprofessional clinicians determine to whom they offer in-home video telehealth and what factors (organizational, personal, or attitudinal) influence their decision. METHODS: We conducted a qualitative study by using semistructured interviews. Participants were interprofessional clinicians (N=16) employed by 11 different VA hospitals and included 1 clinical pharmacist, 6 medical doctors, 2 nurse practitioners, 1 occupational therapist, 3 psychologists, 1 physical therapist, 1 speech-language pathologist, and 1 social worker. All the participants had at least some experience using in-home video telehealth from locations across VA (the largest integrated health care system in the United States) and were interviewed over a 6-month period. Interviews focused on clinicians' use of video telehealth and the decision-making process involved in offering in-home video telehealth. We used directed content analysis with a rapid analytic approach, given the time-pressured nature of our project. RESULTS: This study revealed that clinician decision-making around offering in-home video visits is complex and influenced by several domains, namely, (1) clinician factors, including experience with video and perceived benefits of video; (2) appointment factors, including the visit's clinical goal; (3) clinician-reported patient factors, including age and willingness to try video; (4) patient social context, including caregiver availability; (5) geographical factors, such as availability of reliable high-speed internet and patient distance from the medical center; and (6) health system factors, including technical support and clinician ability to work from home. Access to in-home video telehealth may be facilitated by clinician familiarity and confidence with telehealth technology, strategies to improve patients' technical skills, and support for caregivers. Infrastructure also plays an important role, including device availability, broadband reliability, and clear protocols for matching services to video visits. CONCLUSIONS: Findings highlight the importance of clinician competency in telehealth, patient and caregiver digital readiness, and a supportive technology infrastructure to equitable in-home video care. In addition, improved guidance for specific clinical services and awareness of potential biases may enable consistent, accessible telehealth delivery for older adults and medically complex populations.
20 June 2026
Read appraisal →BMC geriatrics
The association between smart elderly care and subjective well-being among older adults in China: the mediating roles of health status and social activities
BACKGROUND: Under the initiative of China's "Internet+" strategy, smart elderly care reflects the government's capacity to bridge the digital divide and provide inclusive care services for older adults. Though studies have identified positive associations between smart elderly care and health outcomes, empirical research examining its impact on older adults' subjective well-being remains limited. METHODS: This study utilizes data from the 2021 Chinese Longitudinal Healthy Longevity Survey (CLHLS, N = 8,950), which includes multifaceted information on individuals aged 65 and above, covering their personal characteristics, health status, family information, and community elderly care services. A logit model is employed to investigate the association between smart elderly care and SWB. The mediating effects are tested using the bootstrap resampling method. To address endogeneity concerns, instrumental variable (IV) approaches including IV-Probit and 2SLS are applied as robustness checks. RESULTS: The results reveal a positive correlation between smart elderly care and older adults' subjective well-being (β = 0.101, p < 0.01). This association is partially mediated by both health status (proportion mediated: 38.47%) and social activities (proportion mediated: 2.65%). Heterogeneity analyses show that this positive association holds consistently across both rural and urban older adults, and is more pronounced among those with lower digital literacy. CONCLUSIONS: This study challenges the prevailing assumption that the "digital divide inevitably isolates the older adults from participating in a digitized society." By integrating digital technologies into older adults care services, we demonstrate that smart elderly care is positively associated with bridging technological barriers and addressing the unmet needs of aging populations. These findings further propose a synergistic mechanism-policy innovation, technological adaptation, and service optimization, to build an inclusive older adults care ecosystem, offering evidence-based insights for policymakers in aging societies to balance equity and efficiency in digital transformation.
14 June 2026
Read appraisal →Journal of medical Internet research
Uptake of Clinical Decision Support Systems Among Health Care Professionals in Six European Countries and the United States: Cross-Sectional Survey Within the I-CARE4OLD Project
BACKGROUND: The use of Clinical Decision Support Systems (CDSS), such as clinical decision rules, algorithms, or machine learning-based applications, has gained attention in recent years. However, their adoption and effectiveness may vary across different health care systems and settings. For a CDSS to be adopted, it must effectively address the practical issues encountered by professionals; however, little research has been done to identify these needs and requirements. OBJECTIVE: This study aims to describe and compare the current use of various decision-support and prediction tools in long-term care for older people across health professionals from 6 European countries and the United States. METHODS: This study analyzed survey data from a CDSS pilot study in a purposive sample of health professionals working with older adults with complex chronic conditions from six European countries and the United States. The survey included participants' general background information, their current use of decision support tools, and their attitudes on the potential benefits of CDSS. About 20 participants were sampled per country. Closed responses were analyzed using correlation coefficients and regression models, while open-ended responses were clustered in a qualitative manner, categorizing each response. RESULTS: A total of 151 professionals (mean age 45.5, SD 11.6 years, 71.5%, 108/151 female) participated in the pilot study. Most participants were physicians (85/151, 56.3%) or nurses (57/151, 37.7%). About 51% (78/151) of the participants reported using CDSS, while 22.4% (34/151) used predictive CDSS, showing important variation across samples from the seven countries. The regression model for comfort with technology showed a positive association for openness to new technologies (β=0.622; P<.001), although an inverse significant association was found for age (β=-0.022; P<.001). No significant associations were found for the actual use of CDSS. Participants reported using CDSS mainly for diagnostic purposes or for guideline implementation, not aimed at prognostic information. In contrast, examples of prognostic tools were most frequently mentioned by respondents as being valuable improvements to clinical practice. CONCLUSIONS: While some countries' samples reported well-integrated digital health infrastructures and higher CDSS adoption rates, others still face challenges in implementing these. However, we found multiple examples of emerging tools, and at the same time, an important demand for predictive CDSS. Our findings highlight the need for improvement of current CDSS implementation and both development and implementation of particularly predictive CDSS.
14 June 2026
Read appraisal →Journal of medical Internet research
Telehealth Scale and Artificial Intelligence Adoption Tiers Across Clinical and Operational Domains in US Hospitals: Cross-Sectional Study
BACKGROUND: Telehealth expansion and artificial intelligence (AI) adoption are often described as parallel dimensions of health system digital transformation. However, whether telehealth scale is associated with hospital AI adoption and whether this relationship varies across hospital settings remain unclear. OBJECTIVE: This study examined the association of telehealth scale with clinical and operational AI adoption tiers in US hospitals and assessed whether these patterns differed by telehealth reporting behavior and geography. METHODS: This cross-sectional study included 6173 US acute care hospitals using linked 2024 American Hospital Association Annual Survey and Information Technology Supplement data and 2023 Healthcare Cost Report Information System data. Telehealth scale was parameterized using log-transformed telehealth volume, a telehealth nonreporting indicator, and a reported-zero telehealth indicator. Clinical and operational AI adoption tiers were derived from hospital-reported AI capability items and classified into 3 tiers. Both outcomes were modeled using multioutput gradient-boosted tree classifiers, and model behavior was interpreted using Shapley additive explanations, partial dependence plots, and stratified analyses by the Core-Based Statistical Area category. RESULTS: Telehealth volume was the strongest predictor of both clinical and operational AI adoption tiers and had a larger contribution to the clinical AI model. Telehealth nonreporting was common, occurring in 57% (3521/6173) of hospitals, and was concentrated among hospitals in the lowest clinical AI adoption tier, accounting for 91.4% (3145/3441) of hospitals with no reported clinical AI adoption. Higher telehealth volume was associated with a steep increase in predicted clinical AI adoption tiers at lower telehealth volumes, followed by a plateau at higher volumes. At similar telehealth volumes, rural hospitals showed weaker telehealth-attributed contributions to predicted clinical AI adoption tiers than metropolitan hospitals. Supplementary analyses suggested that telehealth reporting status and telehealth intensity reflected related but distinct structural processes. CONCLUSIONS: Telehealth scale was strongly associated with hospital AI adoption tiers, especially clinical AI adoption tiers. These findings suggest that telehealth capacity may serve as a practical hospital-level marker of broader digital readiness for AI adoption, but the cross-sectional design does not establish whether telehealth expansion precedes or causes AI adoption. Hospitals with telehealth nonreporting and rural hospitals may face additional structural barriers that limit the translation of digital capacity into AI maturity. Policies to reduce inequities in hospital AI adoption may therefore need to pair telehealth expansion with implementation support, interoperability capacity, and organizational resources.
14 June 2026
Read appraisal →BMJ open
Diabetic retinopathy treatment cascade and care continuum in the USA: a systematic review
OBJECTIVES: To characterise engagement across the diabetic retinopathy (DR) care continuum in the USA using a cascade-of-care framework and identify gaps contributing to preventable vision loss. DESIGN: Systematic review. DATA SOURCES: From September to November 2025, with an updated search in March to April 2026 using the same eligibility criteria, we systematically searched Ovid MEDLINE and Ovid Embase supplemented by reviewing reference lists of relevant articles and opportunistic searches of the Centers for Disease Control and Prevention publications. ELIGIBILITY CRITERIA: We included English-language US-based cross-sectional, cohort and case-control studies and systematic reviews containing US-based data published between 1 January 2018 and 31 December 2025 relevant to one of the defined DR cascade stages: (1) diagnosis of diabetes, (2) adherence to DR screening, (3) diagnosis of DR, (4) adherence to DR care and (5) DR-related blindness. We included systematic reviews only to inform DR-related blindness, where primary data were limited and excluded them from other stages to avoid double-counting. Exclusion criteria included studies not relevant to one of the defined DR cascade stages and editorial, perspective or commentary pieces. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies, extracted data and assessed risk-of-bias using the Newcastle-Ottawa Scale and Risk of Bias in Systematic Reviews tool. We synthesised data narratively and organised via the DR treatment cascade framework. RESULTS: Of 14 893 studies screened, 46 met the inclusion criteria. Cascade analysis revealed substantial losses in patient engagement at three stages: (1) only 15.5%-78.7% (median 59.4%, IQR 33.9%-74.0%) of individuals with diabetes obtain biennial DR screening; (2) a substantial 54.9%-88.5% (median 70.1%, IQR 62.5%-79.3%) of individuals with DR are unaware of their diagnosis; (3) only 30.9%-62.7% (median 52.0%, IQR 40.9%-59.1%) of individuals diagnosed with DR are initially linked to care and 55.3%-77.8% (median 70.3%, IQR 59.2%-77.7%) have a lapse in DR follow-up. CONCLUSIONS: This review identifies major gaps in the DR care continuum, particularly in diagnosis awareness, linkage to care and follow-up adherence. The cascade framework highlights key points of disengagement and provides a basis for prioritising future research.
13 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Developing the multidisciplinary and multispecialty workforce and the systems needed to sustain high-quality care from diagnosis through long-term survivorship
With the growing population of individuals living with and beyond cancer-who often have a variety of complex health needs across their lifetimes-coordinated multidisciplinary, multispecialty expert care is needed to ensure comprehensive survivorship cancer care beginning at diagnosis. Guided by the July 2023 US National Academies of Sciences, Engineering, and Medicine workshop, we describe the evidence and propose steps to (1) identify and promote clinician competencies through education and training; (2) improve communication and care coordination, including leveraging the electronic health record and digital health innovations; (3) examine health system interventions and financial strategies to facilitate multidisciplinary, multispecialty survivorship care; and (4) identify policy, payment, and advocacy challenges and opportunities to achieve equitable, accessible, high-quality care. Although the recommendations are centered on the United States, their applicability to other countries should also be explored.
1 June 2026
Read appraisal →BMC health services research
Anticipated barriers and facilitators of implementing a digital behavioral treatment for chronic pain: a qualitative interview study
BACKGROUND: In digital health innovations, sustainable implementation should be considered from the initiation of the development to ensure that the solution fits into the existing healthcare system. This study aims at identifying barriers and facilitators for the prospective implementation of a digital behavioral intervention for chronic pain in the Swedish healthcare system, as perceived by key stakeholders. METHODS: Eight semi-structured individual interviews were conducted with stakeholders (n = 2 IT developers, n = 4 healthcare managers, n = 2 healthcare professionals). Qualitative data were analyzed using a framework analysis following the Consolidated Framework for Implementation Research (CFIR). RESULTS: A total of 86 facilitators and 38 barriers to prospective implementation were identified across stakeholder interviews. Facilitators included perceived relevance of the digital intervention and organizational readiness. Identified barriers primarily related to insufficient training and supervision, unclear decision-making processes (e.g., specific actions, responsibilities of stakeholders), and the lack of systematic needs assessment to guide implementation planning. CONCLUSION: Overall, findings indicate a high level of openness toward digital interventions within the implementation context. While implementation context largely demonstrates readiness, successful integration will depend on proactively addressing identified barriers. Awareness of these factors enhances the likelihood of successful and sustainable implementation.
1 June 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cancer patient distress and health service use is linked with carer distress: evidence from a systematic review and meta-analysis
BACKGROUND: Family carers (caregivers) of cancer patients often have poor mental health, which may adversely affect patient wellbeing and health service use. We examined the evidence for this using a systematic review and meta-analyses. METHODS: We systematically searched online databases for studies reporting associations between carer psychological health and either: (a) psychological health of patients with cancer they cared for; or (b) health service use in carers or patients. Studies' risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tool. We undertook meta-analyses to estimate pooled correlations for the most commonly reported associations between carer and patient psychological health. For associations between carer psychological health and carer and/or patient health service use, we conducted a narrative synthesis. RESULTS: Our search identified 11,911 records, 169 of which were eligible. The majority of studies were of high-to-moderate quality. Carers who were distressed, depressed, anxious, or had poor scores in the mental component of quality of life were significantly more likely to be caring for patients with these same outcomes (pooled effect sizes ranged from 0.28 to 0.42; all p < 0.001). Subgroup analyses by gender, disease stage, and study quality revealed no substantial differences. Carers with poor psychological health used general practice, mental healthcare, and hospital services more frequently than those who were psychologically healthy, and the patients they cared for used more medications and had more frequent emergency presentations. CONCLUSION: The inclusion of carers alongside patients in early psychosocial care may improve family outcomes and reduce health service use.
1 June 2026
Read appraisal →BMJ health & care informatics
Routine data are not 'dirty' data: how to approach using routine data for research.
The rise of electronic patient records has led to an abundance of routine healthcare data, and the development of NHS Secure Data Environments (SDEs) is transforming their potential for research. However, routine data differ fundamentally from research data; they are generated to support care processes rather than answer predefined research questions. This creates challenges when they are analysed without understanding the context in which they were produced.Using real examples from practice, this paper illustrates the variety of ways routine data can be shaped and how this leads to misinterpretation if treated as merely 'dirty' research data. These examples underpin practical strategies for researchers, including analysis at or by source and working closely with local data providers to understand how a dataset was created.We argue that SDEs must foster strong links between data users and creators to preserve data provenance and support accurate interpretation. By approaching routine data as meaningful records of healthcare processes-rather than flawed research datasets-researchers can fully realise their potential to drive healthcare improvement.
1 June 2026
Read appraisal →BMC health services research
Does an app make patients happy? Impact of a novel medical history app on patient satisfaction in urgent care consultations in Germany: cluster-randomized interventional trial 'DASI'.
BACKGROUND: Patient satisfaction is an important indicator of healthcare quality. However, time constraints in primary care limit effective communication and history-taking. Digital medical history systems have the potential to improve the quality of medical consultations by enabling patient preparation and providing physicians with comprehensive pre-consultation information. This study aimed to evaluate the impact of a novel medical history-taking app (DASI app) on patient satisfaction in out-of-hours practices (OOHP) in Germany. METHODS: We conducted a two-center, cluster-randomized trial over 12 months. Within each practice, weeks were randomized to either an intervention or control group, resulting in a cluster-randomized trial (CRT) with clustering in weeks within the same practice. Patients either used the DASI app before consultations (intervention group) or received standard care (control group). The DASI app is a patient-facing tool that guides patients through a dynamic questionnaire adapted to the selected complaints and previous answers. Patient satisfaction was measured using 17 items from the EUROPEP instrument covering relation and communication, medical care, and information and support. Additionally, we collected sociodemographic data. Statistical analyses included Mann-Whitney U tests for individual items and t-tests for domain scores. Analyses were performed with R, version 4.5.2. RESULTS: Among 1,460 approached patients, 1,040 (71%) were enrolled and 1,034 included in analyses. Patient median age was 31 years, with 60% female participants. Patients in the intervention group (n = 496) showed significantly better ratings in 12 of 17 EUROPEP items compared to controls (n = 538) and demonstrated significantly higher satisfaction across all three EUROPEP domains. Between 78 and 96% of patients rated care as excellent or very good, with intervention group patients more likely to select top-level evaluations in 9 of 17 items. CONCLUSIONS: Digital medical history taking significantly enhanced patient satisfaction in urgent care settings. The app likely improved satisfaction through multiple pathways including increased patient empowerment, better consultation preparation, and more efficient physician-patient interactions. These findings demonstrate that user-friendly digital tools can meaningfully enhance patient experience without disrupting existing workflows, supporting healthcare digitalization efforts while maintaining high-quality patient-centered care. TRAIL REGISTRATION: German register for clinical trials (DRKS00026659; date of registration: 2021-11-03).
1 June 2026
Read appraisal →Critical care explorations
Linking the Virtual Pediatric System and Pediatric Hospital Information System Databases to Create Novel PICU Research Datasets
BACKGROUND: Lack of a robust, national PICU database hinders epidemiologic and quasi-experimental research of PICU care. Combining the virtual pediatric system (VPS) and pediatric health information system (PHIS) databases would overcome this data gap. However, limits on the use of variables containing protected health information (PHI) have prevented prior large-scale VPS-PHIS linkage. We sought to link VPS and PHIS records from 31 hospitals without using PHI. METHODS: We performed a database linkage study utilizing a sequential direct + probabilistic linkage approach with records from 31 hospitals contributing to VPS and PHIS for patients discharged between January 1, 2017 and December 31, 2022. Using directly linked records as a reference, we assessed the accuracy of probabilistically linked pairs with data from four hospitals by describing the percent of false-negative and false-positive links. We also compared demographic and clinical variables between linked and unlinked records using Cohen's Effect size (CES). RESULTS: We obtained 337,836 VPS and 3,515,595 PHIS records without PHI. In total, we linked 319,955 of 337,836 (94.7%) VPS records. Direct linkage contributed 274,345 of 319,955 (85.7%) of all record linkages. Probabilistic linkage contributed 45,610 of 319,955 (14.3%) of all record linkages. Assessment of the probabilistic linkage found 0.21% false-negative links and no false-positive links. We found negligible imbalance by CES for gender (% female in linked vs. unlinked: 44.8% vs. 43.6%, CES 0.024), age in years (median [interquartile range (IQR)]: 5.1 [1.21-13.22] vs. 5.53 [1.53-13.38], CES 0.054), Pediatric Risk of Mortality 3 score (median [IQR]: 0.44 [0.3-0.9] vs. 0.37 [0.29-1.02], CES 0.096), or days in PICU (median [IQR]: 1.55 [0.8-3.47] vs. 1.33 [0.79-3.45], CES 0.031). CONCLUSIONS: Using a combined direct and probabilistic linkage approach, we created a highly accurate linkage VPS and PHIS database records from 31 hospitals without using PHI. This combined database can be used to create novel datasets for PICU research.
31 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cancer patients' experiences of "time toxicity": a qualitative meta-synthesis
OBJECTIVE: To systematically review and synthesize qualitative studies on cancer patients' experiences of time toxicity, providing evidence to inform policy making. METHODS: We searched PubMed, Web of Science, Cochrane Library, Embase, CINAHL, CNKI, Wanfang, VIP, SinoMed, and other databases for qualitative studies on cancer patients' experiences of time toxicity, from database inception to November 2025. Included studies were appraised using the 2016 edition of the JBI quality appraisal criteria for qualitative research from the Joanna Briggs Institute (Australia). Results were synthesized using an aggregation approach for meta-synthesis. RESULTS: Ten studies were included. Forty-one themes were extracted, which were grouped into 11 categories and further consolidated into 4 synthesized findings: (1) multidimensional sources of time toxicity; (2) negative impacts of time toxicity; (3) coping strategies adopted to address time toxicity; and (4) positive experiences from investing time in treatment. CONCLUSIONS: Time toxicity has multidimensional effects on cancer patients' daily rhythms, psychological states, and social roles, and requires coordinated attention from healthcare professionals, society, and policy makers. Future efforts should build systemic support through integrating healthcare resources, optimizing service processes, and strengthening social support to reduce patients' time toxicity.
30 May 2026
Read appraisal →JAMA
The Veterans Affairs' Whole Health Approach for Chronic Pain Management: The wHOPE Randomized Clinical Trial
IMPORTANCE: The US Department of Veterans Affairs (VA) Whole Health approach was congressionally mandated in 2016 for patients with chronic pain receiving care in VA hospitals, but no randomized clinical trials have tested its benefits. OBJECTIVE: To evaluate the effectiveness of a whole health team intervention in VA patients with chronic pain compared with cognitive behavioral therapy and with usual care, and to evaluate the effectiveness of cognitive behavioral therapy compared with usual care in reducing long-term pain interference. DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial involving 6 VA health systems in the US enrolled participants between September 18, 2020, and January 19, 2024. Final follow-up occurred on January 27, 2025. Analyses took place between April 1, 2025, and February 3, 2026. Participants were patients with chronic pain receiving VA primary care. INTERVENTIONS: Patients with chronic pain were randomized (11:11:2) to receive a whole health team intervention (n = 343), cognitive behavioral therapy for chronic pain delivered in group sessions (n = 339), or usual care (n = 82) for 12 months. The whole health team included a primary physician or nurse practitioner, a second clinician providing nonpharmacological or integrative pain care, and a coach. The team provided interdisciplinary, individualized care consistent with the VA Whole Health model to attain personal health goals aligned with patients' personal values and life goals. MAIN OUTCOMES AND MEASURES: The primary outcome was the Brief Pain Inventory interference (BPI-I) subscale score (range, 0-10 points; higher scores indicate worse interference from pain; minimal clinically important difference, 1.0) at 12 months. RESULTS: Of 764 randomized patients (mean [SD] age, 60.5 [12.3] years; 66.5% were men), 632 (82.7%) completed 12-month follow-up. At 12 months, the whole health group had significantly improved pain interference scores (from 6.6 to 4.9) compared with the cognitive behavioral therapy (from 6.4 to 5.5) (mean difference, -0.58 [97% CI, -1.11 to -0.05]; P = .02) and usual care (from 6.4 to 5.7) (mean difference, -0.77 [99% CI, -1.40 to -0.15]; P = .002) groups. At 12 months, cognitive behavioral therapy did not improve pain interference scores significantly more than usual care (mean difference, -0.19 [99% CI, -0.89 to 0.50]; P = .46). The most common adverse event was suicidal ideation, which occurred in 15.9% of patients in the cognitive behavioral therapy group, 13.7% in the whole health team group, and 13.4% in the usual care group. CONCLUSIONS AND RELEVANCE: These results support use of the whole health team approach to attain a statistically significant but small improvement in pain interference in VA patients with chronic pain. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04330365.
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 →JMIR research protocols
Reducing Mis-triage in Emergency Departments (RemEDy): Protocol for Improving Triage Accuracy Through Real-time Evaluation and Artificial Intelligence
BACKGROUND: Mis-triage represents a global concern, with reported rates ranging from 15% to 33%. Understanding its causes and contributing factors is essential for ensuring patient safety. Currently, available studies have mainly focused on evaluating triage systems rather than investigating the human factors affecting triage performance. A major limitation in triage evaluation studies is the lack of standardized criteria to assess patient acuity and the absence of a clear consensus on how to measure triage accuracy. Most studies rely on retrospective data, which often fail to capture real-life clinical complexity. Therefore, the underlying causes and consequences of mis-triage remain partially understood. OBJECTIVE: This study aims to improve triage by defining the optimal triage evaluation process and identifying clinician-, patient-, and system-level factors that compromise its accuracy and safety. METHODS: Reducing Mis-Triage in Emergency Departments (RemEDy) will be a 4-phase, mixed methods project conducted across 7 Swiss emergency departments. The first phase will focus on developing a standardized triage evaluation instrument, combining evidence from a scoping review of triage evaluation processes, workshops with triage clinicians using design thinking methodology, and a modified Research and Development-University of California Delphi involving international experts and patient representatives. The second phase will prospectively implement this instrument in real time within a multicenter observational cohort study to evaluate triage performance; quantify mis-triage; and identify predictors at the patient level (eg, demographics), clinician level (eg, training), and system level (eg, crowding and length of stay). The third phase will focus on designing and validating an artificial intelligence-based decision support tool, applying multimodal models that integrate real-time triage data to enhance acuity prediction and minimize human error. The fourth phase will develop and evaluate a targeted training program, guided by the Capability, Opportunity, Motivation, and Behavior model, to strengthen triage accuracy and mitigate cognitive biases. RESULTS: The project was funded by the Swiss National Science Foundation in March 2025 (grant 10004535). At submission, the scoping review is ongoing and expected to be completed in early 2026. Development and piloting of the triage evaluation instrument will take place in 2026. A multicenter cohort study is planned between October 2026 and June 2027. The intervention study is scheduled between October 2027 and December 2028. Final results are expected in 2029. CONCLUSIONS: The RemEDy project addresses key limitations of current triage research, including the lack of standardized evaluation methods. By combining expert and clinician consensus; real-time assessment; and multilevel analysis of patient-, clinician-, and emergency department-level factors, RemEDy is expected to provide a more comprehensive understanding of mis-triage and its causes. RemEDy will establish a novel framework for real-time triage evaluation and inform the development of targeted training programs with the potential to improve triage accuracy, safety, and equity. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/92264.
28 May 2026
Read appraisal →BMJ open
Assessment of patient preferences in digital health trials: procedure development and implementation in a preference-based trial
OBJECTIVES: To develop and evaluate a standardised, fully automated assessment procedure for patient treatment preferences suitable for use in randomised controlled trials (RCTs) with remote or digital health interventions. DESIGN: Multiphase developmental study followed by implementation in a fully randomised preference trial (FRPT). SETTING: University and outpatient cancer care settings in Switzerland; implementation in RCTs with a remote digital health intervention. PARTICIPANTS: 50 medical students for pilot testing; 11 patients with cancer for qualitative testing; 124 adult patients with cancer experiencing fatigue or sleep problems in an FRPT with a remote digital health intervention. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome: feasibility and applicability of a standardised digital preference assessment procedure. SECONDARY OUTCOMES: distribution and strength of treatment preferences, reasons for preference. RESULTS: A procedure with three items for assessing treatment preferences was developed and refined based on pilot testing, stakeholder meetings and patient interviews. The stepwise procedure ensured that study participants with an initially unclear preference were finally able to report their preference. The majority of students (91.7%) indicated their preference already in the first assessment step. Similar in the RCT phase, 110 of 124 participants (88.7%) indicated their preference in the first step. Preference drivers included interest, perceived ease and conceptual appeal in addition to expected treatment benefits. CONCLUSIONS: Our fully automated standardised preference assessment with three items appears feasible in trials with digital health interventions. Future trials with two active treatment conditions should consider implementation of such a preference assessment to evaluate non-specific treatment effects driven by the match of patients' preferences with the respective treatment. TRIAL REGISTRATION NUMBERS: DRKS00035596; DRKS00035541; SNCTP000006209.
28 May 2026
Read appraisal →PloS one
Determinants of maternal postnatal care utilization in Bangladesh: A machine learning and SHAP-based analysis of BDHS 2022 data
Postnatal care (PNC) plays a crucial role in minimizing maternal and neonatal morbidity and mortality, but the uptake of services in Bangladesh remains below the recommended level. Although logistic regression has been widely used, it may miss complex nonlinear interactions among social, economic, and healthcare factors. This study contributes to the body of knowledge by using machine learning (ML) to identify the most significant determinants of PNC and to enhance prediction accuracy. We compared logistic regression to several ML models, including Random Forest, XGBoost, CatBoost, Support Vector Machine, AdaBoost, and Gradient Boosting, using nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS) with ADASYN oversampling to correct class imbalance. Among all models, Random Forest achieved the highest AUC (0.9050), closely followed by XGBoost (0.9036) and CatBoost (0.9028), all of which substantially outperformed logistic regression (AUC = 0.8470). SHAP analysis of the Random Forest model indicated that delivery place, husband's occupation, rural residence, wealth index, and media exposure were the most influential predictors of PNC utilization, alongside maternal education, women's occupation, and age-related factors. The results indicate that ML is more effective than classical procedures for revealing latent patterns and making accurate predictions. Policy implications include encouraging facility-based deliveries, improving maternal education, reducing wealth disparities, and enhancing media coverage of health, particularly among rural and low-income groups. This paper not only identifies key drivers of PNC in Bangladesh but also demonstrates how ML can supplement traditional methods to reinforce maternal health policy and interventions.
28 May 2026
Read appraisal →Annals of family medicine
Association of Advanced Access With Primary Care Performance: A Systematic Review
PURPOSE: Timely access to primary care remains a major challenge. Advanced access, an organizational model for appointment management, aims to improve accessibility by allowing patients to consult their health care professional in a timely manner. This systematic review evaluated its association with several key dimensions of primary care performance. METHODS: We conducted a comprehensive search in MEDLINE, Embase, Web of Science, CINAHL, and the Cochrane Library from inception to May 10, 2025. Randomized intervention studies, nonrandomized intervention studies, and observational studies comparing advanced access with traditional scheduling systems among patients in primary care were eligible. Outcomes were the wait time to an appointment, continuity of care, emergency department (ED) use, and patient satisfaction. Two independent reviewers selected studies, extracted data, and assessed risk of bias. We performed a narrative synthesis, separately analyzing universal and non-universal health care systems. RESULTS: Among 7,595 unique records identified, 29 studies were included. Of these, 23 evaluated appointment wait time, 13 examined continuity of care, 3 assessed ED use, and 13 evaluated patient satisfaction. Appointment wait time decreased in all 23 studies assessing this outcome, with 13 reporting statistically significant reductions. Continuity of care improved in 11 of 13 studies, of which 7 reported statistically significant improvements. All 3 studies evaluating ED use reported reductions in ED visits, although none demonstrated statistical significance. Patient satisfaction improved in 8 studies, with 3 reporting statistically significant improvements. Overall, findings were consistent across universal and non-universal health care systems. CONCLUSIONS: Advanced access appears to improve appointment timeliness, continuity of care, and patient satisfaction, and may help reduce avoidable ED use across diverse health care systems.
28 May 2026
Read appraisal →Cancer causes & control : CCC
Built environment (BE) and cancer: a systematic review of the BE's impact during the treatment journey and patient outcomes
The role of the built environment (BE) in cancer research has been extensively explored in prevention, focusing on its influence as a determinant of exposure to carcinogens and a lifestyle shaper, particularly through geographic accessibility to resources like transportation, healthy food, and diagnostic centers. However, the BE's role and influence during the treatment period and its connection with patient outcomes remain comparatively understudied. This systematic review employed the CDC definition of BE, following PRISMA guidelines and AI-driven software (ASReview) for a thorough and efficient literature review. A total of 31 articles were identified (27 database, 4 snowballing) that examined the BE's role during the treatment phase. These studies investigated diverse BE characteristics including urban features (e.g., transportation, green environment, food environment) and architectural features (e.g., design, aesthetics) during the cancer patient treatment journey. Among the included studies, the most analyzed outcomes were Physical Activity (PA) (11 articles), followed by survival and mortality (9 articles). Findings revealed positive associations between specific BE characteristics-such as green spaces, accessibility, transportation options, and aesthetics-and cancer patients' physical activity levels, quality of life, and mental health status; however, findings are inconsistent. These results underscore the potential of integrating urban and architectural BE analysis to enhance the psychosocial dimensions of cancer treatment, improve healthcare delivery, and open new pathways for research strategies.
28 May 2026
Read appraisal →JMIR research protocols
Coconstructing CHAMP, an Artificial Intelligence Chatbot for Pediatric Infectious Symptoms Management: Protocol for a Multiphase Participatory Study
BACKGROUND: Acute infectious symptoms are a leading cause of pediatric emergency department visits in Canada, many of which are low acuity and could be safely managed at home. Artificial intelligence (AI) chatbots offer a promising avenue for delivering accessible, evidence-based guidance to support families in managing these symptoms. OBJECTIVE: This study aims to adapt and coconstruct CHAMP (CHatbot to Assist the Management of Pediatric patients), an AI chatbot to support patients and families with acute pediatric infectious symptoms. CHAMP aims to deliver timely, tailored, and validated health information to support safe at-home self-management and informed care-seeking. METHODS: This multiphase, mixed methods participatory study will be conducted at the Montreal Children's Hospital in Montreal, Quebec, Canada. A coconstruction committee comprised of youth, parents, caregivers, and partners will be engaged as coresearchers. Eligible participants will include (1) youth aged 14-17 years and (2) parents and caregivers of children aged 0-17 years. The study comprises 5 phases. Phase 1 involves a qualitative needs assessment using focus groups with 20 participants to explore their informational needs, preferences, and concerns regarding pediatric infections and the use of AI chatbots. Phase 2 focuses on coconstructing and validating CHAMP's knowledge database through 3-5 workshops. Coresearchers will review pediatric clinical guidelines, map care questions and decision-making processes, and shape CHAMP's conversational framework. Phase 3 consists of iterative prototyping and testing through 3-5 workshops. Coresearchers will engage in prototyping and scenario testing, alongside preliminary usability and acceptability assessments. Phase 4 examines equity and accessibility through focus groups with 20 participants at risk of digital exclusion, as well as multilingual evaluation of an automated large language model-based translation layer. Phase 5 uses collaborative ethnography to explore the process of participatory coconstruction and its impact on CHAMP's design. RESULTS: Funding was secured in 2024, and Research Ethics Board approval was obtained in December 2024. As of December 2025, the coconstruction committee is being assembled, and Phase 1 recruitment is underway. CONCLUSIONS: This study will produce a functioning CHAMP prototype grounded in participatory, equitable, and responsible pediatric AI development. Findings will inform usability testing and an implementation-effectiveness evaluation, contributing to best practices for pediatric-centered AI health tools. By providing timely, tailored, and validated health information on acute infections, CHAMP may support safe at-home self-management, reduce preventable emergency department visits, ensure at-risk children are directed to appropriate care, and improve patient and family health care experiences.
28 May 2026
Read appraisal →Journal of medical Internet research
Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study
BACKGROUND: As oncology workflows integrate increasingly autonomous artificial intelligence (AI) agents, health systems face uncertainty regarding operational impacts. Traditional linear forecasting methods fail to capture second-order effects such as governance saturation, induced demand, and bottleneck migration. To navigate this complexity, the emerging field of medical futures studies requires methodologies that bridge qualitative strategic foresight with quantitative operational modeling. These system-level dynamics directly influence timely diagnosis, treatment delays, and overall health system resilience. OBJECTIVE: This study aimed to develop a proof-of-concept framework coupling qualitative scenario planning with computational discrete-event simulation to stress-test oncology AI adoption strategies. METHODS: We defined a strategic state space using 2 orthogonal axes, AI automation intensity and data interoperability, resulting in 4 distinct futures scenarios. We translated these qualitative narratives into a quantitative discrete-event simulation model of a 3-year operational horizon. The model quantified system performance (referral-to-treatment interval [RTTI] and throughput), volatility, and resource constraints across different adoption trajectories. RESULTS: The scenario-planning phase yielded 4 operational archetypes (analog oncology, automation islands, interconnected clinicians, and AI-orchestrated care) with distinct constraints, risks, and failure modes. In the simulation, the fully integrated scenario maximized capacity (1244, SD 21.4 patients per year) and halved the mean RTTI to 14.9 (SD 0.3) days, a magnitude comparable to major pathway redesign interventions. Isolated automation without data infrastructure led to reduced system performance, increasing RTTI by 26% (37.1, SD 1.3 days) and reducing throughput to 647 (SD 10.1) patients per year due to administrative governance saturation. The model illustrated a structural bottleneck migration: successful upstream AI adoption shifted binding constraints from diagnostic scanners to downstream chemotherapy infusion units, whereas missing data interoperability resulted in governance constraints. Pathway optimization analysis indicated that a coordinated strategy prioritizing early improvements in data interoperability reduced transition volatility compared to an automation-first approach. CONCLUSIONS: Integrating qualitative scenario planning with quantitative simulations enabled a systematic evaluation of oncology AI adoption strategies. As a proof of concept, it offers a replicable framework for health leaders to model future scenarios of digital transformation in times of high uncertainty. Subsequent work should expand this methodology to incorporate financial and health equity dimensions, establishing simulation-based scenario planning as an important tool in medical futures studies.
26 May 2026
Read appraisal →Circulation journal : official journal of the Japanese Circulation Society
Regional Disparities in Access to Specialized Facilities and Prescription Preference for Initial Pulmonary Arterial Hypertension Treatment - A Nationwide Study Using the National Database of Health Insurance Claims
BACKGROUND: Improved prognosis of pulmonary arterial hypertension (PAH) has been reported from specialized facilities, though nationwide patient access to both specialized centers and initial treatment regimen in Japan remains unclear. METHODS AND RESULTS: The study group comprised patients with PAH defined as having disease-specific ICD-10 code, administration of medications and diagnostic testing between 2012 and 2023 in the National Database of Health Insurance Claims. Among 13,228 patients across the country, 30.7% accessed Japan Pulmonary Hypertension Registry facilities and 23.7% were initiated with disease-specific combination therapy, though numbers varied across regions. CONCLUSIONS: Regional disparities in access to disease-specific specialized treatment for PAH need to be addressed.
25 May 2026
Read appraisal →Scandinavian journal of primary health care
Responsible digital transformation in rural primary healthcare services: critical factors from a Norwegian case study
OBJECTIVE: To explore the critical factors that shape the potential for co-creating and implementing a virtual department for home-based healthcare in a rural municipality. METHODS: A qualitative case study was conducted in a rural municipality in Northern Norway. Data were collected through semi-structured interviews, focus groups, and co-creation workshops involving healthcare leaders, frontline staff, service users and relatives, and representative from the pensioners' federation. Thematic analysis was applied to identify organisational, contextual, and cultural factors influencing digital transformation. RESULTS: While the co-creation of virtual department for home-based healthcare showed potential for piloting, readiness for full implementation was limited. Six critical factors were identified: structural readiness, digital competence, technology in practice, cultural change, autonomy and care, and motivation and dedication. These factors highlight the importance of organisational culture, workforce capacity, and stakeholder engagement in shaping readiness for responsible digital transformation. CONCLUSION: Digital transformation holds potential for addressing healthcare challenges in rural municipalities, but success depends on more than technological efficacy. Effective coordination between bottom-up and top-down approaches is required to ensure responsible and sustainable transformation of primary healthcare services.
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 →PloS one
Views, barriers, and facilitators of people living with human immunodeficiency virus and healthcare professionals regarding the use of a mobile health application to improve HIV self-care in Malaysia
AIM: The views, barriers, and facilitators of people living with HIV (PLWH) and healthcare professionals (HCPs), regarding the use of a mobile health application (m-HA) to improve HIV self-care were explored. SUBJECT AND METHODS: Seventeen PLWH, nine doctors and two nurses who owned a smartphone and understood English or Malay were recruited from the infectious disease clinic of a tertiary hospital in Malaysia. Semi-structured in-depth interviews were conducted face-to-face using a topic guide, audio-recorded and transcribed verbatim. Data was analysed using reflexive thematic analysis. Two researchers independently coded transcripts and met to resolve discrepancies and develop themes. Data collection continued until thematic saturation was achieved. RESULTS: Three themes emerged: (1) Perceived benefits and concerns regarding m-HA usage: participants perceived m-HA could improve knowledge, but expressed concerns regarding information overload. Participants viewed m-HA as a means to enhance access to hospital services like clinic visits, medication refills, communication with HCPs, and medical records documentation. However, concerns regarding misuse, data confidentiality and security remained. (2) Perceived motivators of m-HA: participants perceived that high utilitarian and hedonic motivators increase m-HA usage. (3) Perceived influences to use m-HA: participants believed PLWH would use m-HA if introduced by their peers or HCPs. CONCLUSION: These findings provided information for developing a patient-centred m-HA to improve HIV self-care in Malaysia.
24 May 2026
Read appraisal →Scientific reports
Impact of nurse-led postoperative education on health outcomes following endovascular aortic repair: a randomized trial
The purpose of this randomized controlled clinical trial was to investigate the health effects of an education-based intervention performed at a nurse-led clinic, for patients who underwent endovascular treatment for abdominal aortic aneurysm. Fifty-four adult patients were randomized to the intervention group (n = 26) or the control group (n = 28). The control group received standard care, and the intervention group received standard care alongside two consultations at a specialist surgical nurse-led clinic. The consultations focused on individual motivational support and education about abdominal aortic aneurysm disease. Data was collected prior to surgery (baseline) and at one, six, and 12 months postoperatively for each group. Outcomes of interest included participants' responses to the Health Education Impact Questionnaire, responses to the EuroQol Five Dimensions Questionnaire, and responses to four complementary items specific to the scope of this study. Both groups reported overall high ratings of perceived health throughout the study period. While there were no statistically significant differences between the two groups, the control group showed a substantial improvement in perceived understanding of their diagnosis over time. This randomized controlled trial did not find any significant health benefits as compared to standard treatment.Trial registration ClinicalTrials.gov (19/05/2025 NCT06986252). Retrospectively registered.
24 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 →BMC research notes
Survey to evaluate clinical benefits and acceptance in international tele-intensive care unit collaboration
OBJECTIVES: Tele-intensive care unit (TICU) networks have evolved quickly in recent years and have proved their value in providing care for the critically ill. Objective measures to assess performance and quantify success e.g. clinical and economic outcome parameters are desirable but not easily accessible in international collaboration with very diverse clinical settings and legal prerequisites. Therefore, we initiated a study to evaluate the performance in our tele-intensive care unit network. METHODS: We conducted an online survey addressing clinical staff at ten spoke sites in four countries connected to one TICU hub to assess subjective clinical benefit, acceptance and potential for improvement. RESULTS: The majority of the 30 participants felt well supported clinically and personally, with a high acceptance and trust as well as a positive working relationship in tele-medical collaboration. Ethical principles and data protection were discovered to be persistently of utmost significance in tele-intensive care unit networks. CONCLUSION: International telemedicine in critical care is perceived positively without major differences between diverse health care systems when crossing borders, showing potential for improvement to guide future clinical and research approaches. Clinical trial registiry: The study was registered on August 11th, 2023 with the German Clinical Trials Register (DRKS-IDDRKS00032246; https://www.drks.de/search/de/trial/DRKS00032246/details ).
24 May 2026
Read appraisal →Studies in health technology and informatics
Evaluating the Potential of Machine Learning for Discharge Management on Routine Health Insurance Data
Machine learning (ML) has great potential in healthcare, especially with large structured data. Routine health insurance claims (HIC) data are a valuable resource, comprising standardized longitudinal patient information. However, to fully leverage ML in HIC, challenges such as large data volumes, variability, data gaps, sparsity, and high dimensionality must be addressed. To overcome these barriers, our publicly funded KI-THRUST project accessed a large-scale dataset of 2 million anonymized routine insurance records from four German providers. Subsequently, a thorough performance evaluation of various linear and nonlinear ML models (logistic regression, AdaBoost, Random Forest, Neural Network) for their potential to predict clinical outcomes such as mortality and unplanned readmissions was conducted, and the results were interpreted using explainable AI methods (Integrated Gradients, Shapley values, LIME). In particular, we investigated the predictive value of feature sets comprising base demographics (M1) and ICD-10 codes for previous conditions and procedures (M2, M3). While AdaBoost achieved the best overall performance, the less complex logistic regression performed comparably. The additional diagnostic and procedural information in M2 led to a significant performance gain, whereas additional time-related information did not. Overall, our results show the potential of large-scale HIC data when combined with ML methods and XAI. Ultimately, paving the way for future applications like discharge management systems and other healthcare areas.
24 May 2026
Read appraisal →Studies in health technology and informatics
Accessibility of Digital Health and Welfare Services Among Immigrants in Finland
Digital health and welfare services support the delivery of the services. However, the health benefits are mixed at the population level, and further research is needed to better understand the driving factors of healthcare inequalities, particularly among vulnerable subgroups, e.g., immigrants. This qualitative study aimed to understand equality in service accessibility among immigrants, especially from the informational, experiential, and institutional perspectives. Five focus group interviews were conducted for immigrants. Data was analysed with deductive content analysis. Barriers for informational accessibility included linguistic challenges and inadequate digital literacy (DL); for experiential accessibility, usability challenges and disrespectful phone services; for institutional accessibility, challenges with electronic authentication identification for personalized digital services. Facilitators for the use of digital health and welfare services included linguistic improvements of services and support for DL. Further research, including interventions with participatory design and linguistic and digital support, is needed to improve accessibility of digital services for immigrants.
24 May 2026
Read appraisal →Studies in health technology and informatics
Logic Models on Health Information Technology: First Insights from a Cross-Disciplinary Scoping Review
BACKGROUND: Health information technology (HIT) interventions are complex, yet theory-based approaches such as logic models or Theory of Change (ToC) remain rarely applied in medical informatics, although they could clarify assumptions, link activities to outcomes, and support design and evaluation. OBJECTIVES: This scoping review examined how logic models and ToC have been used in HIT research across disciplines. METHODS: Following the PRISMA-ScR guidelines, databases were searched up to August 2025. Eligible studies explicitly applied logic models or ToC to HIT. Data were extracted on HIT type, purpose of using logic models, and reported benefits and challenges. RESULTS: Sixty-nine studies met inclusion criteria, most published after 2020. A majority of studies developed logic models for patient-facing HIT such as mHealth or telehealth. Logic models were mainly used to support HIT design or evaluation and were mostly newly developed. Reported benefits of using logic models included conceptual clarity and better stakeholder communication; challenges involved resource demands and limited generalisability. CONCLUSIONS: Logic models and ToC are increasingly used for HIT but remain inconsistently applied and seldom reused. Standardised reporting and repositories could strengthen theory-informed evaluation in medical informatics.
24 May 2026
Read appraisal →Journal of medical Internet research
Reporting of Telehealth Implementation in Cystic Fibrosis: Scoping Review Using a Novel Theory-Based Evaluation Lens
BACKGROUND: Many inductive reviews exploring telehealth and its application in health care have identified missing or inconsistently reported implementation data, calling for a standardized approach to telehealth research. OBJECTIVE: Using cystic fibrosis (CF) as a case exemplar, this study evaluated the adherence of telehealth research to standardized reporting frameworks through a theory-based evaluation lens to assess implementation reporting quality and identify knowledge gaps and strengths across the literature. METHODS: We conducted an updated systematic review of the PubMed, Scopus, and Web of Science databases using a novel deductive approach to identify relevant scientific papers available in English and focusing on the delivery of telehealth interventions to CF populations as part of or alongside routine CF care. Two relevant reporting checklists were identified in the Equator Network database (Guidelines and Checklist for the Reporting on Digital Health Implementations [iCHECK-DH] and Template for Intervention Description and Replication for Telehealth [TIDiER-telehealth]) to extract data from the papers. Each checklist category was described as being "fully reported" (score=2), "partially reported" (score=1), and "did not report" (score=0) for each paper. An overall score was calculated for adherence to the checklists. RESULTS: In total, 98 studies published between 2006 and May 2025 were included in this review, with the majority appearing during the COVID-19 pandemic (2021-2022). Most studies were conducted in a single country, predominantly the United States, Australia, and the United Kingdom, and were published in medical journals. Telehealth was variably described, with video call-based models in combination with remote monitoring being most common. The median score was 22/40 (range 11-29, 55.0% adherent) for iCHECK and 15/24 (range 6-23, 62.5% adherent) for TIDiER, demonstrating moderate overall reporting quality. For iCHECK, ≥50% of studies fully reported 6/20 categories, partially reported 9/20 categories, and did not report 3/20 categories. For TIDiER, ≥50% of studies fully reported 4/12 categories, partially reported 6/12 categories, and did not report 1/12 categories, indicating persistent gaps in intervention description despite improved partial reporting. CONCLUSIONS: Key areas, such as justification for telehealth, target populations, and outcomes, are well documented, offering valuable insights into the rationale for and outcomes associated with telehealth. However, implementation processes remain underreported, partly due to the more recent adoption of frameworks like iCHECK and TIDiER. The clinical implications of the current evidence limit the implementation of telehealth in terms of the ability to assess feasibility and readiness for adoption; understand financial implications and plan sustainably; ensure patient safety, data protection, and equity; interpret outcome data in context; and share, replicate, or scale evidence-based models of care. Strengthening the commitment to standardized telehealth reporting will ultimately support clinical decision-making and improve the effective and equitable integration of telehealth into care.
23 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 →Systematic reviews
Impact of the COVID-19 pandemic on diagnosis, and healthcare utilization, among patients with cancer (lung, breast, and pancreas) and cardiovascular diseases (HF, AF, hypertensive, and chronic ischemic heart disease) in Germany: two systematic reviews.
BACKGROUND: The COVID-19 pandemic and related non-pharmaceutical interventions (NPIs) (e.g., lockdowns and contact restrictions) disrupted routine healthcare delivery. In Germany, these measures affected diagnostic and treatment services for people with cancer and cardiovascular diseases, potentially delaying diagnosis and adversely influencing outcomes. We assessed whether and to what extent diagnosis, health utilization and health outcome among patients with selected cancer and cardiovascular conditions changed in Germany during the pandemic. METHODS: We conducted two systematic reviews of studies from Germany on selected cancers (breast, lung and pancreatic) and cardiovascular conditions (atrial fibrillation/flutter, heart failure, hypertensive and chronic ischemic heart disease). Protocols were registered in PROSPERO and the reviews were reported in accordance with PRISMA. We searched PubMed, Web of Science, Cochrane Library, Scopus, and Embase and screened grey literature. Outcomes included changes in new diagnoses, healthcare utilization, treatment, and disease-specific mortality during the pandemic (2020-2023) compared with the pre-pandemic period (2018-2019). Two reviewers independently screened records, extracted data, and assessed risk of bias using an adapted ROBINS-E tool. Owing to heterogeneity, we synthesized findings narratively. RESULTS: We screened 1991 records for cancer and 4,981 records for cardiovascular diseases, and included 9 cancer studies and 10 cardiovascular studies. For cancer, several studies reported a relative reduction in new breast and lung cancer diagnoses of up to 25% during lockdown periods; hospital admissions decreased by up to 9%. For cardiovascular conditions, hospital admissions for atrial fibrillation/flutter and heart failure decreased by up to 20%, particularly during pandemic peaks. Evidence on treatment delays, changes in treatment, and mortality was limited, and outcomes for other included diagnoses were often not reported. DISCUSSION: The available evidence indicates substantial reductions in hospital admissions and new diagnoses among patients with cancer and cardiovascular disease in Germany during the pandemic, suggesting major disruptions to care delivery. However, heterogeneity and gaps in the evidence base limit a comprehensive assessment of downstream outcomes. More comprehensive, linked data and further research are needed to quantify the full pandemic's impact and to strengthen health-system resilience for future crises.
23 May 2026
Read appraisal →Current cardiology reports
Bridging Pediatric to Adult Care: A Scoping Review on Transitional Care for Individuals with Congenital Heart Disease Using Data Mining Techniques to Identify Key Topics
BACKGROUND: Adolescents and young adults with congenital heart disease (CHD) face significant challenges when transitioning from pediatric to adult care. Despite growing recognition of its importance, transitional care remains inconsistently implemented across healthcare systems, and a summary of published literature in this regard is still missing. This scoping review aims to systematically map the literature on CHD transitional care and identify key topics and trends. METHODS: Following Joanna Briggs Institute guidelines, we conducted a comprehensive search across seven electronic databases using the Population, Concept, and Context framework. A total of 73 studies were included. Data were extracted and analyzed using Latent Dirichlet Allocation to identify core topics, and Multiple Correspondence Analysis was applied to explore thematic relationships and validate topic structure. Lexicometric analysis assessed the linguistic complexity and specificity of the literature. RESULTS: Three major themes emerged: (1) Education, Self-Management, and Structured Support, (2) Timing, Knowledge Transfer, and Developmental Needs, and (3) Transition Program Implementation and Coordination. These themes reflect an increasing focus on structured educational strategies, developmentally tailored care, and system-level program delivery. Education-focused interventions were more frequently found in recent, high-quality experimental studies. In contrast, studies addressing timing and developmental needs and those focused on implementation were more common in earlier-phase or heterogeneous research contexts. CONCLUSION: Transitional care for individuals with CHD requires more standardized, evidence-based approaches. Improved documentation when reporting transitional care is essential to enhance fidelity, scalability, and long-term impact. This review provides a foundation for developing outcome-focused research and supports designing individualized, high-quality transition programs.
23 May 2026
Read appraisal →JMIR human factors
Telemedicine Service Experience Questionnaire for Chinese Outpatients: Development and Validation Study
BACKGROUND: Telemedicine has rapidly expanded; however, standardized, telemedicine-specific patient-reported experience measures tailored to outpatient workflows are limited in many settings. OBJECTIVE: This study aimed to develop and psychometrically validate the Telemedicine Service Experience Questionnaire (TSEQ) for Chinese outpatients using telemedicine services. METHODS: We conducted a web-based survey among outpatients who completed a telemedicine consultation at Peking Union Medical College Hospital between July 1, 2021, to August 31, 2021, and who had used telemedicine services, using an adapted Chinese Patient Experience Questionnaire that encompasses 15 questions across 4 dimensions, to investigate patients' telemedicine consultation experiences. Item generation was informed by a literature review, workflow mapping, and expert review. We evaluated the factor structure using exploratory factor analysis and confirmatory factor analysis on the full sample with cross-validation. Reliability was assessed using Cronbach α and item-total correlations. RESULTS: In total, 3338 participants completed the survey (mean age 45.3, SD 17.8 y; n=2182, 65.4% female participants; n=1827, 54.8% with college education or above). The exploratory factor analysis of the final 14-item scale resulted in 4 factors. After scrutinizing the content, these factors were labeled "Service Efficiency," "Post-treatment," "Information Guidance," and "Humanistic Care," and they demonstrated good internal consistency (Cronbach α values of 0.876, 0.840, 0.962, and 0.876, respectively). Moreover, as the average variance extracted values were greater than 0.5 and the composite reliability values were greater than 0.7, the TSEQ scale has high convergent validity. Our findings suggest that the psychometric properties of the 14-item TSEQ are valid and reliable for assessing telemedicine service experience among Chinese outpatients. CONCLUSIONS: The TSEQ demonstrates a stable multidomain structure with satisfactory reliability and validity for evaluating outpatient telemedicine service experience in China. The instrument can support routine quality monitoring and guide targeted workflow improvements. Future studies should validate the TSEQ in multisite and postpandemic samples and examine measurement invariance across key subgroups.
22 May 2026
Read appraisal →The British journal of ophthalmology
Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review
This scoping review examines the existing literature on the application of artificial intelligence (AI) in screening for eye diseases, with a focus on evaluating whether AI-assisted diagnostic technologies enhance the availability, accessibility, acceptability and quality of screening services. 42 original studies were selected for in-depth analysis, including those employing health economic evaluations. Methodological quality was assessed using the Mixed Methods Appraisal Tool, with the majority of studies demonstrating high quality-24 scored 5/5, 15 scored 4/5 and the rest scored 3/5. Among the included studies, 34 compared manual screening with either AI-assisted or fully AI-driven approaches. Availability was the most frequently studied aspect (28 studies), followed by acceptability (12 studies), whereas accessibility and service quality were less commonly addressed. Overall, AI shows significant potential to improve the cost-effectiveness of eye care services and enhance patient access, particularly in remote or underserved regions. It was also well-accepted by patients, with high satisfaction and improved referral compliance. The findings suggest that AI holds promise for advancing eye disease screening, although large-scale, long-term trials are needed to effectively integrate AI into the reconstruction of screening processes and the reshaping of eye health service systems.
22 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 →JMIR medical informatics
Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods
BACKGROUND: Social determinants of health (SDoH) are critical drivers of health outcomes but are often underdocumented in structured electronic health record (EHR) data. Instead, SDoH are more commonly recorded in unstructured clinical notes, and unlocking this information could have far-reaching implications for advancing population health research and informing clinical decision-making. OBJECTIVE: This study develops and systematically evaluates cost-efficient methods for extracting SDoH information from unstructured clinical text using rule-based natural language processing (NLP) and large language model (LLM)-based approaches. METHODS: We constructed a gold-standard annotated corpus comprising clinical text segments from 171 patients in the Mass General Brigham Research Patient Data Registry, covering 7 SDoH domain categories and 23 subcategories. A rule-based system (RBS) was developed and evaluated alongside 7 OpenAI GPT models (GPT-4o, 4.1, 4.1-mini, o4-mini, GPT-5, GPT-5-mini, and o3) under zero-shot and few-shot settings using multiple prompting strategies. We additionally implemented late-fusion ensemble approaches that combined outputs from rule- and LLM-based methods. Performance was assessed using precision, recall, and F1-score, alongside qualitative error analysis. RESULTS: The RBS achieved high precision for SDoH domain categories (0.96) but substantially lower recall (0.68). GPT-based models consistently outperformed the RBS in overall recall and F1-scores. The best domain-level performance was observed for GPT-5 and GPT-5-mini in few-shot settings (F1-score=0.89), while o4-mini achieved the highest subcategory-level performance (F1-score=0.88). A late-fusion ensemble integrating RBS and GPT outputs further improved domain-level performance (F1-score=0.92), with balanced precision (0.93) and recall (0.90), but did not improve subcategory-level performance. CONCLUSIONS: Recent GPT models with advanced reasoning capabilities, including the newly released mini models (eg, o4-mini and GPT-5-mini), demonstrated strong performance for SDoH extraction without task-specific fine-tuning and consistently outperformed the rule-based NLP system. Integrating rule- and LLM-based methods via late fusion further enhanced domain-level extraction performance. Our results demonstrate a cost-efficient framework for the accurate identification of SDoH from clinical text, facilitating downstream population health research and clinical informatics applications.
20 May 2026
Read appraisal →Sexually transmitted infections
Complex role of digital health literacy in awareness and use of digital sexually transmitted and blood-borne infections testing: a structural equation modelling analysis of the 2022 GetCheckedOnline survey
BACKGROUND: Although digital health literacy (DHL) is recognised as a determinant of access to digital sexually transmitted and blood-borne infection (STBBI) testing, empirical evidence about its contribution to access disparities remains limited. We applied multidimensional DHL measures to examine inequities in awareness and use of GetCheckedOnline, British Columbia's (BC) publicly funded digital STBBI testing service. METHODS: We analysed data from GetCheckedOnline's 2022 community survey of English-speaking BC residents aged ≥16 years who were sexually active in the past year. Outcomes were awareness and use of GetCheckedOnline (yes/no). DHL was measured using latent factors from the eHealth Literacy Scale: Information Navigation, Resource Appraisal and Confidence in Use. Structural equation modelling (SEM) was used to estimate associations and mediation pathways between DHL, sociodemographic characteristics and service outcomes. Model fit was assessed using standard SEM indices. RESULTS: Among 1657 respondents (mean age 33 years, SD 11.77), Information Navigation was positively associated with awareness (β=0.162, p<0.001) and use (β=0.063, p=0.020) of GetCheckedOnline. Confidence in Use was positively associated with awareness (β=0.206, p=0.014) and use (β=0.115, p=0.020). In contrast, Resource Appraisal was negatively associated with awareness (β=-0.263, p=0.006) and use (β=-0.150, p=0.010). DHL factors mediated the effects of age, income, education and digital access on both outcomes. CONCLUSIONS: DHL operates as a multidimensional and socially patterned determinant of access to digital STBBI testing services. While information navigation and confidence in use facilitate access, higher resource appraisal may reduce use, potentially reflecting concerns about service fit, privacy or trust. Findings highlight the need for digital interventions that are not only accessible but also contextually relevant, trusted and responsive to the needs of diverse users.
20 May 2026
Read appraisal →BMJ (Clinical research ed.)
Concordance between target trial emulation and randomised controlled trials: systematic review and meta-analysis
OBJECTIVES: To assess the concordance of results between paired target trial emulations and their benchmarking randomised controlled trials and to explore determinants of emulation success. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Medline, EMBASE, Scopus, PsycINFO, and Web of Science (2010 to 2025). STUDY SELECTION: Observational studies that explicitly aimed to emulate target randomised controlled trials of a health intervention were included. DATA EXTRACTION: Data were extracted on study domain, target population, intervention, comparator, and main outcomes for each emulation and corresponding randomised controlled trial. MAIN OUTCOME MEASURES: For 21 predefined emulation design features, concordance was assessed using Pearson correlation coefficients, standardised difference agreement, and ratio of ratios, with subgroup and regression analyses of study characteristics and concordance. RESULTS: Among 107 target trial emulation-randomised controlled trial pairs, the Pearson correlation coefficient was 0.59 (95% confidence interval (CI) 0.45 to 0.70), standardised difference agreement was 79% (85/107), and summary ratio of ratios was 0.96 (95% CI 0.92 to 1.01; I2=36%). In 63 pairs with closer emulation of trial design, a higher level of agreement was observed, with a Pearson correlation coefficient of 0.83 (0.73 to 0.89) and standardised difference agreement of 87% (55/63). In subgroup analysis, trial emulations tended to systematically underestimate treatment effects from randomised controlled trials for specific outcomes related to venous thromboembolism and major adverse cardiovascular events, while overestimating those for respiratory outcomes, with pooled ratio of ratios of 0.76 (0.58 to 1.00; I2=40%), 0.91 (0.86 to 0.96; I2=32%), and 1.20 (1.03 to 1.40; I2=40%), respectively. Moreover, emulations based on claims data tended to underestimate the treatment effects (ratio of ratios 0.90, 0.82 to 0.99; I2=38%). In univariate regression analyses, poorer target trial emulation-randomised controlled trial concordance was associated with emulation designs with imbalanced baseline population characteristics, treatment started in hospital, and lower outcome emulation quality. CONCLUSIONS: Current target trial emulations achieve moderate concordance when replicating their corresponding randomised controlled trials. Concordance could be improved by better emulation design, including better baseline and outcome emulation and leveraging multisource linked databases. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42024619811.
20 May 2026
Read appraisal →Lifetime data analysis
Kolmogorov-Smirnov and Cramér-von Mises tests for the k-sample problem with left-truncated and right-censored data
k-Sample versions of the Kolmogorov-Smirnov and Cramér-von Mises tests are proposed for data subject to left truncation and right censoring. Their asymptotic behaviour is studied and a bootstrap resampling plan is proposed to approximate the null distribution of the new tests. The performance of the tests with finite sample sizes is investigated in a simulation study, where the classical log-rank test is also considered. An illustration with a real dataset regarding unemployment times is discussed.
18 May 2026
Read appraisal →Journal of global health
Unveiling the gap in heart failure: a Brazilian Unified Health System study.
BACKGROUND: Heart failure (HF) decompensation is the leading cause of hospitalisations in developed countries and the third most common cause in Brazil. Underdiagnosis or misdiagnosis thereof remains a critical challenge and with significant implications. At the patient level, it delays appropriate treatment and disease management. At the health system level, it leads to inefficient population health strategies, distorted epidemiological estimates, and suboptimal resource allocation. Addressing this hidden HF population is therefore essential for improving disease surveillance, optimising care pathways, and supporting more effective and equitable health system planning in Brazil. In this sense, we aimed to quantify underreporting of HF in individuals in Brazil and to estimate their mortality trends. METHODS: We identified potential HF patients using a guideline-based flowchart that combines ICD-10 codes (e.g. I21, I25, I42) and HF-related procedures (e.g. echocardiography, B-type Natriuretic Peptide (BNP) testing). Then, we developed a machine learning model for early prediction of HF using the Brazilian Department of Informatics of the Unified Health System (Departamento de Informática do Sistema Único de Saúde) mortality information system and ambulatory information system records, 2018-2022. A CatBoost algorithm was trained on a balanced cohort of patients (10 000 with HF and 9 995 without HF), restricting predictors to ≥12 months before the first I50 code to prevent leakage. Key predictors included age, chronic kidney disease, echocardiography, and lipoprotein disorders. We validated model performance in independent cohorts, including a low-prevalence cohort (~2% HF). Finally, we derived underreporting estimates via deterministic sensitivity analysis across scenarios from 0-100%. RESULTS: The proxy identified approximately 54 000 potential HF cases/y that were unreported in ambulatory data, with deterministic analysis suggesting 12-41% underestimation. Mortality analysis showed that around 200 000 deaths could be linked to unreported HF. The CatBoost model achieved an area under the curve (AUC) of 0.91 (balanced cohort; accuracy = 0.82, recall = 0.81, F1 = 0.82) and maintained strong discrimination in low-prevalence settings (AUC = 0.84, sensitivity = 0.82), with excellent calibration (Brier = 0.124-0.136; ECE = 0.01-0.03). CONCLUSIONS: We found that HF underreporting in Brazil is substantial and carries significant mortality implications. Our ML model demonstrates high accuracy in early risk stratification using routine administrative data, aligning with clinical pathways. Implementing it as a screening tool could optimise resource allocation, but ethical considerations around false positives warrant careful deployment. Future work should focus on clinical validation and cost-effectiveness analysis within the Brazilian Unified Health System. Furthermore, our findings highlight significant implications for public health and clinical HF management, emphasising the necessity of strategies that promote early detection of HF and precise case recording. Addressing underestimation is crucial to optimise healthcare resources and improve patient outcomes. The results underscore the importance of accurate diagnosis and comprehensive management approaches for better HF case tracking. Further research should explore the public health impact of these underestimations in Brazil, particularly regarding its health system's financial resources.
18 May 2026
Read appraisal →Journal of global health
Impact of travel time to health facilities on perinatal outcomes: a systematic review with narrative synthesis and meta-analysis
BACKGROUND: Evidence-based global guidance on safe travel time for small or sick newborns who require transfer to health facilities after birth is lacking. A two-hour threshold is frequently cited in low- and middle-income countries (LMICs), while 30 minutes is commonly used in high-income countries (HICs). Although these thresholds are widely referenced, their empirical basis and consistency across levels of newborn care and journey types have not been systematically examined. This study synthesises the evidence linking travel time and perinatal outcomes. METHODS: We conducted a systematic review with narrative synthesis and meta-analysis to assess the impact of travel time or distance (home-to-facility or interfacility) on stillbirth, perinatal mortality, and neonatal mortality. We searched Embase, MEDLINE, and Cochrane Central Register of Controlled Trials for published studies from 2014 to 2023. Given substantial methodological heterogeneity, we used narrative synthesis as the primary analytical approach and conducted random-effects meta-analyses where studies were sufficiently comparable (≥2 with similar definitions and outcome windows), pooling effect estimates for travel time thresholds of 30 minutes, 1 hour, or 2 hours and travel distances of 5, 10, and 15 km. We assessed bias using the Newcastle-Ottawa Scale for cohort and case-control studies. RESULTS: Of 8317 screened records, 166 were eligible for full-text review, with 37 studies meeting the inclusion criteria- All but two had low or moderate risk of bias. Most studies (n = 26) came from LMICs and documented higher perinatal survival with shorter journeys. Studies from HICs demonstrated lower out-of-hospital birth, lower morbidity, and lower mortality with shorter journeys though associations were weaker. Across the narrative synthesis, shorter travel times were consistently associated with better outcomes. Exploratory pooling suggested a greater than 3-fold higher odds of survival for interfacility journeys under 30 minutes (odds ratio (OR) = 3.25; 95% confidence interval (CI) = 1.90-5.57) and over 2-fold higher odds of survival for journeys from any location to hospitals at all thresholds (OR = 2.06; 95% CI = 1.60-2.65 for 2 hours; OR = 2.20; 95% CI = 1.46-3.33 for 1 hour; OR = 1.92; 95% CI = 1.10-3.34 for 30 minutes), though prediction intervals were wide, reflecting methodological and contextual diversity. CONCLUSIONS: We found that shorter journeys were associated with better perinatal outcomes, with the highest survival rates observed for journeys under 30 minutes to the hospital. Due to substantial contextual and methodological heterogeneity, pooled estimates should be interpreted as illustrative, rather than definitive. A travel time norm of 30 minutes or one hour is preferable to the two-hour threshold currently used in LMICs. To safeguard perinatal survival rates, any travel-time standard should be balanced with corresponding quality of care standards. REGISTRATION: PROSPERO: CRD42023460423.
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 →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Health-related quality of life domains relevant to people in Europe undergoing cancer treatment: a systematic review of qualitative research
PURPOSE: To identify and synthesize evidence from European qualitative studies on cancer-related quality of life outcomes, needs, experiences, preferences, and concerns of people undergoing cancer treatment in the last decade. METHODS: Systematic review ( https://www.crd.york.ac.uk/PROSPERO , CRD42024575065) of European studies using qualitative methodology, assessing constructs related to HRQoL, and involving adults receiving cancer treatment. The search was performed in PubMed and Scopus from January 2013 to July 2024. Titles, abstracts, and full texts screening, data extraction and risk of bias assessment were conducted independently by two researchers. The main outcomes were the themes reported in each study. The thematic analysis was performed by organizing the themes of the studies into categories. RESULTS: Out of 18,256 articles initially identified, 36 met the inclusion criteria: 21 with generic and 15 with specific objectives. Five categories encompassing 110 themes were identified from the generic studies: Psychological Function (n = 41), Clinical Management (n = 26), Symptoms and Physical Function (n = 18), Social Function (n = 16), and Life Disruption (n = 9). Eleven studies with specific objectives focused on clinical management with all their themes fitting within the categories identified in the generic studies. CONCLUSIONS: Results showed the predominance of psychological function and clinical management themes. Symptoms and physical function, social function, and life disruption maintained their importance within the classical HRQoL framework. The emergence of clinical management is consistent with the growing patient-centered care approach, suggesting the need to integrate this content into the evaluation of patients undergoing cancer treatment. LIMITATIONS: most European countries were not represented, and publication bias could hide traditional domains.
15 May 2026
Read appraisal →BMJ open
Implementation of safety management systems (SMS) in healthcare: a systematic review and international comparison
OBJECTIVES: Many industries where safety is a priority (eg, aviation) use safety management systems (SMSs), but evidence on their use in healthcare is needed to determine whether they could support patient safety improvement. We investigated the application of national-level SMSs to patient safety in terms of effectiveness, implementation and experience. DESIGN: Systematic review using a case study approach. DATA SOURCES: We identified patient safety organisations in each country and searched their websites. We also searched MEDLINE (in December 2023) and Embase (via Ovid), CINAHL (EBSCO) and Web of Science (in February 2024). ELIGIBILITY CRITERIA: Any evidence from five high-income countries that have publicly funded healthcare systems with universal coverage: Australia, Canada, Ireland, New Zealand and the Netherlands. We included publications on the effectiveness, implementation or experience of the patient safety approach in each country. DATA EXTRACTION AND SYNTHESIS: Included evidence was summarised and mapped onto an initial analytical framework based on analysis of SMSs in high-risk industries, enabling cross-country comparisons. Drafts were shared with experts from patient safety organisations in each country for comment. Formal quality appraisal was not possible as most evidence was non-evaluative. RESULTS: 53 publications were included, from Australia (5), Canada (7), Ireland (8), New Zealand (9) and the Netherlands (24). Only the Netherlands implemented a patient safety programme explicitly based on an SMS approach. Some aspects of patient safety in hospitals improved as a result, but there was significant variation in both the implementation of the programme and attributed outcomes.In the other four countries, the main components of an SMS were identified to some extent, along with evidence that their patient safety approaches had been influenced by concepts from high-risk industries and 'safety science' more widely. CONCLUSIONS: Emerging ideas from high-risk industries (beyond SMSs) and broader safety science have influenced all countries, reflecting increasing awareness of the need for initiatives to be context-specific to be successful. However, their implementation and impact need further evaluation. PROSPERO REGISTRATION NUMBER: CRD42023487512.
15 May 2026
Read appraisal →Journal of the American College of Cardiology
Behavioral Economics and Medication Adherence for Hypertension: A Randomized Clinical Trial
BACKGROUND: Nonadherence to antihypertensive medications is common. Mobile health (mHealth)-based behavioral economic interventions may improve adherence, but remain largely untested, especially in vulnerable populations. OBJECTIVE: The study sought to test whether an mHealth incentive lottery would lower systolic blood pressure (SBP) and improve adherence. METHODS: BETTER-BP (Behavioral Economics Trial To Enhance Regulation of Blood Pressure) was a randomized trial conducted in 3 safety-net clinics in New York City. Eligible participants were adults with hypertension prescribed at least 1 antihypertensive medication, with SBP >140 mm Hg, and poor self-reported adherence. In the intervention arm, an incentive lottery was administered via SMS messaging. All participants received passive adherence monitoring. The intervention lasted 6 months, with continued monitoring until 12 months. The primary clinical endpoint was change in SBP at 6 months. The primary process endpoint was adequate antihypertensive medication adherence (≥80% days adherent) from baseline to 6 months. RESULTS: Four-hundred participants (265 intervention:135 control) were enrolled with median age 57 years, 60.5% women, 61.5% Hispanic, and 20.3% non-Hispanic Black. Over 70% had Medicaid or no insurance. At 6 months, intervention arm participants were twice as likely to achieve adequate adherence (71% vs 34%; adjusted risk ratio: 2.04; 95% CI: 1.58-2.63), but there was no significant change in mean SBP (-6.7 mm Hg intervention vs -5.8 mm Hg control; P = 0.62). From 6 to 12 months, adherence was similar (31% intervention vs 26% control; adjusted risk ratio: 1.17; 95% CI: 0.83-1.65). CONCLUSIONS: In a diverse safety-net population, the BETTER-BP intervention doubled the rate of adequate antihypertensive medication adherence but did not reduce SBP at 6 months.
14 May 2026
Read appraisal →BMC health services research
Barriers and facilitators to telerehabilitation implementation: a mixed-methods study of German physiotherapists
BACKGROUND: The COVID-19 pandemic accelerated telehealth service adoption in physiotherapy, including telerehabilitation (TR). However, the extent of TR use and factors influencing its implementation in Germany remain unclear. This study aimed to evaluate TR use in physiotherapy (during COVID-19 lockdowns, current use, future intentions, conditions treated, content, setting, and session type) and identify barriers and facilitators from physiotherapists' (PTs) perspectives. METHODS: A mixed-methods sequential explanatory design was employed, combining a cross-sectional online survey (n = 152) with two focus group interviews (n = 9). The survey collected data on demographics, TR use, barriers, and facilitators. Focus groups explored themes emerging from the survey in greater depth. Data were analyzed using descriptive statistics and content analysis, integrating quantitative and qualitative findings. Quantitative and qualitative findings were integrated through explanatory linking and joint display in a comparative analysis table. RESULTS: TR use peaked during COVID-19 lockdown (32.26%) but decreased to 18.06% by October 2022, with 26.45% intending future use and 43.87% considering it. Among TR users, musculoskeletal conditions were most commonly treated (75%), followed by sports (38%), pulmonology (33%), and neurology (27%). The primary barrier was lack of physical examination (74% agreement). While technical challenges were not reported as a major barrier in the survey, interviews revealed significant concerns about insufficient internet bandwidth and technical infrastructure. Common reasons for using TR included promoting patient self-management (78% agreement) and broadening therapy options (69% agreement). Qualitative data identified additional implementation facilitators, including structured implementation processes, appropriate technical infrastructure, and patient involvement in decision-making. CONCLUSION: While TR implementation in German physiotherapy shows growth potential, several barriers currently limit its adoption. Successful implementation requires addressing PTs capabilities, knowledge gaps, professional identity concerns, and environmental factors. Addressing these issues could enhance patient care quality, increase service accessibility, and advance healthcare delivery models.
13 May 2026
Read appraisal →BMC medical research methodology
When to stop reviewing: validation of stop criteria in ASReview.
BACKGROUND: Systematic reviews are essential for evidence-based research, but they often require a great deal of time and effort. Although title and abstract (T&A) screening is just one part of the review process, it can be very time-consuming when search strategies retrieve large numbers of records. Given the exponential growth of scientific publications in recent decades, tools such as ASReview, which use machine learning (ML) for active learning-based screening, aim to reduce the workload. However, since ASReview helps users prioritise potentially relevant studies rather than supporting them in screening all records, a key question arises: at what point can the screening process safely stop without risking the omission of relevant studies when the entire dataset is not being reviewed? METHODS: This simulation study tested three proposed stop criteria for terminating screening in ASReview without loss of relevant data: (1) stopping after a calculated number of relevant studies based on an initial sample; (2) stopping after a fixed number of consecutively studies deemed irrelevant; (3) stopping after a predefined percentage of the dataset has been screened. A total of 35,000 automated title and abstract screenings were conducted using five datasets from the SYNERGY repository. Key outcomes included the percentage of studies screened until the last relevant study was found and the number of relevant studies missed under each stop criterion. RESULTS: The proportion of the dataset that needed to be screened to identify all relevant studies (as pre-classified in the SYNERGY dataset) varied greatly across datasets, ranging from 2.9% to 76.9% on average. None of the tested stop criteria could consistently identify all relevant studies across all datasets. Stop criterion 1 was reliable in only 2% of simulations. Stop criterion 2 showed high variability, with thresholds ranging from 2% to 61%, depending on the dataset. Stop criterion 3 failed to define a universal percentage applicable across datasets. CONCLUSIONS: ASReview can reduce screening workload by prioritizing potentially relevant studies through ML-based ranking, thereby allowing researchers to identify relevant studies earlier in the screening process. However, no stop criterion reliably ensures that all relevant studies are identified. Early stopping may result in missed studies, depending on dataset characteristics. Current stop criteria should be applied cautiously and potentially combined with quality assurance measures. Further research is needed to develop more robust and generalizable stopping rules. TRIAL REGISTRATION: Not applicable - this is a simulation study, not a registered systematic review.
13 May 2026
Read appraisal →Archives of women's mental health
Who is screened for perinatal mental health? findings from an academic health system in California.
PURPOSE: To identify sociodemographic characteristics, prenatal healthcare utilization, and health factors associated with completed mental health screenings during pregnancy and postpartum, respectively. METHODS: We examined electronic medical records (EMR) data on documented perinatal mental health screenings from over 6000 patients who delivered singleton infants within one academic healthcare system in California between 2021 and 2023. Dependent variables included having at least one Edinburgh Postnatal Depression Scale (EPDS) score documented in the EMR during (1) pregnancy and (2) postpartum, among those who completed postpartum care within 12 weeks of delivery. We used univariate frequencies to determine rates of completed screenings and logistic regression models to identify patient characteristics associated with completed screenings in each time period. RESULTS: Overall, 67% of patients completed screenings in pregnancy and 56% completed screenings in postpartum. In pregnancy, patients who were White, multiparous, did not prefer English, began prenatal care in the third trimester, and completed 10 or fewer prenatal care visits each had lower odds of completed screenings than their counterparts. In postpartum, patients who were White, privately insured, had vaginal deliveries, and had term births (at least 37 weeks of gestation) each had lower odds of completed screenings than their counterparts. CONCLUSIONS: Although California law requires perinatal mental health screening in pregnancy and postpartum , the identified inequities in screening suggest additional efforts are needed to ensure all perinatal patients are screened for mental health in accordance with the law. These efforts should be tailored to the populations least likely to be screened in order to achieve universal perinatal mental health screening.
13 May 2026
Read appraisal →JMIR medical informatics
Resource Use Patterns in US Telehealth Services: Machine Learning and Clustering Analysis Across 4 Specialties
BACKGROUND: The expansion of telehealth services, particularly during the COVID-19 pandemic, has transformed health care delivery in the United States. Telehealth promises greater access and resource efficiency by reducing wait times and appointment lengths, especially in specialties like psychiatry, behavioral health, bariatrics, and sleep medicine. However, disparities exist in adoption based on demographics, geography, and socioeconomic status, raising concerns about equitable access and optimal resource use. OBJECTIVE: This study aims to evaluate how telehealth impacts health care resource use across 4 specialties by examining 2 key metrics: patient-to-provider ratios and appointment durations. It seeks to understand how factors such as patient demographics, facility characteristics, and social determinants influence telehealth adoption and efficiency using a national dataset spanning from 2018 to 2023. METHODS: We analyzed a deidentified dataset from Epic Cosmos, covering outpatient visits across 48 US states (2018-2023). After data preprocessing and feature engineering, we applied 3 machine learning (ML) models (random forest, extreme gradient boosting, and deep neural networks) to predict resource use. Using the model performing the best, feature importance was assessed using Shapley Additive Explanations values. We then used k-means clustering to group facilities into clusters per specialty. Comparative analyses were conducted to evaluate differences in use among clusters, during and after the pandemic. RESULTS: Telehealth use peaked in 2020 and has remained above prepandemic levels since then. In 2018-2023, telehealth adoption reached 36.9% (4,543,021/12,311,710) in psychiatry, 23.9% (5,321,099/22,264,013) in behavioral health, 21.2% (924,333/4,360,061) in bariatrics, and 16.8% (851,803/5,070,256) in sleep medicine. Telehealth visits were consistently shorter than office visits (mean reduction 12.24 minutes; SD 3.33 minutes; P=.18), while patient-to-provider ratios varied significantly across specialties. Among ML models, extreme gradient boosting regression achieved the best performance (patient-to-provider ratios: R2=0.96-0.99; appointment durations: R2=0.61-0.69). Shapley Additive Explanations analysis identified visit type, telehealth use, facility size, rurality, and Social Vulnerability Index household vulnerability as the strongest predictors. Comparative analyses showed significant differences across clusters (all P<.05). CONCLUSIONS: Telehealth has become a sustainable component of health care, enhancing access and efficiency across both rural and urban areas. However, its impact varies across specialties and regions, highlighting the need for targeted strategies such as staffing support for vulnerable populations, infrastructure investments in rural facilities, and reimbursement models that reflect telehealth's resource use. This study provides robust evidence from ML and clustering analyses, demonstrating how telehealth shapes resource use and offering actionable insights for equitable and sustainable integration.
12 May 2026
Read appraisal →International journal of health care quality assurance
Understanding trust in mandatory telemedicine use during COVID-19: a modified health belief model approach
PURPOSE: This study explores the determinants of trust in telemedicine when its use was mandated during the COVID-19 pandemic in South Korea. It extends the health belief model by incorporating two service related constructs: service quality and perceived cost. DESIGN/METHODOLOGY/APPROACH: An online survey was conducted with 288 individuals who were confirmed COVID-19 patients and required to use telemedicine services during isolation. Partial least squares structural equation modeling was applied to examine the relationships between health beliefs including perceived severity, perceived susceptibility and self-efficacy, service perceptions such as service quality and perceived cost, trust in telemedicine and behavioral intention to use telemedicine services. FINDINGS: The results showed that self-efficacy, service quality and perceived cost positively influenced trust in telemedicine. In contrast, perceived severity and perceived susceptibility did not have a meaningful impact. Trust was found to be a key factor in shaping patients' intention to continue using telemedicine services. Overall, the proposed model was supported, highlighting the importance of confidence and service related perceptions in building trust under mandatory usage conditions. ORIGINALITY/VALUE: This study provides empirical evidence on trust formation under mandatory telemedicine use, a scenario rarely explored in the literature. It demonstrates that trust is primarily driven by users' self-efficacy and service perceptions rather than by health risk perceptions, offering practical guidance for enhancing remote care engagement during policy enforced use conditions.
11 May 2026
Read appraisal →PloS one
FairCareNLP: An AI-driven patient review analyzer for healthcare.
OBJECTIVE: To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis. MATERIALS AND METHODS: We designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), and fairness modules. Bias mitigation was addressed through the integration of three complementary approaches: adversarial debiasing, Hard Debiasing, and Iterative Null-space Projection (INLP). Multiple BERT-based models (DistilBERT, BioBERT, RoBERTa-base, BERT-base-uncased) were trained and evaluated under varying hyperparameters and fairness/adversarial loss configurations. Model performance was assessed using accuracy, F1, recall, precision, AUC, Equalized Odds (EOD), and Word Embedding Association Test (WEAT) metrics. RESULTS: Adversarial loss ([Formula: see text]) consistently decreased model performance across accuracy, F1, precision, and recall. In contrast, Hard Debiasing and INLP improved WEAT scores while preserving or enhancing other metrics, with INLP yielding the best overall performance. Specifically, INLP with fairness loss improved EOD by 14%, gender WEAT scores by 15%, and achieved slight gains for ethnicity and socioeconomic WEAT scores. The best model achieved accuracy of 0.856, F1 score of 0.812, recall of 0.798, and precision of 0.829. The key theme analysis module identified 82% of expert-labeled themes, though 21% of patient comments lacked expert labels for valence or related attributes. DISCUSSION: Our results demonstrate the trade-offs between fairness and performance in bias mitigation strategies. While adversarial debiasing reduced predictive accuracy, INLP and Hard Debiasing improved fairness without significant degradation in task performance. Gender bias was easier to mitigate than multi-class sensitive attributes such as ethnicity and income. This difference indicates a need for fairness techniques designed for multi-class sensitive attributes. CONCLUSION: This work presents an NLP pipeline for patient feedback analysis with multiple debiasing strategies. This pipeline improves the fairness and accuracy of insights from unstructured patient reviews and supports inclusive patient-centered care.
11 May 2026
Read appraisal →Archives of orthopaedic and trauma surgery
Healthcare utilization following hip fractures based on social vulnerability status in the US: an analysis of 2016-2020 nationwide readmissions data
INTRODUCTION: Social vulnerability (SV) influences rehabilitation and postoperative care for patients with hip fracture. However, most previous work relies on area-level measures that overlook interindividual variation. The recent adoption of ICD-10 Z-codes allows clinical identification of patient-level SV and may offer a better understanding of its impact. This study aimed to evaluate healthcare utilization, including readmissions, discharge disposition, and length of stay (LOS) in surgically treated hip fracture patients with and without clinically acknowledged SV. METHODS: Adults surgically treated for hip fracture between 2016 and 2020 were included from the Nationwide Readmissions Database. SV was defined as having at least one documented relevant ICD-10 Z-code. Primary outcome measures included complications, LOS, discharge disposition, and 30- and 90-day readmissions, stratified by SV and evaluated using chi-square analyses. Multivariable logistic regression assessed long LOS (≥ 5 days) and discharge to home, adjusting for age, insurance/income status, and substance use. RESULTS: Patients with SV were younger (35.6% with SV vs. 50.1% without SV were 81+), had a lower median household income (38.8% with SV vs. 25.7% without SV were in the lowest quartile), and were more often insured by Medicaid (19.3% vs. 3.8%). Alcohol/drug use disorders were significantly more prevalent in patients with SV (18.5% vs. 4.5%). SV was associated with 47% higher odds for long LOS (1.47, 1.41-1.54) and 23% higher odds for discharge to home (1.23, 1.16-1.30) but comparable 90-day readmissions (21.2% vs. 19.8%). CONCLUSION: Among surgically treated hip fracture patients, SV was associated with higher odds of long LOS and discharge to home but no meaningful difference in readmissions. The small number of patients with clinically documented SV highlights the limited reporting by healthcare workers. This analysis of a nationwide all-payer database highlights the need to identify these higher risk patients and implement appropriate care pathways to reduce healthcare utilization.
10 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Telemedicine adoption, pandemic-related fear, and treatment adherence in cancer care during COVID-19: a prospective cohort study
PURPOSE: To evaluate the impact of telemedicine adoption and pandemic-related fear on treatment adherence, healthcare utilization, and supportive care processes among patients undergoing cancer therapy during the COVID-19 pandemic. METHODS: In this prospective cohort study, we followed 149 cancer patients over two years to evaluate telemedicine utilization, treatment modifications, and COVID-19-related fear. We employed validated psychometric tools, including the COVID-19 Fear Scale. RESULTS: Telemedicine adoption (24.8% of patients) was associated with fewer hospital visits (7.3 ± 3.2 vs 9.4 ± 4.1, p = 0.004) and reduced emergency room utilization (16.2% vs 46.4%, p < 0.001). Treatment modifications occurred in 55.9% of patients, predicted by COVID-19 infection (OR = 2.8, 95% CI:1.7-4.6) and neutropenia (OR = 2.1, 95% CI:1.3-3.4). High fear scores were linked to increased mortality (8.1% vs 2.7%, p = 0.032), disease progression (37.8% vs 21.4%, p = 0.018), lower treatment adherence (75.7% vs 87.5%, p = 0.041), and higher hospitalization rates (51.3% vs 24.1%, p < 0.001). CONCLUSION: This study demonstrates that telemedicine can reduce acute care utilization while treatment modifications and high pandemic-related fear significantly worsen cancer outcomes. These findings highlight the need for oncology teams to proactively integrate remote care strategies and targeted psychological support to maintain treatment adherence and mitigate adverse outcomes during health crises.
10 May 2026
Read appraisal →International journal of medical informatics
Harmonizing patient-reported outcome measures for nasal complaints using traditional and machine learning methods
BACKGROUND: Nasal obstruction measurement instruments are widely used in the field of nasal surgery. There are various scales that measure nasal obstruction and they differ regarding the number of items, their wording, and the type of response options. In order to pool the data and analyze it together, it is necessary to harmonize it so that we can compare participants' nasal obstruction scores irrespective of instrument they filled in. Data harmonization is still not used in the field of nasal obstruction assessment. THE AIM: The aim of this study was to find the best harmonization method in terms of predicting the scores on a target instrument based on the scores from another instrument as precise as possible in the case of four different nasal complaints instruments. A method was sought to find a transformation of scores on the NOSE, Utrecht-Q and SCHNOS that makes them equivalent to ENFAS scores. METHODS: A total of 1324 unique patients completed all four measurement instruments. We tried linear equating, Item Response Theory (IRT), and the following machine learning methods: linear regression, random forest regression, support vector machine regression, and neural network. We used the root-mean-square error (RMSE) of differences between predicted and observed scores to evaluate the quality of harmonization in 5-fold cross-validation. RESULTS: The ML methods gave overall the best results (the lowest RMSEs) and outperformed IRT (which is considered as a common choice for data harmonization in psychometrics). CONCLUSION: The ML methods led to the best quality of the results, confirming their strong potential for data harmonization. This study shows that next to linear equating and IRT that are commonly used for data harmonization, we can also use ML methods for the same purpose and, by doing so, to even increase the quality of the harmonization in certain use cases.
6 May 2026
Read appraisal →PloS one
A GNN-based approach for accurate trade balance forecasting and interpretable analysis
In this study, we developed a machine learning pipeline to predict trade balances across 229 countries, utilizing a Graph Neural Network (GNN), and compared it with several deep learning and regression-based models. The data preprocessing involved handling missing values, normalizing features, and conducting exploratory data analysis to uncover key patterns. Feature selection was performed using a Random Forest Regressor to identify the most influential predictors of trade balances. We then evaluated multiple models, including a complex Deep Neural Network (DNN), Transformer with multi-head attention, Random Forest, and a hybrid ensemble model, using various regression metrics. Among these, the GNN proved to be the most effective model, achieving an MSE of 0.06, RMSE of 0.26, MAE of 0.18, and an R² of 0.91. These results demonstrate that GNN outperforms other models in terms of accuracy, robustness, and consistency in predicting trade balances. We compared models across several key evaluation metrics and conducted a detailed comparison of residual plots to assess prediction quality and error distribution. Residual plots and ROC curves were used to validate the reliability and performance of the GNN and other models, ensuring robust and accurate predictions across the board. This study highlights the potential of machine learning techniques to improve trade balance forecasting, providing policymakers and economists with a more adaptable and precise tool for navigating complex global trade dynamics. The findings contribute to more informed economic strategies and enhanced forecasting methodologies.
4 May 2026
Read appraisal →Journal of medical Internet research
Digital Health Interventions for Family Members of ICU Patients: Current Evidence and Future Directions
A recent systematic review and meta-analysis of randomized trials evaluating digital health interventions for family members of intensive care unit (ICU) patients found no significant improvements in anxiety, depression, posttraumatic stress, quality of life, or communication quality. Rather than concluding that digital approaches are inherently ineffective, we argue that these null findings reflect identifiable and remediable limitations in intervention design, outcome measurement, and trial methodology. In this commentary, we examine four structural barriers that currently constrain the evidence base and outline the conditions that next-generation trials must meet to adequately address the questions raised by this review.
4 May 2026
Read appraisal →JMIR research protocols
Geriatric Telemanagement of Health Conditions (GET HEALTH) in Nursing Home Residents Recently Discharged From the Hospital: Protocol for a Before-After Study
BACKGROUND: Nursing home (NH) residents have a high burden of multimorbidity and disability and are frequently hospitalized. Comprehensive geriatric assessment might reduce hospitalization. However, few geriatricians are working in NHs. Technology might be used to overcome this problem. OBJECTIVE: This study aims to verify if a geriatric telemanagement model improves patient outcomes in NH residents recently discharged from the hospital. METHODS: Clinical outcomes of NH residents recently discharged from the hospital are compared before and after the implementation of GTM, where a geriatrician evaluates each patient during televisits with NH staff and discusses the case with the general practitioner. NH residents aged ≥70 years readmitted to NHs after hospitalization in the 3 years prior to the COVID-19 pandemic (2017-2019) represent the control group enrolled in the retrospective preintervention phase. The primary outcome of the study is the hospital readmission rate during 6-month follow-up. Health technology assessment evaluates the cost-effectiveness of model implementation. RESULTS: A total of 333 NH residents were included in the preintervention retrospective study. Data collection started in February 2022 and was completed in February 2023. A total of 104 NH residents were recruited in the intervention study, from March 2024 to March 2025. Data analysis is currently in progress. CONCLUSIONS: Currently, NHs in Italy are striving to manage the complexity of older patients who are often hospitalized. A geriatric telemanagement model might reduce hospitalizations, and therefore negative outcomes, as well as health care costs in this vulnerable population.
3 May 2026
Read appraisal →Turkish journal of ophthalmology
Large Language Models in Ophthalmology: A Bibliographic Analysis
This study evaluated the distribution of research on the use of large language models (LLMs) in ophthalmology through a bibliographic analysis of articles retrieved from PubMed through November 2024. Studies were categorized into four main areas of LLM application: clinical decision-making (further divided according to subspecialties), education, patient interactions, and miscellaneous applications. Descriptive statistics were used to analyze the distribution of studies by ophthalmic subspecialty, geographical region, journal quality, and author characteristics, including gender and scholarly impact (h-index and i10-index). The findings revealed that clinical decision-making was the most common application (43.7%), with the majority of studies in this subgroup focusing on the retina (39.5%). Geographically, most of the research originated from North America (48.3%), followed by Asia (29.9%) and Europe (20.7%). Most studies were published in high-impact journals (Q1 journals: 74.7%), particularly for those related to clinical decision-making in retina (80.0%), glaucoma (100%), and multiple subspecialties (87.5%). Gender disparities were evident across all author roles, with female authors accounting for only 29.9% of first authors, 25.3% of last authors, and 26.4% of corresponding authors. The results suggest a need for greater diversity in terms of gender and geographic representation in LLM research in ophthalmology to promote inclusive progress in the field. Bu çalışma, PubMed’de Kasım 2024’e kadar yayımlanmış göz hastalıkları alanında büyük dil modellerinin (BDM) kullanımı ile ilgili araştırmaların bibliyografik analizini amaçlamaktadır. Çalışmalar, klinik karar desteği (yandallara göre ayrılmış), eğitim, hasta etkileşimleri ve diğer uygulamalar olmak üzere BDM yöntemlerinin dört ana uygulama alanına göre kategorize edilmiştir. Tanımlayıcı istatistikler kullanılarak çalışmaların, oftalmoloji alt uzmanlıkları, coğrafi bölge, dergi kalitesi ve yazar özellikleri (cinsiyet ve bilimsel etki, h-endeksi ve i10-endeksi dahil) açısından dağılımı analiz edilmiştir. Bulgularımız, klinik karar desteğinin en yaygın uygulama olduğunu (%43,7) ve bu alt grup içerisinde çalışmaların çoğunun retina üzerine odaklandığını (%39,5) ortaya koymuştur. Coğrafi olarak, araştırmaların çoğu Kuzey Amerika’dan (%48,3) yayımlanmakta olup sırasıyla Asya (%29,9) ve Avrupa (%20,7) Kuzey Amerika’yı izlemektedir. Çoğu çalışma, özellikle de klinik karar desteği alt grubunda, retina (%80,0), glokom (%100) ve birden fazla alt uzmanlık dalını kapsayan çalışmalar (%87,5), yüksek etki faktörüne sahip dergilerde yayımlanmıştır (Q1 dergileri: %74,7). Cinsiyet dağılımındaki eşitsizlik, tüm yazar rollerinde belirgin olarak gözlemlenmiştir. Çalışmalarda kadın yazarlar, ilk yazarların yalnızca %29,9’unu, son yazarların %25,3’ünü ve sorumlu yazarların %26,4’ünü oluşturmuştur. Sonuçlar, göz hastalıkları alanındaki BDM araştırmalarında cinsiyet ve coğrafi temsilde daha fazla çeşitliliğe ihtiyaç duyulduğunu ve bu çeşitliliğin alandaki kapsayıcı ilerlemeyi teşvik edeceğini göstermektedir.
2 May 2026
Read appraisal →BMJ open
Do studies of interventions to improve laypeople's critical thinking about health choices assess potential harms? A systematic review.
OBJECTIVES: To make informed health choices, and avoid waste and unnecessary suffering, people need critical thinking skills. However, like health interventions, educational interventions can have adverse effects. In this systematic review, the objective was to assess the extent to which researchers have included potential adverse effects in studies of interventions intended to improve the critical thinking of laypeople about health choices. DESIGN: This study was a systematic review, in which we updated the search for an earlier systematic review of intended effects of relevant interventions. The earlier review did not address potential adverse effects. We did not update the analysis of intended effects. DATA SOURCES: We searched Cochrane Central Register of Controlled Trials (CENTRAL), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Epistemonikos, Medical Literature Analysis and Retrieval System Online (MEDLINE), Education Resources Information Center (ERIC) and Web of Science up to March 2025. In addition to studies from the original review and updated search, we included any additional studies included in a similar, even earlier review. Our unit of analysis was study report (eg, journal article). ELIGIBILITY CRITERIA: We included all studies from the original review. We applied the same inclusion criteria to the results of the updated search: the study included a comparison, the population was laypeople and the intervention was intended to improve understanding of ≥1 key concept for informed health choices. DATA EXTRACTION AND SYNTHESIS: We extracted data about study design (randomised trial or other), participants (children, adolescents or adults), study setting (countries), main intervention (resources delivered to participants) and comparator (usual/no intervention or other). For the analysis, we extracted verbatim text describing any assessment of a potential adverse effect of the intervention. We conducted a narrative synthesis of the extracted data. RESULTS: We included 35 reports of quantitative studies (including multi-method and mixed-methods studies). Most often, the study was a randomised trial, the setting was a high-income country, the population included adults (including university students) and the intervention was school-based (including university). In one of the 35 reports, authors described assessing a potential adverse effect. CONCLUSION: To our knowledge, this is the first systematic review assessing the extent to which researchers have assessed adverse effects of any category of educational interventions. Our review shows that researchers generally have not assessed potential adverse effects of interventions to improve critical thinking about health choices. Researchers should pay more attention to such effects, while policymakers and educators making decisions about implementing relevant interventions should consider the lack of evidence. The findings of this study suggest a need for research that facilitates assessing potential adverse effects of interventions to improve critical thinking about health choices.
27 Apr 2026
Read appraisal →PloS one
Disparity of ophthalmic surgeries in Japan
PURPOSE: Although regional disparities in access to surgical care have been reported across medical specialties, nationwide evaluations of variation in ophthalmic surgical services remain limited. This study examined prefecture-level differences in major ophthalmic surgeries in Japan using publicly available claims data. METHODS: We analyzed the ninth release of the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB Open Data) for fiscal year 2022 (April 2022-March 2023). Prefecture-level procedure counts were extracted for cataract surgery (K282), vitrectomy (K279-K281 and related codes), glaucoma surgery (relevant K-codes), and corneal transplantation. To reduce potential underestimation in non-Diagnosis Procedure Combination settings, ophthalmology-related procedures reimbursed under the Short-Stay Surgery Basic Fee 3 (A400) were identified and incorporated according to predefined mapping rules. Surgical rates per 100,000 population and per board-certified ophthalmologist were calculated. Regional inequality was quantified using population-weighted Gini coefficients. Associations between ophthalmologist density and surgical volume were assessed using Pearson's correlation coefficients. RESULTS: Nationwide totals were 1,777,502 cataract surgeries, 154,336 vitrectomies, 80,753 glaucoma surgeries, and 2,895 corneal transplantations. Prefecture-level surgical rates per 100,000 population varied 1.8-fold for cataract surgery, 3.3-fold for vitrectomy, and 7.8-fold for glaucoma surgery. Several prefectures reported no corneal transplantation cases. Population-weighted Gini coefficients were 0.064 for cataract surgery, 0.125 for vitrectomy, 0.190 for glaucoma surgery, and 0.351 for corneal transplantation. Similar patterns were observed after adjusting for age structure, indicating that regional disparities were not solely explained by demographic differences. Ophthalmologist density varied 2.5-fold across prefectures and was positively correlated with surgical volume for cataract surgery and vitrectomy, but not for glaucoma surgery. CONCLUSIONS: Using nationwide claims data, we identified substantial regional variation in major ophthalmic surgical procedures in Japan, with greater inequality observed for more specialized surgeries. These findings provide a population-level description of ophthalmic surgical distribution and may inform future evaluations of healthcare resource allocation.
27 Apr 2026
Read appraisal →BMJ health & care informatics
Using a large language model artificial intelligence agent to improve the efficiency of clinical quality measure evidence evaluation: a case study.
OBJECTIVES: To evaluate the feasibility and performance of a large language model (LLM)-based artificial intelligence (AI) agent, implemented within a structured Claim-Argument-Evidence System (CAES), for supporting the review of clinical quality measure (CQM) evidence in the Centers for Medicare & Medicaid Services Consensus-Based Entity (CBE) endorsement process. METHODS: The CBE conducted a pilot study using a previously endorsed measure. CAES extracted claims and citations from a submitted diagnostic performance measure for pneumonia, automatically retrieved additional relevant evidence from PubMed abstracts and assessed the quality, confidence and agreement of evidence supporting each claim. The system's assessments were compared with the judgement of a subject matter expert (SME). RESULTS: CAES completed the assessment in approximately 5 hours. The SME agreed with the CAES-assigned claim statuses for 69% of claims, was neutral for 11% and disagreed for 14%. Disagreements primarily stemmed from the need for contextual interpretation beyond abstracts. DISCUSSION: Manual evaluation of CQM evidence requires significant time and resources, estimated at over 2400 labour hours per review cycle, limiting efficiency and transparency.The AI agent evaluated 64 claims and 355 claim-evidence pairs related to the pneumonia diagnosis measure. It assigned claim statuses based on evidence strength and generated justifications. CONCLUSION: This pilot demonstrated the feasibility and potential of LLM-based AI agents to improve the efficiency and transparency of evidence review for CQMs. Further development is needed to incorporate additional data sources and extend applicability across the measure development lifecycle.
27 Apr 2026
Read appraisal →JMIR mental health
Expected Competencies and Personal Attributes of Digital Health Navigators to Support Digital Mental Health Care: Focus Group and Interview Study With Patients and Health Care Professionals
BACKGROUND: Digital mental health apps (DMHAs), and in particular digital therapeutics (DTx), offer promising opportunities to support mental health care. However, their effective use in outpatient settings in Germany remains limited. To overcome this gap, the role of digital health navigators (DHNs) has been introduced. DHNs are trained individuals who support patients and health care professionals in selecting, using, and integrating DMHAs into care. Despite increasing interest in this role, there is limited evidence on the competencies, knowledge, and personal attributes required for DHNs to work effectively in mental health settings. OBJECTIVE: The study aims to explore the expected competencies, knowledge areas, and personal attributes that DHNs need to effectively support the implementation and use of DTx in outpatient mental health care. METHODS: As part of the prestudy of the Digital Navigators for Acceptance and Competence Development with Mental Health Apps (DigiNavi) study, a qualitative study was conducted involving 35 participants (7 general practitioners, 8 patients in general practice, 11 outpatient psychiatrists/psychologists, and 9 patients in psychiatric outpatient clinics) from different general practices and psychiatric outpatient clinics in Germany. A total of 17 semistructured interviews and 4 focus groups were conducted to explore expectations of DHNs. Data were analyzed using qualitative content analysis. RESULTS: Participants emphasized that DHNs should combine strong interpersonal skills (empathy, patience, and sensitive communication) with technical and basic clinical competencies. Most favored DHNs as integrated clinical team members (eg, medical assistants), citing their existing patient relationships, but noted time and training constraints. Key expectations included the ability to support patients with DTx use, adapt communication to individual needs, and convey data privacy information clearly. Foundational knowledge of mental health conditions and sensitivity to crises were considered important for identifying warning signs and escalating concerns. While DHNs were seen as essential intermediaries between patients, health care professionals, and DTx, participants highlighted the necessity for clearly defined roles, structured training, and realistic expectations to prevent role overload and enable sustainable implementation in outpatient mental health care. CONCLUSIONS: DHNs require a specialized skill set that bridges clinical understanding, digital expertise, and interpersonal competence. Our results lay the groundwork for developing training curricula and implementation strategies that align with real-world expectations for the DHN role. Defining these core competencies is essential for supporting the sustainable and effective integration of DMHAs into mental health care.
26 Apr 2026
Read appraisal →Behavior research methods
A review and evaluation of doubly robust approaches for estimating average treatment effects
In nonexperimental studies, obtaining an unbiased estimate of the average treatment effect (ATE) typically requires two key assumptions: that all relevant covariates are measured (i.e., no unmeasured confounding) and that the statistical model used for covariate adjustment is correctly specified. Two common approaches for adjustment are specifying an outcome model and propensity score weighting. To mitigate bias from model misspecification, doubly robust methods combine both approaches, ensuring unbiased ATE estimates if either the outcome model or the propensity score model is correctly specified. In this study, we review four doubly robust methods that have received considerable attention in the methodological literature but remain underutilized in psychological research: augmented inverse probability weighting, regression weighted by the inverse propensity score, regression incorporating the inverse propensity score as a covariate, and calibrated propensity score weighting. Using two simulation studies, we compare these methods with regression estimation and inverse probability weighting estimators. Our results suggest that doubly robust methods-particularly regression weighted by the inverse propensity score-offer greater protection against bias from model misspecification across various data-generating scenarios. We also discuss practical considerations for implementing doubly robust methods, including weight normalization, propensity score truncation, and potential efficiency losses due to overfitting. The different methods for estimating the ATE are illustrated in a data example.
26 Apr 2026
Read appraisal →Journal of medical economics
Value frameworks for digital health technologies: a comparative analysis
AIMS: This study aimed to review and compare existing value frameworks for digital health technologies (DHTs) and identify common domains to guide their evaluation. MATERIALS AND METHODS: A comprehensive literature search was conducted across PubMed, Embase, and Google Scholar, along with citation and website searches, was conducted in August 2025. English-language publications that provided guidance on evaluating value of DHTs were included. A narrative review approach was employed to synthesize findings and identify key value domains and indicators. RESULTS: From an initial pool of 1,132 articles, four met the inclusion criteria and formed the basis of the review, supplemented by additional frameworks from relevant organizations. Six core value domains were identified: technical and security aspects, data rights and governance, clinical characteristics, economic characteristics, health inequalities, and user preferences. Five value frameworks from the United Kingdom (UK) National Health Service, Digital Therapeutic Alliance, World Bank, Institute for Clinical and Economic Review-Peterson Health Technology Institute, and UK National Institute for Health and Care Excellence were compared across these domains. A heat map analysis demonstrated variation in the inclusion of indicators, reflecting differing stakeholder priorities and perspectives. LIMITATIONS: Limitations include potential bias, subjectivity, and limited scope inherent in a narrative review, although elements of PRISMA were applied to reduce risks. Non-English frameworks and those specific to certain technologies were excluded. Additionally, frameworks for both DHTs and digital therapeutics were considered together. CONCLUSION: Six core value domains are consistently emphasized across value frameworks. However, variations across value frameworks highlights the influence of stakeholder needs and priorities. Further research is needed to refine these frameworks and support consistent and informed decision-making in DHTs adoption and implementation. Digital health technologies (DHTs) are increasingly used in healthcare, but their value is assessed in variable ways. We reviewed published studies to identify common elements. Six key areas emerged: technical and security aspects, data rights and governance, clinical and economic characteristics, health inequalities, and user preferences. While some areas are consistently included, others vary across frameworks. Our findings suggest that more consistent approaches could support clear decisions about adopting digital health tools.
25 Apr 2026
Read appraisal →Neurology
Virtual vs In-Person Neurologic Ambulatory Care: A Case-Control Study of Subsequent Health Care Utilization
BACKGROUND AND OBJECTIVES: Implementation of telemedicine expanded options for outpatient neurology care. It remains uncertain which new neurology patients can be appropriately evaluated virtually. We compared subsequent health care utilization after virtual vs in-person new patient neurology visits across 3 academic medical centers. METHODS: We conducted a retrospective multicenter cohort study of adults with a new outpatient neurology visit from September 2020 through December 2021 using the Vizient Clinical Data Base and Clinical Practice Solutions Center databases. Virtual and in-person patients were matched 1:1 using propensity scores incorporating demographics, clinical characteristics, time period, and previous health care utilization. Outcomes were analyzed overall and stratified by neurologic chief complaint category and institution. We compared rates of subsequent neurologic clinic follow-up, emergency department (ED) visits, and hospitalizations after virtual and in-person encounters. Testing and all-cause ED visits/hospitalizations were also assessed. RESULTS: We identified 10,428 virtual and 36,767 in-person neurology new outpatient visits. After propensity score matching, 8,202 virtual visits were matched to 8,202 in-person visits. Neurology follow-up within 90 days did not differ between virtual and in-person visits (24.6% vs 23.7%, p = 0.18). Thirty-day neurology clinic follow-up was slightly lower after virtual visits, whereas follow-up at 6 months and 1 year was similar between groups. Neurologic ED visits and hospitalizations within 90 days were similar (0.9% vs 0.8%, p = 0.23 and 1.8% vs 1.7%, p = 0.47, respectively). All-cause ED visits and hospitalizations within 90 days were also comparable (1.8% vs 1.7%, p = 0.59 and 2.2% vs 1.8%, p = 0.13, respectively). Analyses by chief complaint found that 90-day follow-up was higher after in-person visits for dementia, whereas 30- and 90-day follow-up was higher after virtual visits for Parkinson disease and multiple sclerosis, and 90-day follow-up was higher after virtual visits for headache. Testing was more frequent after in-person visits for certain chief complaints. DISCUSSION: In this propensity score-matched multicenter cohort, new neurology patients seen virtually had similar downstream utilization as those seen in-person, including comparable 90-day follow-up and similar neurologic and all-cause ED visits and hospitalizations. Although follow-up varied modestly by chief complaint and testing was more frequent after some in-person visits, no major differences emerged overall.
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 →Journal of medical Internet research
Exploring Patient Perspectives on the Use of Artificial Intelligence to Inform Joint Decision-Making for Patients With Multiple Conditions in Primary Care in the United Kingdom: Qualitative Study
BACKGROUND: Multimorbidity, living with 2 or more long-term health conditions, is increasing globally and now affects over one-quarter of adults in England. People with multiple long-term conditions (MLTC) face complex health and treatment challenges, often experiencing fragmented care within systems oriented toward single-disease management. Artificial intelligence (AI) has the potential to support clinicians and patients by analyzing complex health data, optimizing treatment strategies, and predicting disease trajectories. OBJECTIVE: The OPTIMAL (Optimizing Therapies, Disease Trajectories, and AI-Assisted Clinical Management for Patients Living with Complex Multimorbidity) project aims to develop AI-enabled tools to support shared decision-making in primary care. This study explored how patients with MLTC perceive the use of AI to inform joint decision-making in primary care. METHODS: Semistructured interviews were conducted via telephone or video call with 29 adults living with MLTC between July and November 2023. Participants were recruited through general practitioner practices via the Clinical Practice Research Datalink and community-based organizations across the West Midlands. Interviews were transcribed verbatim and analyzed thematically using an inductive approach. Members of a patient advisory group were involved in developing study materials, refining the interview guide, and reviewing emerging findings to ensure relevance and authenticity. RESULTS: Participants identified potential benefits of AI in enhancing consultation efficiency and accuracy, improving access to information for patients and clinicians, promoting early detection of health changes, and reducing health care inequalities. However, concerns were raised about the loss of human interaction, data privacy and security, transparency of algorithms, and the potential for bias and inequity in AI systems. Trust and acceptance varied by age and familiarity with technology. Some participants expressed uncertainty about what AI entails and how it could be used in primary care. CONCLUSIONS: Patients with MLTC viewed AI-assisted decision-making in primary care with cautious optimism. While many recognized potential benefits for coordination and personalization of care, others expressed reservations about privacy, fairness, and the risk of diminished human connection.
24 Apr 2026
Read appraisal →JMIR formative research
Access to Technology-Mediated Community Mental Health Care Among Low-Socioeconomic Status Consumers With Serious Mental Illness: Qualitative Study
BACKGROUND: Access to mental health care is critical for the effective management of serious mental illness (SMI), but consumers with low socioeconomic status (SES) have lower rates of service usage and worse retention in care. Digital technologies are often lauded as a way to bridge access gaps; however, little is known about how technology-mediated care may influence care access among low-SES consumers and how consumers use technology in care access. OBJECTIVE: This study aimed to examine the applicability of Levesque et al's access framework to technology-mediated care for SMI and analyze how low-SES consumers use technology to facilitate care access. Furthermore, the study assesses whether and how technologies are involved in care access at multiple points within the process of accessing care. METHODS: This study used 2 qualitative methods: ethnographic observations at a mental health treatment court and interviews with low-SES consumers with SMI using community mental health care (n=14) and key informant interviews with health and service providers working with this population (n=14). Observations occurred from July 2022 through September 2023, and interviews occurred between January 2022 and May 2024. Data analysis involved both inductive and deductive coding approaches. Data from both the interviews and observations were analyzed in NVivo and further triangulated through analytic memos. RESULTS: Levesque et al's framework required several extensions to accommodate technology-mediated care related to SMI for low-SES consumers: (1) a cyclical rather than linear trajectory; (2) simultaneous care acquisition from multiple health and service providers; (3) staying in care long-term; (4) identification of both one-time and ongoing health needs; and (5) an emergency pathway for entering care. Consumers often faced challenges related to the varied digital requirements of each provider and a dearth of integrative, patient-facing tools like portals. Within this context, some consumers use mobile apps, communication, and telehealth technologies across various care access stages. Consumers used technology by figuring out how to navigate technology-mediated care, especially by leaning on others, such as case managers, for support. These others provided consumers with temporary technologies, showed them how to use technologies, and accompanied them through the process of using technology for accessing care. CONCLUSIONS: This study highlights that accessing care is iterative and ongoing, involving multiple forms of co-occurring service provision. A theoretical contribution of this work is its extension of Levesque et al's care access framework to better reflect technology-mediated care for SMI among low-SES consumers. This work also underscores ongoing challenges for accessing technology-mediated care and the importance of human support in addressing access difficulties. Clinical implications include incorporating digital readiness assessments and providing comprehensive guidance on how consumers can effectively use technologies for care. Future work should investigate how technology-mediated care can make care access easier rather than harder.
24 Apr 2026
Read appraisal →American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
Feasibility and acceptability of incorporating telemedicine into pharmacy services in the rheumatology clinics of the School of Medicine of the University of Puerto Rico
PURPOSE: Incorporating telemedicine into pharmacist services can promote medication adherence, reduce polypharmacy, and prevent adverse drug events as part of identification of drug-related problems. Our primary aim was to measure the acceptability to patients of telemedicine services using a standardized patient satisfaction survey and compare it to that for face-to-face services. Our secondary aim was to assess the feasibility of the telemedicine modality for pharmaceutical care interventions by measuring, quantifying, and categorizing drug-related problems identified during the service. METHODS: The study followed a prospective crossover design from October 2021 to September 2022. Adult patients (21 years or older) with rheumatic diseases evaluated at a university-based outpatient clinic were eligible to be enrolled. After the intervention, patients completed a survey regarding satisfaction and perception of their appointment. Descriptive statistics were used for patient demographic characteristics. Patient satisfaction with the modalities was compared using a t test. RESULTS: In this study, 296 patients were enrolled with a mean (SD) age of 50.4 (12.5) years. Participating patients were utilizing a mean (SD) of 10.2 (4.7) medications. No significant differences (P < 0.05) were observed for any of the survey questions when comparing responses for the face-to-face and telemedicine encounters. In general, patients strongly agreed that they were satisfied with the services they received and perceived the services as unique and valuable in both types of encounters. CONCLUSION: Overall satisfaction was very high, and no differences were found in the perception of or satisfaction with services between the telemedicine and face-to-face modalities.
24 Apr 2026
Read appraisal →PloS one
Understanding older adults' perception, acceptance, and adoption of smart home technologies
Smart home technologies have increased in popularity and affordability in recent years, however, there is limited research on their adoption specifically among older adults. This study aims to uncover incentives and barriers to the adoption of smart home components, given the impact of COVID-19 on older adults' quality of life (QoL). For this purpose, online audio-recorded interviews were conducted with 21 participants aged between 65 and 90 years from the UK, Malta, and Pakistan. Participants were shown various smart home technology images and videos and asked for their perception, existing knowledge, and current use. The corresponding audio recordings were transcribed and thematically analysed. Findings revealed that older adults experienced a decline in QoL during COVID-19, accompanied by an increased reliance on digital technologies. While participants recognised the potential of smart home devices to enhance independence and well-being, their knowledge of available options was limited. Key barriers included affordability, privacy and trust concerns, compatibility and integration issues, and environmental and social considerations. Cross-cultural differences emerged, with European participants reporting higher familiarity than those in Pakistan, particularly rural areas where availability was limited. These results highlight the need for designers and policymakers to improve affordability, usability, and cultural adaptability in order to support older adults' acceptance and meaningful adoption of smart home technologies.
23 Apr 2026
Read appraisal →PloS one
An AI-based chatbot to support health-related social needs among pediatric primary care population: Protocol for a pilot randomized controlled trial
Unmet health-related social needs (HRSNs) are major drivers of poor health outcomes in early childhood. Children with unmet HRSNs are at greater risk for developmental delays, caregiver stress, and increased healthcare utilization, yet current screening approaches in pediatric primary care are resource-intensive and inconsistently implemented. AI-powered chatbots (conversational agents or virtual assistants) may offer a private, secure, scalable, and cost-effective alternative for identifying unmet needs and connecting families to services. This protocol describes a pilot randomized controlled trial designed to evaluate the feasibility, acceptability, and usability of DAPHNE, an AI-driven chatbot developed to facilitate the identification of unmet HRSNs and provide personalized community resource referrals. One hundred caregivers of children under two years of age will be recruited from Nationwide Children's Hospital pediatric primary care clinics and randomized to either the standard care (control) group or DAPHNE+ Standard care (intervention) group (n = 50 each arm). Caregivers will complete surveys at baseline, 1 month, 3 months, and 6 months post-intervention (depending on the measure). For the intervention group, participants will receive weekly chatbot prompts and on-demand access throughout the 6-month study period. Primary outcomes include study feasibility (recruitment, retention, and survey completion across both arms), acceptability (caregiver-reported ratings in both arms and intervention-specific ratings), and usability of the DAPHNE chatbot (System Usability Scale among intervention participants). Secondary outcomes includes caregiver-reported outcome measures (caregiver stress, self-efficacy, satisfaction with resource access, quality of life), and electronic health record-derived measures (including documentation of HRSN screening and referrals, adherence to well-child visits, missed appointments, emergency department utilization, and estimated healthcare costs). In addition, ten primary care providers will also participate to assess workflow integration and report on current HRSN practices. Mixed-methods analyses will integrate survey data, chatbot engagement metrics, and qualitative interviews to refine both the intervention and the study protocol. The results of this study will inform the design of a future multi-site trial to evaluate the efficacy and implementation of DAPHNE for addressing HRSNs in pediatric primary care. Trial registration: NCT07168382.
23 Apr 2026
Read appraisal →Journal of health organization and management
Predicting CSR authenticity in healthcare organizations: an integrated SEM-IPMA-ANN approach through the lens of self-compassion theory
PURPOSE: As healthcare business organizations face mounting pressure from civil society to act responsibly, they are expanding and strategically restructuring their CSR activities. Under the umbrella of CSR, cause-related marketing (CRM) campaigns are a means of fundraising available to healthcare firms engaging in socially responsible activities to improve global health. While healthcare organizations increasingly emphasize CSR implementation, its precise nature within the context of CSR authenticity has not been systematically investigated. Adopting self-compassion theory as a platform, this study has demonstrated novel relationships for gaining deeper insights into the key predictors of CSR authenticity in healthcare organizations. DESIGN/METHODOLOGY/APPROACH: The authors collected 882 responses and adopted the hybrid SEM-IPMA-ANN (Structural Equation modelling- Importance Performance Map Analysis- Artificial Neural Network) method to confirm the proposed model's significant predictors influencing CSR authenticity. FINDINGS: The findings provide an extended knowledge of self-compassion theory, thus contributing to the extant literature on CSR domain and healthcare marketing. The ANN findings established that social connectedness was the strongest predictor of CSR authenticity, followed by trust in the CRM campaign, isolation, mindfulness, common humanity, self-kindness, over-identification, and self-judgement. The IPMA result, consistent with ANN findings, revealed that social connectedness and trust in CRM campaigns were of relatively higher importance when predicting CSR authenticity. ORIGINALITY/VALUE: The study contributes to our knowledge, and the findings would have implications for CSR practitioners in developing effective CRM campaigns for healthcare organizations. The conclusions would offer invaluable intelligence to CRM marketers, healthcare service practitioners, ad-campaign business professionals, and non-profit managers to manage critical and informed decisions.
20 Apr 2026
Read appraisal →Journal of health organization and management
Implementing telemedicine for cancer care in European healthcare organizations: lessons from the eCAN Joint Action
PURPOSE: Telemedicine represents a promising innovation to complement conventional cancer care in Europe to improve treatment quality and patient outcomes. Funded between September 2022 and December 2024, the Joint Action "Strengthening eHealth including telemedicine and remote monitoring for health care systems for CANcer prevention and care" (eCAN JA) aimed to bring the benefits of telemedicine to cancer patients across the European Union (EU) Member States, with 35 partner organizations from 16 countries and explored its feasibility through multicenter clinical trials. This study sheds light on key factors to implement telemedicine services for cancer care in European healthcare organizations (HCOs) and offers a forward-looking perspective by conducting a foresight exercise as part of the Sustainability Work Package (WP4) of the eCAN JA. DESIGN/METHODOLOGY/APPROACH: Foresight is an umbrella term for innovative strategic planning, policy formulation and solution design methods that empower decision-makers. For our multi-country foresight study, we followed three sequential steps to gain qualitative and quantitative findings on the facilitators of telemedicine services for cancer patients in clinical settings, comprised of (1) literature review; (2) surveys to HCOs in different EU Member States and (3) a foresight workshop with survey respondents. FINDINGS: Telemedicine implementation in HCOs requires equipping healthcare professionals with the necessary skills, ensuring system compatibility and addressing resource constraints for hybrid care models for success. Future policies in the EU should focus on establishing telemedicine training for healthcare professionals and support interoperability as well as user-friendliness of telemedicine services in HCOs. Policies should also address clinical workload challenges, enable harmonized telemedicine protocols across Europe, provide incentives for implementation and invest in digital infrastructure in HCOs for a sustainable telemedicine adoption. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to conduct a foresight analysis on the feasibility of telemedicine for cancer care across different HCOs in EU Member States.
20 Apr 2026
Read appraisal →BMJ open respiratory research
Comparing paper Letters in addition to Emailed Audit and feedback in Refining Asthma treatment to Improve clinical and environmental Results in primary care through a cluster randomised controlled trial: the CLEAR AIR study
BACKGROUND: Suboptimal use of preventer inhalers and salbutamol reliever overprescribing are associated with preventable asthma deaths and are a major source of primary care carbon emissions. Audit and feedback produces modest behaviour change by assessing clinical performance and delivering feedback to encourage improvement. Although feedback is increasingly delivered digitally, clinicians may respond more to additional printed feedback reports. We evaluated whether combined digital and paper feedback was more effective than digital-only feedback in promoting safer and greener asthma prescribing at the practice level. METHODS: In this parallel, cluster randomised controlled trial, all 273 primary care practices in West Yorkshire were assigned within their primary care network clusters by stratified, permuted block randomisation to receive seven bimonthly reports on asthma prescribing either in 'digital and paper' (intervention) or 'digital-only' (control) formats. The primary outcome was the proportion of preventer inhalers prescribed in pressurised metred-dose devices due to their high carbon footprint. Intervention group allocation was concealed. The intention-to-treat population was analysed and adjusted for both potential confounders and preintervention achievement. RESULTS: Final analysis assessed 270 practices in 26 clusters per arm due to practice mergers within the control group. There was no significant difference between the intervention groups based on change in the primary outcome (intervention-0.15%; control-0.19%; risk ratio-1.00; 95% CI 0.98 to 1.03) nor any secondary outcome. Analysis of both interventions combined showed a background trend of mixed improvement following feedback. CONCLUSIONS: There was no evidence that combined paper and digital feedback was more effective than digital-only feedback, despite the background of mixed improvements following both interventions. Challenges remain to understanding the barriers to influencing the prescribing of preventer inhalers and transitioning inhaler devices towards low-carbon 'green' alternatives; however, this study demonstrated the value of an efficient 'real-world' trial embedded within an existing quality improvement initiative. TRIAL REGISTRATION NUMBER: NCT05761873.
20 Apr 2026
Read appraisal →BMJ open
Report cards and outcome measurements to improve the safety of surgical care (North America): an updated systematic review from Making Healthcare Safer IV
OBJECTIVES: In the USA, an estimated 40-50 million operations are performed annually, with high rates of adverse events. Since the 1980s, report cards have been used for outcome measures and to improve safety of surgical care. As part of Making Healthcare Safer IV-an initiative aimed at publishing evidence-based reviews as they are completed to help healthcare leaders, researchers and policymakers act more quickly on evidence-supported practices-we performed an updated review on the certainty of evidence on patient safety practices related to the use of surgical report cards and outcome measurements. DESIGN: Systematic review using the Grade of Recommendations Assessment, Development and Evaluation (GRADE) approach. DATA SOURCES: PubMed, Web of Science, Scopus and the Cochrane Library were searched from November 2011 to May 2023. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included primary research studies (randomised control trials or observational studies with a comparison group, including pre-post studies) or observational studies that investigated a surgical report card in adult or paediatric surgical patients at the hospital or surgeon level in inpatient or outpatient settings. Excluded studies included: narrative reviews, scoping reviews, editorials, commentaries, abstracts, studies that measured only patient knowledge or levels of engagement or studies using local surgical dashboard data. DATA EXTRACTION AND SYNTHESIS: Screening and eligibility were done in duplicate, while data extraction was done by one reviewer and checked by a second reviewer. Specific items in the Risk Of Bias In Non-randomised Studies - of Interventions tool and a modification of the National Institutes of Health Tool were used to assess for bias in studies. Two reviewers assessed each study for risk of bias. A modified version of the GRADE framework was used to assess the certainty of evidence. RESULTS: We identified 19 studies that met the inclusion criteria: 13 primary research studies and 6 descriptive studies of surgical collaboratives. Of the primary studies, nine used a pre-post or longitudinal study design and four used a regression discontinuity or concurrent controlled design. Seven of the studies were about the American College of Surgeons National Surgical Quality Improvement Project. Five studies were from single institutions and the remainder included nine to greater than 700 hospitals. Pre-post studies of report cards that prompted quality improvement (QI) programmes all reported improvements in outcomes, longitudinal studies reported benefits in some but not all outcomes and one in four controlled before-and-after studies reported a statistically significant mortality benefit. All studies, except for one, were at moderate or high risk of bias. Six collaboratives were identified with preliminary data. CONCLUSIONS: Based on the above evidence, the theoretical rationale and parallel evidence in other settings, we judged that it was moderate certainty that report cards and outcomes measurements can improve surgical outcomes. However, given the evidence from studies where report cards were actively linked to institutional QI initiatives, we recommend that outcome data must be paired with actionable QI efforts to meaningfully improve patient outcomes.
19 Apr 2026
Read appraisal →PloS one
Operationalization of Severe Maternal Morbidity surveillance in administrative databases of the Brazilian public and private health systems: Brazil 2015-2022
The study of severe maternal morbidity (SMM) is an important strategy for improving the quality of obstetric care. The objective of this study is to assess SMM cases recorded in the Hospital Information System of the Unified Health System (SIH/SUS), which contains public funded hospitalizations, and in the database of the National Supplementary Health Agency (ANS), which contains hospitalizations paid for by health plans. This is a cross-sectional study using obstetric hospitalizations of women aged 10-49 years from both databases in the period 2015-2022. We assessed SMM cases using the World Health Organization's criteria for potentially life-threatening conditions (PLTC). Of the 26 PLTC criteria, 22 were operationalized through diagnoses during hospitalization, using International Classification of Diseases-10 codes, or medical procedures, using specific codes of each database. We estimated the frequency and odds ratio of death for each isolated criterion, for grouped criteria and for the total number of SMM cases, as well as the frequency of deaths according to the number of SMM criteria. We identified 18,807,757 obstetric hospitalizations in the SIH/SUS and 3,776,986 in the ANS database. The estimated proportion of SMM was 8.51% and 7.30% in SIH/SUS and ANS, respectively. Most cases presented only one criterion. Differences were observed among sectors, with a higher frequency of procedure-based criteria in the ANS database, compatible with the greater availability of resources in private services. The proportion of deaths increased with the increase in the number of criteria, and more severe criteria presented a higher odds ratio of death. The results indicate that it is possible to estimate the number of SMM cases in the country, both in public and private hospitalizations. Continued investment in the quality of recording in both databases are necessary so that the estimates can truly reflect the maternal morbidity of Brazilian women.
18 Apr 2026
Read appraisal →Hernia : the journal of hernias and abdominal wall surgery
The impact of socioeconomic status in hernia treatment: a qualitative systematic review.
INTRODUCTION: Hernias are among the most common surgical conditions worldwide. However, access to optimal treatment remains disproportionately distributed. Socioeconomic disparities play a critical role in determining whether patients receive timely surgical intervention, access to minimally invasive techniques, or suffer from postoperative complications due to delayed or suboptimal care. This study aims to evaluate the impact of socioeconomic disparities on the treatment of ventral hernia repair. METHODS: This systematic review was conducted following PRISMA guidelines. A comprehensive search was conducted using MEDLINE/Pubmed, EMBASE, Web of Science, Cochrane Library, and LILACS, from inception until March 2026 without any filter applied. A search strategy was created using the MeSH terms. Our inclusion criteria comprise studies related to socioeconomic disparities in ventral hernia repair within the United States. Due to heterogeneity in study designs, socioeconomic variables, and outcome definitions, a meta-analysis was not feasible, and findings were synthesized using a narrative synthesis. A qualitative assessment of included studies was made using the Cochrane Risk of Bias tool, ROBINS-I. RESULTS: A total of 28 studies were included, encompassing 2,096,513 patients with ventral hernia. Most were retrospective cohorts, with sample sizes ranging from a few hundred to over 665,000 patients. Across studies, patients with government funded insurance (Medicaid or Medicare) generally experienced higher rates of complications, readmissions, and recurrences compared to commercial insured patients, although the magnitude of association varied. Odds ratios demonstrated increased risks of wound complications, readmission, and recurrence among government funded insured and uninsured patients, while commercial insured and higher-income patients were more likely to undergo advanced surgical approaches and had shorter hospital length of stay. Some studies also highlighted socioeconomic and racial disparities, with vulnerable populations and minority groups showing disproportionately higher complication rates and different patterns of insurance coverage. CONCLUSION: This systematic study demonstrates significant racial and socioeconomic disparities in ventral hernia repair. Government funded insurance holders or lower incomers, faced higher complication rates, more emergent presentations, and worse outcomes, including increased mortality and readmissions. Moreover, they usually have less access to minimally invasive and robotic techniques. These inequities highlight systemic barriers in healthcare access.
18 Apr 2026
Read appraisal →Journal of medical Internet research
Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research
Secondary use of clinical data offers unprecedented opportunities to rapidly conduct large-scale research and improve patient care. However, incomplete understanding of data quality requirements for a study often causes significant delays in executing analyses and validating results. Current practice has largely followed 2 paths. First, multi-institutional networks have developed general data quality programs, but these are typically tied to unique network characteristics and do not address study-specific requirements well. Second, models have been proposed to formalize the requirements for data fitness analyses without extending to the methods needed to meet these requirements. More recently, tools have been developed to conduct cohort-centric screening, focusing on generally applicable structural checks such as missingness or facial implausibility. These provide a first level of information but incompletely capture the fitness requirements of an analysis. In turn, investigators conduct per-study exploratory analyses, but these efforts are typically ad hoc and partially reported, which can hinder reproducible science and delay advances in patient care. Analogously to advances over the past decade in data modeling and reproducible analytics, there is a need for a more systematic, capable approach to study-specific data quality assessment (SSDQA). We discuss such a model, which guides improved SSDQA design and implementation, including metadata for consistent annotation and reporting of data quality assessment results. The model integrates theoretical principles of data quality testing with pragmatic considerations of application to clinical data, providing a consistent approach to specifying data quality assessment checks. Additionally, it proposes to regularize check application through a standard set of options. The SSDQA model builds on current practice, providing a path toward more complete, sound, and reproducible assessments. These characteristics foster multidisciplinary collaboration to identify data quality issues that, in turn, inform decisions about study design and provide important context that has a bearing on adoption of results.
17 Apr 2026
Read appraisal →BMJ open
Remote assessment and practice implementation for disorders in ears, nose and throat: a mixed-methodology observational study in the United Kingdom
INTRODUCTION: Ear, nose and throat (ENT) conditions are highly prevalent in primary and secondary care, yet patients frequently face prolonged waits for specialist review. In England, over half of ENT patients wait beyond the NHS 18-week referral-to-treatment target. Many of these cases can be effectively managed with advice and non-surgical interventions, presenting an opportunity for remote service innovation.This study aims to evaluate the clinical effectiveness, cost-effectiveness, acceptability and environmental sustainability of a digitally enabled remote ENT clinic model compared with traditional face-to-face pathways. METHODS AND ANALYSIS: This single-centre, mixed-methods, prospective cohort study will be conducted at University Hospitals Birmingham NHS Foundation Trust. Remote clinics will use trained staff to collect diagnostic data (including endoscopic imaging and boothless hearing tests) for consultant review via secure cloud-based software. Quantitative analysis will assess patient outcomes, costs, waiting times, carbon footprint and satisfaction. Qualitative data from semi-structured interviews with patients, clinicians and managers will explore acceptability, scalability and barriers to implementation. The qualitative data will be analysed using the framework methodology, according to the non-adoption, abandonment, scale-up, spread and sustainability framework, while the strengthening the reporting of observational studies in epidemiology framework will be used to guide the reporting of quantitative data. Cost-effectiveness analyses will follow NICE guidelines, while environmental impact will be measurement will be informed by the sustainability in quality improvement framework. Recruitment will be aiming for 300 completed datasets and 30-35 interviews. ETHICS AND DISSEMINATION: Ethical approval has been granted (IRAS 350908; REC 25/SW/0116). Findings will be disseminated via conferences, peer-reviewed journals and institutional communication channels.
17 Apr 2026
Read appraisal →JMIR formative research
Development of Virtual Mental Health Stepped Care Service for a Heart Failure Remote Management Program: Qualitative Descriptive Study
BACKGROUND: Depression is highly prevalent yet undertreated among people living with heart failure, indicating barriers to mental health services. Although various digital mental health interventions have been developed to detect, treat, and manage depression in this population, these interventions have seen limited integration into clinical care and a lack of implementation research. Stepped care is a service innovation that may promote the implementation of these technologies into clinical settings, but few studies have examined how these services are designed in clinical settings. OBJECTIVE: This study aimed to identify strategies to address health system barriers to accessing mental health care from the perspective of people living with heart failure, clinicians, and researchers, and to incorporate these strategies into the design of a virtual mental health stepped care service within a heart failure remote management program. METHODS: A qualitative description study was conducted using purposive recruitment of people living with heart failure, clinicians, and researchers from a heart failure remote patient management program. As part of a service design approach, semistructured interviews explored potential strategies to address barriers to accessing mental health services. Two researchers coded the data descriptively and constructed themes to guide the development of a virtual stepped care service. RESULTS: A total of 22 participants were interviewed, comprising 13 people living with heart failure and 9 clinicians and researchers. Six themes were identified, comprising 4 requirements and 2 foundational principles. The requirements were to (1) adopt a collective approach to identify distress across methods, people, and time points; (2) maintain a referral-based approach; (3) rely on existing mental health human resources; and (4) offer patient choice among various mental health care options. These requirements were supported by two principles: (1) building on organizational strengths and (2) reducing treatment burden. Based on these findings, a virtual stepped care service was developed, incorporating a depression screening module, referral-based workflows, and, where clinically appropriate, patient choice in treatment selection. CONCLUSIONS: The stakeholder-informed design of this virtual stepped care service contributes to the limited literature on stepped care service design and demonstrates how such models can be tailored to their intended contexts. Although each component was designed to address health system barriers to mental health care for people living with heart failure, resource limitations may constrain the balance between feasibility and quality of care. Future research should evaluate the acceptability of this model among people living with heart failure and clinicians.
16 Apr 2026
Read appraisal →JMIR research protocols
Integrating Gait Analysis and AI Into Knee Osteoarthritis Care: Protocol for a 3-Phase Participatory Qualitative Study
BACKGROUND: Knee osteoarthritis (OA) leads to pain, disability, and reduced quality of life. For advanced stages, knee arthroplasty surgery is the standard treatment, yet dissatisfaction and persistent mobility deficits remain common. Current surgical decision-making processes seldom incorporate objective predictors of outcomes, such as biomechanical data. Advances in computer vision, wearable sensors, and artificial intelligence now enable efficient capture and interpretation of clinically prognostic gait features in real-world settings. However, the clinical adoption of such innovations remains limited, hindered by usability challenges, misalignment with stakeholder needs, and system-level barriers. OBJECTIVE: This study aims to inform the development of a knee OA clinical decision support (CDS) tool that integrates gait analysis and artificial intelligence with clinical decision-making. More broadly, it seeks to advance a framework for participatory digital health CDS tool implementation by identifying and addressing suboptimal workflows, stakeholder priorities, and organizational challenges. METHODS: This study uses a 3-phase participatory design process, with in-depth interviews conducted in each phase to generate insights that inform subsequent stages. Phase 1 investigates perceived barriers to and facilitators of the implementation of a digital CDS tool and examines current workflow processes in knee OA management. Phase 2 focuses on identifying and defining user requirements to guide solution ideation for translational digital decision support. Phase 3 assesses user acceptance of prototyped solutions. Field observations in phase 1 and usability testing in phase 3 will supplement interview findings with real-world evidence. RESULTS: The study was funded in November 2024. Ethics approval was granted by the Nova Scotia Health Research Ethics Board on July 28, 2025. Data collection began in September 2025. As of February 2026, a total of 6 clinicians have been interviewed, with all 6 interviews transcribed and 4 coded as part of phase 1 analysis. Study completion is anticipated by August 2026. Findings will be published by January 2027. CONCLUSIONS: Our stakeholder-driven approach prioritizes both technological rigor and practical usability, addressing key human factors for successful implementation. While initially focused on Nova Scotia's orthopedic setting, this research provides a scalable framework for integrating digital decision support tools into broader clinical environments, advancing innovation in knee OA care and beyond.
16 Apr 2026
Read appraisal →Journal of medical Internet research
A Health-Related Digital Ecological Momentary Assessment in Children (Aged 5-11 Years): Systematic Review
BACKGROUND: Digital ecological momentary assessment (EMA) collects data on experiences as they occur in daily life, capturing dynamic, context-sensitive experiences often missed by retrospective reporting. While EMA shows promise for pediatric health research, preadolescents have distinct socioemotional and cognitive characteristics likely to affect engagement. Existing reviews have not focused on the acceptability and feasibility of EMA protocols for this age group. OBJECTIVE: This review aimed to examine digital EMA protocols used with children aged 5-11 years across health domains, focusing on protocol characteristics, acceptability, and feasibility. We address 3 research questions (RQs)-RQ1: What are the characteristics of these protocols? RQ2: What is the feasibility and acceptability of these protocols? RQ3: What are the characteristics of high and low response rate protocols? METHODS: We searched 10 databases (CINAHL, Embase, ACM Digital Library, IEEE Xplore, Cochrane Library, PsycINFO, Web of Science, PubMed, Scopus, and MEDLINE) for peer-reviewed studies published up to October 2025. Eligible studies used EMA with children aged 5-11 years to collect health data via digital devices. Two researchers independently screened studies (SC and LT); one (SC) conducted quality assessment and data extraction. Findings were narratively synthesized. RESULTS: We identified 17 protocols across 37 studies. Most targeted nonclinical populations, used handheld devices, spanned 3-28 days, and applied interval-contingent prompting (RQ1). Response rates were available or calculable for 15 of 17 protocols, ranging from 48% to 92% (RQ2). Six protocols reported response rates of ≥80%. However, key data required for pooling (eg, raw counts for planned vs completed prompts) were missing or selectively reported. This contributed to 13 of 17 protocols being rated at critical risk of bias (ROBINS-I, v2). As a result, the strength of evidence was limited by poor reporting and high risk of bias. Facilitators included uncomplicated, engaging technology, reminders, and caregiver involvement. Barriers included device burden, restricted device access, difficulty with accurate reporting, stigma, limited device awareness, and insufficient caregiver support. High-response protocols (≥80%) often involved older children or clinical groups, ≥3-week duration, fixed schedules (≥20 items per prompt, 3 or 4 times per day), timing customization, and incentives (RQ3). CONCLUSIONS: This review provides the first systematic synthesis on preadolescents, offering insight into EMA protocol design beyond prior work treating children as a single group. By examining 17 EMA protocols, the review identifies gaps in developmental appropriateness and reporting quality, highlighting where the evidence may differ from adolescent and adult EMA research. The results suggest that digital EMA for preadolescents requires greater focus on child-centered design to increase acceptability and adherence, alongside improved reporting standards, so protocols can be meaningfully compared. With these advances, EMA could be more effectively integrated into pediatric health monitoring, tailored to the needs of different age groups.
16 Apr 2026
Read appraisal →International journal of medical informatics
A structured decision-support framework for selecting imputation methods in clinical structured datasets: A secondary analysis
OBJECTIVE: Missing values are a common challenge in healthcare data analysis, and inadequate handling can introduce bias and undermine the validity of findings. Imputation methods offer a practical solution, but selecting an appropriate approach depends on multiple dataset-specific factors. This study proposes a structured decision-support framework that defines key prerequisites for choosing suitable imputation methods during the preprocessing of clinically structured datasets. METHODS: A secondary analysis of a previous systematic review was conducted, covering 69 studies to identify factors influencing imputation method selection. Domain experts evaluated assumptions regarding missing data characteristics and dataset structure, reaching consensus on the most relevant factors. These factors were synthesized into a structured framework designed to guide systematic and transparent imputation method selection in clinical data preprocessing workflows. RESULTS: Nine key factors were identified as essential for determining an appropriate imputation method. These include missing data characteristics, mechanism, pattern, and ratio and dataset attributes such as data type, variable role, distribution, and correlation. The ratio of missingness was the most influential factor, followed by variable role and missing value mechanism. Most studies emphasized the combined importance of both missing data properties and dataset features in imputation selection. CONCLUSIONS: Understanding the characteristics of missing values and dataset structure is crucial for selecting appropriate imputation methods. The proposed structured decision-support framework provides an evidence-based checklist to enhance transparency, reproducibility, and reliability in preprocessing clinical datasets within medical informatics workflows.
16 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 formative research
Comparing Large Language Models and Traditional Machine Translation Tools for Translating Medical Consultation Summaries: Quantitative Pilot Feasibility Study
BACKGROUND: Translation of medical consultation summaries is essential for equitable health care communication in culturally and linguistically diverse populations. While machine translation (MT) tools and large language models (LLMs) are widely accessible, their feasibility and safety for health care contexts remain underexplored. OBJECTIVE: This pilot study investigates the feasibility and limitations of using LLMs and traditional MT tools to translate medical consultation summaries from English into the most common languages other than English spoken in Australia-Arabic, Chinese (simplified written form), and Vietnamese. METHODS: Two simulated summaries-a simple patient-facing summary and a complex clinician-oriented interprofessional letter-were translated using 3 LLMs (GPT-4o, Llama-3.1, and Gemma-2) and 3 MT tools (Google Translate, Microsoft Bing Translator, and DeepL). Translations were benchmarked against professional third-party interpreter translations using Bilingual Evaluation Understudy, Character-level F-score, and Metric for Evaluation of Translation with Explicit Ordering metrics. RESULTS: The translation performance varied across languages, tools, and summary complexity when assessed using automatic evaluation metrics. Traditional MT tools outperformed LLMs on surface-level metrics, while LLMs showed relative strengths in semantic similarity for Vietnamese and Chinese. Arabic translations improved with complex input, suggesting morphological advantages. The metric-based evaluation highlighted feasibility but also risks, particularly in Chinese clinical contexts. CONCLUSIONS: This pilot study provides formative evidence of opportunities and limitations in applying artificial intelligence translation for health care communication. Findings underscore the importance of human oversight; domain-specific evaluation metrics; and further formative and clinical research to guide the safe, equitable use of artificial intelligence translation tools.
14 Apr 2026
Read appraisal →JMIR cancer
Leveraging Innovative Electronic Health Record Data to Characterize Social Determinants of Health Among Survivors of Cancer in Persistent Poverty Areas: Cross-Sectional Study
BACKGROUND: Residents in persistent poverty areas experience higher cancer mortality due to social determinants of health that negatively affect multiple factors, including health behaviors. OBJECTIVE: This study aimed to characterize demographic, clinical, and social determinant of health factors among survivors of cancer in persistent poverty areas using electronic health record (EHR) data-including an embedded social risk screener and natural language processing (NLP) of social work notes-to inform community-engaged adaptation of lifestyle interventions. METHODS: EHR data from a large multispecialty group practice were extracted for patients with cancer residing in zip codes inclusive of persistent poverty areas targeted for a health behavior intervention and receiving care between January 2018 and November 2023. Self-reported social determinant of health data were obtained using the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE) and through NLP of social histories from a social work visit. RESULTS: We identified 2672 unique patients with cancer, of whom 578 (21.6%) had PRAPARE data and 1597 (59.8%) had social history data available for analysis. The most common cancers among survivors (n=1420, 53.1% female; n=1299, 48.6% Black individuals; mean age 65.2, SD 13.7 years) included breast (n=536, 20.1%), prostate (n=400, 15%), and lymphoid or hematopoietic (n=323, 12.1%) cancer. Among survivors in persistent poverty areas (n=509, 19%; all with a high Social Vulnerability Index), 34.6% (176/509) were single, 55.4% (282/509) had Medicare coverage (with only 73/509, 14.3% having private insurance), 36.5% (186/509) had obesity, 63.9% (325/509) had hypertension, and 31.2% (159/509) had diabetes. Of survivors in persistent poverty areas with PRAPARE data, 15.8% (19/120) lacked transportation, 4.2% (5/120) lived with housing insecurity, and 6.7% (8/120) felt unsafe where they lived. CONCLUSIONS: Innovative EHR and NLP approaches identified several socioeconomic and safety-related challenges along with opportunities for health behavior interventions to leverage Medicare coverage and target multiple comorbidities when adapting interventions for survivors of cancer living in persistent poverty areas.
14 Apr 2026
Read appraisal →JMIR medical informatics
Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability
BACKGROUND: Amid growing demands and constrained health care resources, effective hospital bed capacity management is crucial. Delayed hospital discharge, where patients remain in the hospital beyond the need for acute care, strains resources, affects patient outcomes, and reduces system efficiency. Predicting such delays facilitates early interventions to avert them and alleviate burdens on patients, care partners, hospitals, and the broader health care system. OBJECTIVE: This study aimed to develop comprehensive predictive analytics for delayed discharges among older adults using explainable machine learning to boost transparency and interpretability, while integrating fairness to mitigate algorithmic biases. METHODS: Leveraging longitudinal data from over 2 decades in Ontario, Canada, we applied extreme gradient boosting and logistic regression models to predict delayed discharges within 90 days post-acute care. Data preprocessing included a 2-year look-back for clinical histories and balanced sampling to address class imbalance. Model performance was assessed via area under the receiver operating characteristic curve, calibration, and clinical utility. Fairness was evaluated across sex, urban or rural residence, and residential instability using several threshold-free metrics. Explainability was examined at the global model level (via partial dependence plots and permutation feature importance) and locally (via Shapley Additive Explanations, breakdown, and ceteris paribus methods), with principal component analysis used to cluster key features for high-risk patients. RESULTS: The extreme gradient boosting model outperformed logistic regression, achieving an area under the receiver operating characteristic curve of 0.82 on the test set, with acceptable within-group and cross-group ranking fairness across subgroups. Explainability clustering analyses identified functional and cognitive declines (eg, care support needs, dementia, and mobility issues) and regional disparities as primary drivers of high-risk predictions. Bias mitigation improved calibration parity, especially when stratifying by residential instability, underscoring the trade-offs policymakers must weigh between accuracy, fairness, and explainability. CONCLUSIONS: This study demonstrates the potential of responsible artificial intelligence in health care, emphasizing the need to balance predictive accuracy, equity, and interpretability. It uncovers systemic gaps and offers actionable insights for enhanced discharge planning, resource optimization, and equitable care delivery.
14 Apr 2026
Read appraisal →European journal of clinical pharmacology
Clozapine prescribing in treatment-resistant schizophrenia - an updated systematic literature review of barriers and facilitators among clinicians
BACKGROUND: Increased rates of clozapine prescribing are essential to improving timely patient access within treatment-resistant schizophrenia (TRS) management. The extent of geographical variation in its use suggests it is possible to develop interventions to increase clinician engagement. To inform intervention development, a contemporary review of barriers and facilitators to increased clozapine prescribing is required. We aimed to conduct a systematic review of research addressing barriers and facilitators to clozapine prescribing among clinicians within TRS management. METHODS: The review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Embase, CINAHL, and PsycINFO were searched from inception to July 2025. Results were synthesized qualitatively. RESULTS: Fifteen studies were included. Barriers related to clinicians, patients and carers, and healthcare institutions. Primary clinician-related barriers included insufficient knowledge of, and confidence in, managing clozapine treatment and the associated administrative burden. Primary patient-related barriers included concerns regarding patients' willingness to consistently adhere to clozapine treatment and associated monitoring requirements. A lack of dedicated systems of care to facilitate clozapine initiation and shared community care were the leading institutional barriers. Major facilitators included improved education for clinicians, access to point-of-care testing, and increased availability of dedicated clozapine clinics. CONCLUSION: Most barriers to systematically increasing clozapine prescribing rates are beyond the influence of individual prescribers. Instead, structural interventions focusing on (i) reducing the administrative burden associated with establishing clozapine treatment, (ii) increasing access to standardised training and supervision opportunities, and (iii) providing longitudinal support to clinicians when managing clozapine treatment, are required.
13 Apr 2026
Read appraisal →BMJ paediatrics open
Which healthcare services did children and adolescents use before presentation at specialised outpatient clinics for post-COVID-19 condition? Descriptive findings from the Post-COVID Kids Bavaria study.
OBJECTIVE: Measuring the utilisation of healthcare services in children and adolescents with symptoms suggesting post-COVID-19 condition (PCC) until their presentation at specialised outpatient clinics (SOC). METHODS: 117 patients (aged 4-17 years) with symptoms suggesting PCC were recruited for study participation in SOCs of the comprehensive care network 'Post-COVID Kids Bavaria' for children and adolescents with PCC in Bavaria, Germany. Information on healthcare utilisation from their initial SARS-CoV-2 infection until presentation at an SOC was collected via telephone interviews with parents. RESULTS: Participants had a median of 6.5 physician contacts (Q1-Q3: 3.8-11.5), mainly in person (99.1%, n=116) and rarely used telemedicine (21.4%, n=25). Contacts were mainly with paediatricians (72.6%, n=85), followed by general practitioners (46.2%, n=50). Non-pharmacological measures were used by 56 participants (47.9 %), with therapies by Heilpraktiker and osteopaths being used most commonly (30.8%, n=36). 76 participants (65.0%) were prescribed medications, and 71 (60.7%) used non-prescribed medications or supplements. CONCLUSIONS: Children and adolescents with PCC symptoms frequently consult healthcare services, often self-initiated measures provided by Heilpraktiker and osteopaths, until their presentation at an SOC. This may be due to unclear care pathways. Paediatricians and general practitioners are key providers, making their expertise and strong connections with SOCs essential for early diagnosis and appropriate treatment.
12 Apr 2026
Read appraisal →BMJ open respiratory research
What is the evidence for virtual wards or hospital-at-home care pathways for exacerbations of chronic obstructive pulmonary disease? A systematic review and meta-analysis.
OBJECTIVES: Given increasing interest in admission avoidance, we evaluated the evidence to support virtual wards (VW) and hospital at home (HaH) models of care during exacerbations of chronic obstructive pulmonary disease (ECOPD). DESIGN: A systematic review and meta-analysis. A comprehensive search of MEDLINE (1946 to March 2024), Embase (1974 to March 2024) and CENTRAL (searched 22 March 2024) was conducted. Risk of bias and a random effects meta-analysis were performed. POPULATION: Adults with an ECOPD presenting to the hospital or who require hospital-led care. INTERVENTIONS: VW: defined as assessments and interventions delivered remotely or HaH (defined as assessments and interventions delivered by healthcare professionals in patient's homes) care pathways, compared with hospital admission. PRIMARY AND SECONDARY OBJECTIVES: Safety (mortality rate of all causes, in-patient, 7 days and 30 days) and readmission rate in 7 and 30 days. Length of stay in hospital and changes in pulmonary function tests. RESULTS: One study assessed VWs (reported in two publications) and 10 assessed HaH. There were no changes in survival or short-term readmission rates attributable to the interventions and no evidence that VW or HaH care pathways reduced the total time a patient spent under hospital-led care, whether at home or in the hospital. CONCLUSIONS: More evidence is needed to support the widespread roll-out of HaH and especially VW pathways for ECOPD. PROSPERO REGISTRATION NUMBER: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024517565.
12 Apr 2026
Read appraisal →PloS one
Exploring barriers and enablers to diabetes self-care practice in Ethiopia, 2025: A qualitative systematic review
INTRODUCTION: Diabetes mellitus is a growing public health concern in Ethiopia, with increasing prevalence and a high proportion of undiagnosed cases. Effective self-care is crucial for managing diabetes; however, many patients face challenges ranging from personal beliefs to systemic and sociocultural constraints. Although multiple qualitative studies have explored these challenges, a synthesized, context-specific understanding that incorporates broader social and health system factors remains limited. This review aimed to synthesize qualitative evidence on barriers and facilitators of diabetes self-care practice in Ethiopia. METHODS: A qualitative systematic review was conducted in accordance with PRISMA 2020 and PRISMA-QS guidelines. A comprehensive search of PubMed/MEDLINE, Scopus, CINAHL, Web of Science, and Google Scholar was performed using relevant keywords and Medical Subject Headings. Eligible studies included primary qualitative and mixed-methods studies with extractable qualitative findings that explored diabetes self-care barriers and facilitators among patients, caregivers, or healthcare providers in Ethiopia. Purely quantitative studies, reviews, editorials, and studies conducted outside Ethiopia were excluded. Thematic synthesis was employed to integrate findings, and the GRADE-CERQual approach was used to assess confidence in the review findings. RESULTS: Eleven studies were included, comprising qualitative and mixed-methods designs conducted across diverse urban and semi-urban healthcare settings in Ethiopia. Study participants included adults with type 1 and type 2 diabetes, as well as healthcare professionals and caregivers. Six major barriers to diabetes self-care were identified: health system limitations, individual-level challenges, socioeconomic constraints, behavioral and lifestyle factors, cultural and social norms, and lack of social support. Key facilitators included strong family and social support, health education and counseling, patient motivation and skills, culturally aligned practices, and signs of health system readiness. CONCLUSION AND RECOMMENDATION: Diabetes self-care in Ethiopia is shaped by interconnected individual, cultural, and systemic factors. While substantial barriers persist, important enablers, particularly family involvement and supportive healthcare interactions, offer opportunities for improvement. Interventions should be culturally sensitive, community-centered, and supported by strengthened health systems. Policy efforts should prioritize integration of diabetes care into primary healthcare services, capacity building of healthcare providers, and incorporation of patients' lived experiences into care planning. Prospero registration number CRD420251033692.
11 Apr 2026
Read appraisal →Proceedings. Biological sciences
Statistical structure and the evolution of languages.
Human cultural development is marked by the emergence of new words and ideas, reflecting societal changes. But how does this evolution proceed? We use modern methods in natural language processing (namely, word embeddings) to measure statistical traces of cultural development, providing a testing ground to compare different models as to how this process works. We show that real embeddings of English and 21 other languages exhibit a series of previously unrecognized regularities. Specifically, these are: (i) frequency assortativity, where entities of high popularity cluster near other high-popularity entities; (ii) characteristic clustering velocity profiles due to aggregation into hierarchical structures; (iii) persistent temporal dynamics, where newly created entities appear disproportionately near other recent entries; and (iv) Taylor's law, implying that over time and across empirical semantic space the variance in new entity counts scales as a power of the mean, which helps systematize and quantify large historical fluctuations of neologisms. To explain these facts, we propose a class of generative models (specifically, directed preferential placement) that construct synthetic embeddings exhibiting similar regularities. We show that analogous regularities also occur in other datasets, suggesting that such generating models may shed light on new aspects of language and cultural evolution.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Digital Health Technology Use Among Rehabilitation Professionals in China: Multi-Province Cross-Sectional Survey
BACKGROUND: The rapid expansion of rehabilitation needs in China has intensified pressure on a workforce that remains unevenly distributed. Digital health technologies (DHTs) offer potential to increase service reach and efficiency. However, little is known about how rehabilitation professionals currently gather and document clinical information, nor about their readiness to integrate digital tools into routine practice within China's rapidly digitalizing health system. OBJECTIVE: This study aimed to describe how rehabilitation professionals in China collect subjective and objective clinical information, document patient data in routine practice, and assess their willingness to use DHTs in clinical settings. METHODS: We conducted a multi-province observational cross-sectional survey using a culturally adapted questionnaire based on the World Health Organization Digital Health Interventions framework. The instrument assessed participant characteristics, information collection methods, documentation practices, and willingness to adopt digital functions across rehabilitation activities. Descriptive analyses and subgroup comparisons were performed on 324 complete responses from certified rehabilitation professionals. The multi-province cross-sectional online survey was conducted among licensed rehabilitation professionals in China with internet access. Participants were recruited through professional networks and social media platforms. RESULTS: Respondents were drawn from 20 provincial-level administrative regions across China, including Fujian (n=72), Guangdong (n=77), and Shanxi (n=45), among others, with 82.7% (268/324) employed in public sector rehabilitation services. Traditional methods dominated clinical work. Face-to-face communication was used frequently for subjective assessment by 96.3% (312/324) of respondents, whereas digital channels such as email (22/324, 6.8%) and telephone (47/324, 14.5%) saw limited use. For objective information, visual observation (271/324, 83.7%) and manual measurement tools (195/324, 60.2%) remained the primary approaches, while motion capture technology (45/324, 13.8%) and wearable sensors (13/324, 4%) were rarely used. Documentation practices also relied heavily on analogue formats, with 82.1% (266/324) using handwritten notes and 60.2% (195/324) using paper templates. In contrast, willingness to adopt DHTs was consistently high, with 80.6% (261/324) of respondents indicating readiness to use digital systems for identity verification, 79.0% (256/324) for progress tracking, and 78.1% (253/324) for outcome measurement. Subgroup analyses revealed that educational level significantly influenced the adoption of advanced technologies, with master's or doctoral degree holders reporting higher use of sensor-based assessment, motion capture, and wearable devices. In contrast, professional title and clinical specialty showed limited influence, with no significant differences observed for most digital health functions. CONCLUSIONS: Rehabilitation professionals in China demonstrate strong readiness to use DHTs, yet their routine practice remains largely paper-based and analogue. These findings provide evidence to inform implementation strategies, workforce training, and system-level planning aimed at accelerating digital transformation in rehabilitation services.
11 Apr 2026
Read appraisal →Acta orthopaedica
Effect of an in-consult patient decision aid on decisional quality and involvement for patients with severe hip or knee osteoarthritis: a multicenter, cluster randomized controlled trial
BACKGROUND AND PURPOSE: Shared decision-making (SDM) is important in managing patients with severe hip and knee osteoarthritis, where treatment decisions often depend on patient preferences and values. We aimed to evaluate the effectiveness of an in-consult patient decision aid in improving decision quality and patient involvement compared with usual care. METHODS: In a cluster-randomized controlled trial, surgeons from 2 Danish orthopedic departments were randomized to use in-consult patient decision aids or provide usual care. The primary outcome was decision quality assessed using the Hip/Knee OA Decision Quality Instrument (HK-DQI). Patients who were well informed and received their preferred treatment received an Informed Patient-Centered (IPC) score of 1, indicating good decisional quality; all others scored 0. Secondary outcomes included patient-experienced engagement and involvement in decision-making, assessed using CollaboRATE and the decision-process domain of the Hip/Knee OA Decision Quality Instrument and consultation duration. Relative risks (RR) and mean differences (MD) with 95% confidence intervals (CIs) were estimated using multilevel regression models. RESULTS: 20 surgeons were randomized to either use in-consult patient decision aids (n = 10) or provide usual care (n = 10). Of the 1,038 patients screened for severe hip or knee osteoarthritis, 581 were included in the study. Decision quality was similar between groups, with a score of 43 (36-51) in the intervention group and 44 (37-52) in the usual care group (RR 0.9, CI 0.7-1.2; P = 0.8), likewise, patient-experienced engagement assessed with CollaboRATE (RR 1.1, CI 0.8-1.4; P = 0.6). Consultations were longer in the intervention group, with a mean difference of 3.9 minutes (CI 0.1-7.7; P = 0.04). CONCLUSION: Decision quality and patient-reported engagement were similar between groups, while consultation time was longer with in-consult patient decision aids use. In the absence of prespecified minimal important differences, the clinical relevance of these findings remains uncertain.
10 Apr 2026
Read appraisal →PloS one
A decade of triage with the Manchester Triage System-The MTS big data study
BACKGROUND: Over the past 25 years, numerous studies have evaluated triage system performance, but longitudinal data are limited. This study is the first to assess the Manchester Triage System (MTS) over a decade to determine the accuracy of initial patient assessments and system stability under external influences such as staff turnover, health policy changes, demographic shifts, and the COVID-19 pandemic. METHODS: We conducted a large-scale, monocentric, retrospective observational study at a tertiary care hospital. RESULTS: Between 2014 and 2023 a total of 355,135 emergency patients were included. Average age increased from 48.1 to 52.1 years. The distribution of the five MTS urgency levels remained stable. Prediction of hospital admission (AUC 0.719-0.756) and ICU admission (AUC 0.831-0.862) was consistent throughout the period. Twelve-hour survival probabilities were above 0.99 for "blue" to "orange" patients and above 0.89 for "red" patients. Short-term mortality prediction (AUC) ranged from 0.845 to 0.894. Thirty-day survival remained >0.8 for "blue" to "orange" stages, while the "red" stage declined from 0.63 (2014) to 0.44 (2023). The AUC for 30-day mortality increased from 0.672 to 0.750 over the decade. CONCLUSION: Construct validity assessment demonstrates that the MTS reliably assigns appropriate urgency levels to incoming emergency patients. Longitudinal evaluation shows that its accuracy remained stable over ten years despite high staff turnover, health policy and demographic changes, and the COVID-19 pandemic. These findings confirm the robustness and reliability of the MTS in a high-volume tertiary care setting and support its continued use for emergency patient prioritization.
10 Apr 2026
Read appraisal →The New England journal of medicine
Multifaceted Strategies for Hypertension Control in Low-Income Patients
BACKGROUND: Uncontrolled hypertension disproportionately affects populations that have substantial health disparities. Data regarding the effectiveness and implementation of multifaceted, team-based strategies for hypertension control among low-income patients are lacking. METHODS: We randomly assigned federally qualified health center clinics in Louisiana and Mississippi to use either a multifaceted implementation strategy (intervention group) or enhanced usual care (control group) for hypertension control. The intervention included team-based care, protocol-based intensive blood-pressure management, blood-pressure audit and feedback, health coaching on lifestyle changes and medication adherence, and home blood-pressure monitoring. Enhanced usual care involved educating physicians about clinical guidelines for hypertension. The primary effectiveness outcome was the mean change in systolic blood pressure from baseline to 18 months. The primary implementation outcome was the adherence summary score (on a scale of 0 to 4, with higher scores indicating better adherence to blood-pressure management). RESULTS: A total of 36 clinics underwent randomization. Among these clinics, we enrolled 1272 patients with uncontrolled hypertension who were 40 years of age or older; 642 were in the intervention group and 630 were in the control group. The mean age of the patients was 58.8 years, 56.7% were women, 63.4% were Black, 75.9% were unemployed, and 73.4% had a family income of less than $25,000 per year. At 18 months, the mean change from baseline in the systolic blood pressure was -15.5 mm Hg (95% confidence interval [CI], -17.4 to -13.6) in the intervention group and -9.1 mm Hg (95% CI, -11.0 to -7.2) in the control group (between-group difference, -6.4 mm Hg; 95% CI, -9.0 to -3.8; P<0.001). The mean adherence summary score over the 18-month follow-up period was 2.8 (95% CI, 2.7 to 2.9) in the intervention group and 2.1 (95% CI, 2.0 to 2.2) in the control group (between-group difference, 0.7 points; 95% CI, 0.6 to 0.8; P<0.001). Serious adverse events occurred in 20.9% of the patients in the intervention group and in 21.7% of those in the control group. CONCLUSIONS: Among low-income patients with hypertension, a multifaceted, team-based implementation strategy resulted in a significantly greater reduction in systolic blood pressure than enhanced usual care. (Funded by the National Heart, Lung, and Blood Institute and others; IMPACTS-BP ClinicalTrials.gov number, NCT03483662.).
9 Apr 2026
Read appraisal →BMJ open
Evaluation of equity in quality improvement for multiarterial grafting in females and patients with low socioeconomic status: retrospective analysis of a statewide collaborative database
OBJECTIVES: Quality collaboratives improve quality of care at the hospital and collaborative levels, but less is understood about how such efforts affect patient-level disparities. This study evaluated how a quality improvement (QI) effort (increasing multiarterial grafting during coronary artery bypass grafting (CABG)) translated into populations which historically receive lower-quality care (females and patients of low socioeconomic status). DESIGN: Retrospective cohort study. SETTING: All non-federal hospitals in the state of Michigan that perform cardiac surgery and participate in a statewide collaborative database (n=33). PARTICIPANTS: Patients undergoing first-time, isolated CABG receiving at least two bypass grafts from 2011 to 2022 were identified. PRIMARY ENDPOINTS: Association of sex and socioeconomic status with multiarterial grafting was evaluated across the study period. The distressed community index (DCI), a socioeconomic ranking (0-not distressed, 100-severely distressed), was matched to the patient's zip code. Hierarchical regression modelling was performed to associate DCI and sex with multiarterial grafting, incorporating patient factors and hospital and surgeon effects. A sex-surgery year and DCI-surgery year interaction term was performed to assess the change in the rate of multiarterial grafting. RESULTS: A total of 40 322 patients underwent CABG at 33 centres with a median age of 66 years and 24% were female. The rate of multiarterial grafting was 15%, although lower among females (10% vs 17%) and the highest (vs lowest) DCI quartile (14% vs 18%). After risk adjustment, females were less likely to receive multiarterial grafting (ORadj 0.51 (95% CI 0.45 to 0.58), p<0.001). Patients living in zip codes with a higher DCI had lower adjusted odds of receiving multiarterial grafting (ORadj 0.35 per 10-point increase (95% CI 0.24 to 0.51), p<0.001), a finding that was independent of study time (sex-year and DCI-year interaction terms p>0.05). CONCLUSIONS: Despite a large overall increase in multiarterial grafting due to QI efforts, females and patients with low socioeconomic status had lower rates of multiarterial grafting. QI efforts should be evaluated both overall and among patients who historically receive lower quality care to improve quality and equity.
9 Apr 2026
Read appraisal →Journal of the American Heart Association
Beyond Composite Indices: Comprehensive Social Determinants Improve Heart Failure Readmission Prediction
BACKGROUND: Heart failure (HF) hospitalization readmissions are associated with a high mortality rate and strain the health care system. Both clinical factors and social determinants of health (SDOH) predict HF readmissions, but the optimal approach to incorporating area-level SDOH data remains unclear. METHODS: We merged census tract- and county-level SDOH measures with electronic health record data in a retrospective cohort of 33 579 Black and White patients with HF (Emory Healthcare, 2010-2018). Six combinations of electronic health record data with 752 area-level SDOH were evaluated using multiple machine learning models (logistic regression, random forest, XGBoost) to predict 30-day HF readmission. Models were assessed for predictive performance using area under the receiver operating characteristic curve and algorithmic fairness across race using the equalized odds ratio. RESULTS: Expanded SDOH predictor sets improved predictive performance and algorithmic fairness compared with traditional SDOH indices. The XGBoost model using expanded SDOH alongside clinical predictors provided better predictive performance (area under the receiver operating characteristic curve, 0.671) and improved algorithmic fairness across patient race (equalized odds ratio, 0.437) than models with traditional indices (area under the receiver operating characteristic curve, 0.632; equalized odds ratio, 0.329). Feature-importance analysis revealed that specific environmental predictors (housing cost burden, air quality) ranked among top predictors alongside clinical biomarkers. County-level SDOH outperformed census tract-level SDOH and matched prediction performance of clinical predictors alone. CONCLUSIONS: Including hundreds of individual SDOH indicators rather than traditional composite indices improves machine learning models' ability to predict 30-day HF readmissions. While performance gains are modest, inclusion of specific environmental factors may provide greater clinical utility and improved equity across racial groups than composite SDOH measures for guiding further research and preventive interventions.
8 Apr 2026
Read appraisal →BMJ open quality
Feasibility and early outcomes of a multi-site TeleHospitalist admissions programme: staff and patient perspectives from a large health system
BACKGROUND: Staffing shortages contribute to admission delays, fragmented care and hospitalist burnout. Although telemedicine has been widely adopted in critical care and subspecialties, its use in general inpatient admissions remains understudied. This study evaluated the feasibility, operational performance and early perceptions of a TeleHospitalist admissions programme designed to address nocturnal staffing shortages in a large academic health system. METHODS: In October 2024, Houston Methodist launched a nocturnal TeleHospitalist programme. The programme provided centralised coverage via audio-video technology, operating from 18:00 to 02:00 initially and expanding to 18:00-06:00. Adult patients admitted from the emergency department (ED) between October 2024 and August 2025 and clinical staff involved in admission workflows, were surveyed. Operational data on admission timeliness were extracted from the Epic electronic medical record, and patient and staff experiences were evaluated through structured surveys and qualitative feedback. RESULTS: Over 10 months, 1575 TeleHospitalist encounters were completed (median age 65 years (IQR 49 to 75); 56% were ≥65 years). Median hospital length of stay was 38.5 hours (IQR 17.3 to 66.3), and median ED arrival-to-admission time was 7.3 hours (IQR 4.7 to 12.8). Among 311 patient respondents (19.7%; median age 66.5 years (IQR 52.3 to 75.6); 5% were ≥65 years), satisfaction scores were high (median 4 (IQR 3 to 5). Staff responses (n=29) demonstrated strong endorsement: 86% favoured continuation, 79% reported comfort with the technology and 75% rated admission processes as better than traditional workflows. CONCLUSIONS: A system-wide TeleHospitalist admissions programme was feasible and well accepted by patients and staff. Older adults reported positive experiences, suggesting age is not a barrier to virtual inpatient care. Early findings indicate improved timeliness, communication and workflow efficiency while mitigating overnight staffing gaps. Future studies will evaluate long-term outcomes, cost-effectiveness and strategies to enhance privacy and patient engagement.
8 Apr 2026
Read appraisal →CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
Behavioural nudges to reduce low-value care for low back pain in the emergency department (NUDG-ED): a 2 × 2 factorial, pragmatic cluster randomized trial
BACKGROUND: Low-value care exposes patients to unnecessary risk and wastes scarce health resources. We aimed to determine if patient- or clinician-directed nudges could reduce low-value care for low back pain in the emergency department. METHODS: We conducted a 2 × 2 factorial, cluster randomized controlled trial involving patients with low back pain presenting to emergency departments. Eight emergency departments were randomized to receive patient nudges (6 electronic information posters discouraging unnecessary imaging and opioids, displayed on 55-inch screens in waiting rooms), clinician nudges (3 electronic health record alerts that provided indications for lumbar spine imaging and suggested alternatives to opioids), both patient and clinician nudges, or no nudges. The primary outcome was the proportion of encounters for low back pain with low-value care, defined as non-indicated lumbar spine imaging test, opioid prescription at discharge, or both. We calculated odds ratios (ORs), adjusted for baseline and clustering. RESULTS: There were 3770 encounters for low back pain during the study period. The overall baseline prevalence of low-value care was 41.6%. During the intervention period, the proportion of encounters with low-value care reduced to 36.4% with patient nudges versus 38.1% without, but the difference was not significant (adjusted OR 0.80, 95% confidence interval [CI] 0.51 to 1.27). The proportion of encounters with low-value care was 39.4% with clinician nudges versus 35.0% without (adjusted OR 1.31, 95% CI 0.84 to 2.05). We did not observe an interaction effect between the interventions (p = 0.4). The patient nudge may have reduced strong beliefs among patients in the value of imaging for low back pain. We found no important differences in secondary outcomes. INTERPRETATION: Nudges - including waiting room information posters targeting patients and electronic health record alerts targeting clinicians - did not reduce low-value care in emergency departments. TRIAL REGISTRATION: www.anzctr.org.au, ACTRN12623001000695.
8 Apr 2026
Read appraisal →JMIR human factors
Factors Influencing User Satisfaction in Accessing Health Data: Cross-Sectional Survey of United Kingdom Adults
BACKGROUND: While patient health records (PHRs) are becoming ubiquitous, nationwide evidence on the drivers of user satisfaction in the United Kingdom remains scarce. OBJECTIVE: This study aimed to quantify determinants of PHR user satisfaction in a nationally representative sample of United Kingdom adults and to contrast real-world experiences with hypothetical expectations among individuals without PHR exposure. METHODS: We conducted a cross-sectional online survey in March 2022 using stratified quota sampling (eg, age, sex, ethnicity) through the Prolific platform. Of 1001 respondents, 533 (53%) were female (mean age 41, SD13) years, 468 (47%) reported previous PHR use ("experienced" cohort), and 533 (53%) respondents did not ("hypothetical" cohort). Primary outcomes were five satisfaction items (5-point Likert), overall PHR evaluation (0-100), and stated/anticipated PHR functions. Two-sample t-tests with unequal variances examined between-group differences; multivariate analysis of variance (MANOVA) assessed demographic equivalence. Significance was set at P<.05. RESULTS: Respondents with PHR experience rated their record easy-to-use in 79% (370/468) of cases versus an anticipated 93% (498/533) among nonusers (P<.001). Experienced users rated appointment-scheduling time-saving at 68% (319/468) compared with 72% (387/533) of nonusers (P=.17). Mean overall evaluation scores were 71.0 (SD 19.5) versus 74.2 (SD 18.6) (t₉₉₇=2.25, P=.02). MANOVA confirmed demographic balance (Wilks Λ=.99, F₇,₉₉₃=1.48, P=.18). Preferred functions across cohorts were viewing personal health information (experience 81%, hypothetical 90%) and lab results (50% vs 76%). Users lacking experience consistently over-estimated future use of carer-related functions (eg, children's data: 36% vs 12%, P<.001). CONCLUSIONS: Adults in the United Kingdom value PHRs primarily for personal data access and scheduling convenience. Nonusers displayed optimistic expectations about carer-related features and breadth of functionality, indicating an information gap. Enhancing transparency about current capabilities and expanding features that facilitate caregiving could increase satisfaction and adoption.
7 Apr 2026
Read appraisal →JMIR medical informatics
Automated ICD-10-Anchored Classification of Primary Care Text Data: Development and Evaluation of a Custom Multilabel Classifier
BACKGROUND: Electronic medical records are a vast and valuable source of information, useful for tasks such as estimating disease prevalence. However, in routine primary care, much of this information is in free-text format rather than in a structured form and, therefore, not readily amenable to analysis. Manual coding of this textual data is both time-consuming and resource-intensive, making it impractical for large datasets. Although powerful open-source language models offer new opportunities for automated coding, their use on short heterogeneous primary care notes, particularly in German-language settings, remains insufficiently studied. OBJECTIVE: By providing hands-on guidance for applied health researchers, this study aims to demonstrate the effective and accurate automatic classification of free-text notes using a language model fine-tuned for automated International Statistical Classification of Diseases, Tenth Revision (ICD-10) coding. METHODS: Building on the extensive Family Medicine Research Using Electronic Medical Records (FIRE) routine database from the Institute of Primary Care at the University Hospital Zurich and the University of Zurich, we trained a large language model-based multilabel classifier on a dataset of 38,728 free-text notes, which had been manually categorized into 47 classes using specific ICD-10 codes and code ranges or nondiagnostic/ad hoc labels (eg, "unclear diagnosis," "status post"). We stratified the labeled data into training (70%), validation (15%), and posttraining test (15%) sets, ensuring similar label distributions across these sets. Using the Transformers Python library, we trained the model over 10 epochs and evaluated it on the posttraining test dataset. RESULTS: Across 48 classes, the FIRE classifier achieved strong performance on the held-out posttraining set, with F1-scores of 0.85 (micro, overall across all predictions), 0.86 (macro, mean of per-class scores treating classes equally), and 0.84 (weighted, per-class scores weighted by class frequency). CONCLUSIONS: This study demonstrates steps for training open-source large language models and highlights the potential to streamline and scale the extraction of diagnostic information for practical applications. Our model can be robustly deployed, for example, for prescreening and labeling of free-text information, thus potentially reducing the burden of repetitive and error-prone manual handling.
7 Apr 2026
Read appraisal →JMIR dermatology
Community-Based Teledermatology for Urgent Suspected Skin Cancer: Health Economic Cost-Comparison and Discrete Event Simulation Study
BACKGROUND: The increasing incidence and financial burden of skin cancer place immense pressure on the UK's National Health Service (NHS). Systemic challenges, including dermatologist shortages and long waiting lists, complicate timely assessment of skin lesions for patients under the urgent suspected cancer pathway. While teledermatology offers an innovative solution compared to traditional face-to-face appointments, standard teledermatology models still face limitations in addressing health care access barriers. Community-based decentralized models may reduce such barriers, but the cost and operational impact of such specific models remain largely underresearched. OBJECTIVE: This study evaluated the differences in financial cost to the NHS and patient waiting times at the Northern Care Alliance NHS Foundation Trust by comparing a community-based teledermatology model using Pathpoint eDerma against the Trust's standard-of-care for patients in the urgent suspected skin cancer pathway. METHODS: This study used an ambidirectional design involving 2 distinct analyses. The cost comparison analysis (CCA) compared costs incurred under the teledermatology model (intervention arm, n=563) against the Trust's standard care, represented by a synthetic comparator arm (n=4011). The discrete event simulation (DES) modeled the operational impact on patient waiting times over a 1-year period. Data for the intervention arm were collected prospectively from December 2022 to May 2023 for CCA and up to November 2023 for DES, while comparator data were collected retrospectively from September 2021 to December 2022. Publicly available resource costs were incorporated to ensure the robustness of the analyses. RESULTS: The community-based teledermatology model was associated with significant improvements in both cost to the NHS and patient waiting times. The CCA revealed a mean cost saving of £45 (£1=US $1.24) per referral (95% CI £22-£60; P<.001). This cost saving was associated with a 26% reduction in the proportion of patients requiring a full diagnostic biopsy, falling from 48% (1925/4011) in standard care to 22% (124/563) in the teledermatology model as well as time savings in face-to-face clinics and administration. Furthermore, the DES demonstrated that, on average, the teledermatology pathways decreased the time to reach a clinical diagnosis by 9.90 (95% CI 9.64-10.16) days; to communicate a diagnosis to patients by 54.18 (95% CI 50.76-57.61) days; and to reach a histopathological diagnosis by 62.8 (95% CI 59.76-65.83) days compared to standard care. CONCLUSIONS: The implementation of the community-based teledermatology model appears to be a highly effective, cost-efficient strategy associated with shortened patient journeys. The intervention showed a faster initial triage phase, but the study identified the histopathology process as the next major systemic constraint that could deter further pathway efficiency. Achieving timely diagnosis for all patients, including those requiring diagnostic biopsies, will necessitate continued strategic investment in innovative technologies to accelerate this downstream process.
7 Apr 2026
Read appraisal →Cancer causes & control : CCC
Comparing endocrine therapy adherence across EHR and claims data by race and ethnicity, language, and insurance.
PURPOSE: We compared adherence measurements across electronic health records (EHRs) and claims data for 79 HR + breast cancer survivors, considering their race, ethnicity, language, and insurance status. METHODS: Adherence was evaluated using the medication possession ratio (MPR) and proportion of days covered (PDC). The correlation between EHR and claims data was assessed with Spearman correlation coefficients, while Kaplan-Meier curves were employed to visualize and analyze the time to the first instance of non-adherence and the time to discontinuation. RESULTS: A total of 75 breast cancer patients receiving care at Massachusetts General Hospital (MGH) were identified. While there was a positive correlation, EHR data tended to overestimate adherence based on medication prescriptions, while claims data captured adherence through prescription fills. Kaplan-Meier analyses demonstrated differences in the time to first non-adherence between EHR and claims data sources, with observed variations based on language and insurance status. CONCLUSION: Our findings suggest that EHR and claims data offer complementary insights into adherence measurement, and their integration could improve adherence assessment across diverse populations.
5 Apr 2026
Read appraisal →JAMA health forum
Sources of Variation in Cardiovascular Care Cascades
IMPORTANCE: Variation in cardiovascular care completion is well documented. However, less is known about differences originating from earlier, intermediate stages such as ordering or scheduling of testing or referrals, despite their role as key prerequisites for care access. OBJECTIVE: To examine the care cascades for coronary artery disease (CAD) after emergency department (ED) visits and to identify the specific stages at which variation emerges for CAD testing and cardiology referrals. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective cohort study using data and metadata from electronic health records from a large multicenter health system. Participants were adult patients with established primary care and no history of ischemic heart disease or cardiology care who presented to an ED from January 1, 2020, to June 30, 2022, and underwent a troponin test, a proxy for clinically suspected myocardial ischemia. Variation in cardiovascular follow-up care (CAD testing and cardiology referrals) was identified and analyzed. Analyses were restricted to patients with above-median electrocardiogram (ECG)-derived ischemia risk scores to enrich for higher likelihood of benefit from cardiovascular follow-up. MAIN OUTCOMES AND MEASURES: Receipt of an order for CAD testing (stress tests, coronary computed tomography angiography) or cardiology referral, scheduling of the service, and completion within 6 months. Outcomes were compared by insurance type, race and ethnicity, language, and sex using multivariable logistic regression adjusted for demographic characteristics, clinical factors, and ECG-derived cardiovascular risk. RESULTS: Among 16 475 patients with an ED visit (median [IQR] age, 67.4 [54.9-77.9] years; 36% female and 64% male individuals) and elevated cardiovascular risk, marked variation in follow-up care emerged. Compared to commercially insured patients, those with Medicare dual or disabled coverage had lower adjusted odds of completing CAD testing (adjusted odds ratio [aOR], 0.45; 95% CI, 0.36-0.56) and cardiology referrals (aOR, 0.47; 95% CI, 0.39-0.57); similar patterns were seen for Medicaid coverage. Patients whose primary language was not English were less likely to complete either service (CAD testing aOR, 0.77; 95% CI, 0.61-0.98; referral aOR, 0.75, 95% CI, 0.61-0.92), and female patients had lower adjusted odds of completing CAD testing (aOR, 0.86; 95% CI, 0.77- 0.96). Adjusted differences by race and ethnicity were modest. Variation was primarily associated with ordering differences and with additional scheduling barriers for select groups. Once scheduled, completion rates exceeded 75%, without differences between groups. CONCLUSIONS AND RELEVANCE: This retrospective cohort study found that among patients who visited the ED with elevated ischemic risk, attrition in follow-up care was concentrated early in care cascades and most pronounced among those with noncommercial health insurance. This stepwise analytic framework offers a novel, reproducible approach for health systems to identify where and for whom care gaps arise, which can enable targeted interventions to improve equity and efficiency.
5 Apr 2026
Read appraisal →Medicine
Development of a prediction model for the risk of prolonged ICU length of stay in patients undergoing coronary artery bypass surgery: A retrospective analysis based on the MIMIC-IV database
The duration of hospitalization, particularly in the intensive care unit (ICU), for patients undergoing coronary artery bypass grafting (CABG), is influenced by both patient prognosis and treatment-related costs. Reducing the ICU length of stay (LOS) in CABG patients is critical for improving healthcare resource utilization. This study aimed to develop a model for identifying risk factors associated with prolonged ICU LOS in CABG patients, thus providing a foundation for clinical treatment, healthcare safety, and quality management research. In this single-center, retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed. Clinical data from 6152 CABG patients who required treatment in ICU were included. The dataset was split into a training set and an internal validation set. Predictor variables were identified using least absolute shrinkage and selection operator regression. A nomogram prediction model was developed based on the identified predictors. The predictors incorporated into the model included the Charlson comorbidity index, acute physiology score III, atrial fibrillation, heart failure (HF), body mass index, acute kidney injury stage, type 1 diabetes mellitus and glucose levels. The prediction model demonstrated robust performance, achieving an area under the curve of 0.789 in the training cohort and 0.790 in the validation cohort. The Hosmer-Lemeshow (H-L) test and calibration plots validated the model's accuracy in both cohorts. The nomogram prediction model proposed in this study provides significant clinical utility for predicting prolonged ICU LOS in patients undergoing CABG. This model can assist clinicians in identifying CABG patients at risk for prolonged ICU LOS, thus enabling timely interventions and improving prognostic outcomes.
5 Apr 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
A systematic review of the implementation of cancer-specific holistic needs assessment (HNA) in adult clinical practice, and applicability to the brain tumour population
PURPOSE: This review aimed to identify and review any brain tumour-specific holistic needs assessment (HNA) and the clinical implementation of any HNAs in an adult cancer population. METHODS: A systematic review and narrative synthesis. Five electronic databases were searched from 2008 to 2023 and updated in January 2025. Reference lists of systematic reviews were screened for relevant studies. Two independent reviewers performed study selection, critical appraisal, and data extraction. RESULTS: Four HNA tools implemented in clinical practice, across 9 studies, were included. One study was adapted for brain tumour patients. Evidence of widespread implementation in clinical practice was limited and varied evidence, relating to the timing, location and mode of delivering. Patients were more likely to report on physical rather than psychosocial needs, with limited evidence of patient and staff satisfaction with the use of HNA. CONCLUSION: There is limited and varied evidence of HNAs being successfully implemented in brain tumour and the wider cancer healthcare settings. Tailored interventions for the needs of people with brain tumour have the potential to address the complexities this population faces. However, clear evidence-based guidance to develop relevant HNAs for successful implementation in clinical settings is needed.
4 Apr 2026
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