搜索结果：International journal of medical informatics

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. 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. 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. 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). 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.

Generative artificial intelligence (GenAI) such as ChatGPT, is increasingly used to generate research ideas, aid in literature reviews, and support clinical reasoning. While its adoption is rapidly growing worldwide, concerns persist regarding accuracy, bias, data privacy, and academic integrity. Understanding how physicians and medical students perceive and use these tools is critical for developing effective guidelines. This study aimed to assess familiarity, usage patterns, perceived benefits, and ethical concerns surrounding ChatGPT and other GenAI platforms among physicians and medical students in Saudi Arabia. A cross-sectional survey was conducted between April 19 and July 7, 2025. The study included a total of 417 respondents: 182 physicians from multiple Saudi major academic hospitals and 235 medical students from six universities across Saudi Arabia. Recruitment was via online and on-site methods using QR codes and survey links. A validated questionnaire addressed demographics, prior research experience, ChatGPT and other GenAI use, perceived benefits, applications, and ethical concerns. Data were analyzed using IBM SPSS version 24, applying Chi-square and Fisher's exact tests to compare groups. We compared intact groups as they exist in practice; findings are interpreted in light of inherent differences in age and clinical experience, without formal adjustment. The use of GenAI in research was higher among medical students (73%) compared to physicians (59%). Most medical students had used ChatGPT (95% vs 81%, p&#xa0;<&#xa0;0.001), whereas physicians more often reported using other GenAI tools (48% vs 29%, p&#xa0;<&#xa0;0.001). Physicians most often used GenAI for academic writing (81%), while students preferred summarizing findings (75%). Nearly all students (99%) and physicians (89%) found ChatGPT easy to learn, and a majority in both groups acknowledged improvements in research efficiency (85% vs 80%), time reduction (84% vs 91%), and overall quality (77% vs 74%). Physicians expressed greater concern over data privacy (73%), while students emphasized accuracy and reliability as the key issue (77%). Both physicians and medical students are increasingly embracing GenAI for research purposes. Nonetheless, prevalent concerns about reliability and ethics highlight the need for clear guidelines and training programs to ensure responsible use of GenAI in future medical research. Group contrasts are interpreted in light of inherent differences in age and clinical experience.

Routinely collected patient reported experience measure (PREM) surveys capture patients' experience across many countries and care settings. Secondary use of open-ended PREM comments is not common practice, however with increasing volume and scope being captured, this should be investigated. We examine how routinely collected open-ended PREM comments are used in research for secondary uses and synthesise considerations. Using the JBI approach and PRISMA-ScR guideline, we searched four academic databases for peer-reviewed English-language studies (2010-2024) that used routinely collected open-ended PREM comments to answer questions focused on a specific person, place, time or perspective beyond the broad intent of the original survey. We summarised study characteristics, categories and methods of secondary use and performed a descriptive analysis of study authors' considerations for secondary use of this data. We identified 2,200 unique articles and conducted full-text review of 206 articles, yielding 25 included studies. Studies most frequently used open-ended PREM comments to investigate elements of care (32%, n&#xa0;=&#xa0;8), time periods (24%, n&#xa0;=&#xa0;6), examine types of care (20%, n&#xa0;=&#xa0;5), or treatments (20%, n&#xa0;=&#xa0;5). Researchers used manual analysis approaches (56%, n&#xa0;=&#xa0;14), applied sentiment analysis and thematic analysis (each 48%, n&#xa0;=&#xa0;12). A key strength in the secondary use of open-ended PREM comments is that it reflects what is important to patients' care experiences; while a limitation is the potential bias inherent in survey data (e.g. non-response bias). Secondary use of open-ended PREM comments for research is growing, but it is not yet widely used. Using these already collected data for research eliminates the time and cost of additional data collection and incorporates patient voice into research. A formal framework for secondary use of open-ended PREM comments will support incorporating these data into research. Secondary use of PREMs comments could enable more insightful, efficient research and maximise the patient voice in healthcare.

Artificial intelligence (AI) is increasingly integrated into scholarly publishing workflows, extending beyond manuscript preparation into editorial triage, reviewer assistance, and policy development. Peer review simultaneously faces long-standing problems including reviewer fatigue, bias, opacity, and publish-or-perish incentives. How AI interacts with these structural weaknesses remains unclear. To map how AI is currently used in scholarly peer review, synthesize reported benefits and risks, and identify governance and research gaps relevant to health sciences. A scoping review following Arksey and O'Malley was conducted and reported according to PRISMA-ScR. Scopus, Web of Science, PubMed/MEDLINE, and IEEE Xplore were searched (January 1, 2024-August 31, 2025) using terms combining artificial intelligence and peer review. Grey literature (publisher policies, professional guidelines, editorials) was identified through targeted searches of COPE, ICMJE, WAME, major publisher portals, and preprint servers. Duplicate screening/extraction with adjudication were done. Data were synthesized using inductive thematic analysis. Of 2,908 records, 189 met inclusion criteria. AI is used as AI assistive (triage, assistance) and autonomous (review generation, prediction).Reported benefits include improved workflow efficiency, standardized checks, and clearer feedback. However, current systems lack domain reasoning and ethical judgment for autonomous evaluation. Key risks are confidentiality breaches when manuscripts are submitted to third-party tools, algorithmic bias favoring elite institutions or male authors, and homogenization of scholarly voice. As of August 31, 2025, governance policies across publishers, journals, and professional societies remain fragmented. In many documented cases, reviewer use of generative AI is more restricted than author-side use; however, policies vary by publisher, journal, and society, and continue to evolve. AI can strengthen peer review when deployed as a transparent, auditable, privacy-preserving support tool under human oversight. Responsible integration in medical informatics requires coordinated governance, bias monitoring, secure infrastructures, and reforms to evaluation incentives.

Healthcare professionals are increasingly expected to use digital health technologies in their clinical practice, despite limited prior education and training. Measuring digital health competence can assist organisations to understand workforce learning needs and tailor digital transformation efforts for maximum impact. This study aims to review the extant literature to answer the questions: what validated assessment tools are available to assess healthcare professionals' digital competence and what is the quality of evidence for these tools? We used a criteria-led evaluation approach to 1) develop criteria for evaluation, 2) conduct a rapid literature review to identify tools and 3) evaluate tools against key criteria and determine their quality of evidence. We searched PubMed, CINAHL, Google scholar and grey literature up to April 22, 2025. The search strategy included three concepts: 'questionnaire OR survey' AND 'digital competence' AND 'healthcare staff'. Reporting followed PRISMA guidelines adapted for the rapid review methodology. Twenty-eight publications and grey literature met the inclusion criteria, with 61% published in the last 5&#xa0;years. Most assessments designed for the healthcare workforce were nursing-specific (n&#xa0;=&#xa0;9/20, 45%). Psychometric properties were reported for 71% of included instruments with varying quality of evidence. Only two tools met the criteria of being valid and scoped to the interprofessional healthcare workforce. Understanding which tools are validated and fit-for-purpose is essential for researchers, educators and health services seeking to measure and improve digital health competence among healthcare professionals. There is a need to expand research into interprofessional measures of healthcare workforce digital competence to support effective workforce transformation.

Machine learning (ML) models can accurately predict hospital admissions in emergency departments (EDs), but real-world adoption remains rare. We evaluated the feasibility and early implementation of an ML-based hospitalization prediction tool in an ED of a tertiary care centre using the Medical Research Council (MRC) framework. A prospective mixed-methods study was conducted at the ED of the Leiden University Medical Centre (&#x223c;23,000 annual visits). A validated ML-based hospitalization prediction tool was deployed via a web-based dashboard for four months. The dashboard was designed with stakeholders' input obtained during focus groups. Staff use, perceptions, and implementation barriers were assessed through pre-/post-implementation surveys with Likert-scales and open questions, and were analyzed using Mann-Whitney U-tests. Operational outcomes (ED length of stay [LOS], hospitalization rates) were extracted from the Netherlands Emergency department Evaluation Database and compared across pre- and post-implementation periods via logistic regression adjusted for confounders. Tool utilization was low despite an initial positive attitude towards the tool: 67% of staff reported rarely or never using it. Post-implementation surveys indicated a decline in perceived utility and reduced concern about AI replacing clinical roles. Reported barriers included lack of electronic health record integration, absence of linked actions, and misalignment with existing workflows. Hospitalization rose from 31.8% to 33.3% (p&#xa0;=&#xa0;0.027), while ED-LOS increased during the implementation period (19.8% to 26.1%, p&#xa0;<&#xa0;0.001); these changes could not be attributed to the tool given limited adoption. This early implementation study demonstrated low adoption of a ML prediction tool for hospitalization at the ED. Context-specific implementation barriers were identified. Guided by the MRC framework, the findings offer concrete strategies to enhance future implementation, including EHR integration, embedded action protocols, and role-specific responsibilities.

Information on childhood cancer burden is crucial for effective cancer policy planning. Unfortunately, observed paediatric cancer data are not available in every country, and previous global burden estimates have not discretely reported several common cancers of childhood. We aimed to inform efforts to address childhood cancer burden globally by analysing results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, which now include nine additional cancer causes compared with previous GBD analyses. GBD 2023 data sources for cancer estimation included population-based cancer registries, vital registration systems, and verbal autopsies. For childhood cancers (defined as those occurring at ages 0-19 years), mortality was estimated using cancer-specific ensemble models and incidence was estimated using mortality estimates and modelled mortality-to-incidence ratios (MIRs). Years of life lost (YLLs) were estimated by multiplying age-specific cancer deaths by the standard life expectancy at the age of death. Prevalence was estimated using survival estimates modelled from MIRs and multiplied by sequelae-specific disability weights to estimate years lived with disability (YLDs). Disability-adjusted life-years (DALYs) were estimated as the sum of YLLs and YLDs. Estimates are presented globally and by geographical and resource groupings, and all estimates are presented with 95% uncertainty intervals (UIs). Globally, in 2023, there were an estimated 377&#x2008;000 incident childhood cancer cases (95% UI 288&#x2008;000-489&#x2008;000), 144&#x2008;000 deaths (131&#x2008;000-162&#x2008;000), and 11&#xb7;7 million (10&#xb7;7-13&#xb7;2) DALYs due to childhood cancer. Deaths due to childhood cancer decreased by 27&#xb7;0% (15&#xb7;5-36&#xb7;1) globally, from 197&#x2008;000 (173&#x2008;000-218&#x2008;000) in 1990, but increased in the WHO African region by 55&#xb7;6% (25&#xb7;5-92&#xb7;4), from 31&#x2008;500 (24&#x2008;900-38&#x2008;500) to 49&#x2008;000 (42&#x2008;600-58&#x2008;200) between 1990 and 2023. In 2023, age-standardised YLLs due to childhood cancer were inversely correlated with country-level Socio-demographic Index. Childhood cancer was the eighth-leading cause of childhood deaths and the ninth-leading cause of DALYs among all cancers in 2023. The percentage of DALYs due to uncategorised childhood cancers was reduced from 26&#xb7;5% (26&#xb7;5-26&#xb7;5) in GBD 2017 to 10&#xb7;5% (8&#xb7;1-13&#xb7;1) with the addition of the nine new cancer causes. Target cancers for the WHO Global Initiative for Childhood Cancer (GICC) comprised 47&#xb7;3% (42&#xb7;2-52&#xb7;0) of global childhood cancer deaths in 2023. Global childhood cancer burden remains a substantial contributor to global childhood disease and cancer burden and is disproportionately weighted towards resource-limited settings. The estimation of additional cancer types relevant in childhood provides a step towards alignment with WHO GICC targets. Efforts to decrease global childhood cancer burden should focus on addressing the inequities in burden worldwide and support comprehensive improvements along the childhood cancer diagnosis and care continuum. St Jude Children's Research Hospital, Gates Foundation, and St Baldrick's Foundation.

In the evolving landscape of health care, data use plays an ever-increasing role in health care IT. However, data are often siloed and uncoded free text distributed across several IT systems. This paper introduces a health knowledge management platform, designed to integrate, harmonize, and enable reuse of health care and medical research data. The platform aims to bridge the gap between research and patient care, showcased through real-world scenarios, emphasizing data harmonization and knowledge management within a health care institution. The study is based at the University Hospital Schleswig-Holstein. The main objective of this project is to design, implement, and evaluate a knowledge management platform that integrates health care and biomedical research to support use cases in both domains. The study describes the "health knowledge management platform" designed to access and gain knowledge from health care and medical research data. We performed several rounds of focus groups with stakeholders to elicit the platform requirements. In the process, we identified key aspects of the platform. From the functional requirements, we designed an architectural concept. The platform evaluation follows the Framework for Evaluation in Design Science Research and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 standard with a focus on key aspects identified and real-world scenarios. Two application scenarios, cardiology and radiology, are selected for a requirement-based, qualitative evaluation. We show that our health knowledge management platform is capable of integrating diverse data formats like Health Level 7 Version 2 messages, CSV exports, and Digital Imaging and Communications in Medicine. It currently integrates over 46 million admit, discharge, transfer messages, 38 million imaging studies, and structured clinical data for approximately 1.5 million patients. The platform supports different scenarios based on its 5-layer architecture, including a clinical data repository and services like Master Patient Index and Consent Management. The evaluation against 39 predefined functional requirements showed our platform's capability in certain real-world scenarios of cardiology and radiology. Our evaluation demonstrates that the platform covers the majority of the identified requirements to support knowledge management in health care institutions. Our requirement-based evaluation of the health knowledge management platform at University Hospital Schleswig-Holstein reveals its capabilities, which is possibly leading to better knowledge transfer between patient care and research. The platform's architecture and standardized data improve the quality of data and facilitate access to knowledge. Ongoing development and potential quantitative measures will further enhance its applicability in dynamic health care landscapes.

Clinical prediction models often suffer from poor model transportability and/or subgroup performance resulting from using a single data source. We aimed to determine whether ensemble methods can combine multiple existing models to improve predictive performance when compared to component models. As a case study, we used electronic medical records from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) to test ensemble methods for models estimating the risk of developing chronic kidney disease (CKD) among people with diabetes in a cohort of 37,604 individuals. We considered 13 models identified from prior systematic reviews and combined their unique risk estimates using many strategies (e.g., averaging or mixture-of-experts). We assessed discrimination, precision, recall, calibration, net reclassification index, and integrated discrimination improvement. Ensemble methods performed well, but no better than the best performing component model. Among ensemble methods, the averaging or selection process with the best performance weighted the predictions from all component models by their development cohort size (AUROC: 0.827 [95% CI: 0.821 to 0.833]). However, this did not exceed the best performing component model (AUROC: 0.826 [95% CI: 0.820 to 0.832]). Similarly, based on the NRI>0, estimated risks based on the ensemble methods were often worse than the best performing component model. This study suggests ensemble methods may not improve predictive performance, though further research should confirm these findings. Many clinical prediction models exist that predict the same outcome, but commonly suffer poor performance when applied in new settings. Ensemble methods provide a method of combining multiple models developed across diverse settings to potentially improve predictive performance. When applied in primary care electronic medical records, we found that ensemble models based on existing clinical prediction models could match, but did not surpass the performance of the best performing component model. Ensemble methods may not be necessary to combine existing models; rather, the best performing component model can be used.

Radiomics has shown potential for quantitative characterization of tumors in molecular imaging; however, its clinical translation in theranostic 1 7 7Lu SPECT/CT remains limited due to poor robustness of extracted features to reconstruction variability and partial volume effects. Establishing reproducible radiomics biomarkers across correction strategies is therefore a prerequisite for reliable clinical modeling and treatment monitoring. This study aimed to evaluate radiomics feature reproducibility, defined as the stability of feature values across different partial volume correction (PVC) strategies and reconstruction settings, in clinical 1 7 7Lu SPECT/CT imaging. In addition, we explored two volumetric shape-based indices, the metastasis-to-liver ratio (MLR) and metastasis-to-spare liver ratio (MSLR), as surrogate markers of hepatic metastatic burden in the theranostic treatment setting. In 13 patients (40 scans) treated with 177Lu, 837 radiomics features were extracted from 11 abdominal regions and metastases on SPECT/CT using original and wavelet-decomposed images across four bin widths (50-200). Two post-reconstruction PVC methods, namely Richardson-Lucy (RL) and Reblurred Van Cittert (RVC), were applied. Feature reproducibility was quantified using two complementary metrics: the intraclass correlation coefficient (ICC) to assess feature-level stability across PVC strategies, and the concordance correlation coefficient (CCC) to evaluate pairwise agreement and systematic bias among reconstruction methods. Visual image quality assessments were independently performed by two experienced nuclear medicine specialists in a blinded setting. Exploratory metastatic tumor burden was assessed descriptively using 3D shape-based MLR and MSLR indices. Low-frequency wavelet decomposition (LLL-wavelet) and original features showed the highest reproducibility (ICC&#xa0;&#x2265;&#xa0;0.90 in&#xa0;>95% of liver and metastasis features at BW50), whereas high-frequency features and larger bin widths demonstrated reduced stability. CCC analysis revealed excellent agreement between RL and RVC (&#x2265;0.95 in major organs at BW50-100), while agreement with uncorrected SPECT (no PVC) was consistently lower, especially for high-frequency features. RL achieved higher visual scores in sharpness and contrast (p&#xa0;<&#xa0;0.01), with good inter-reader agreement supporting the consistency of these assessments. MLR/MSLR demonstrated inter-patient variability and were explored descriptively as indices of metastatic liver burden. Reproducibility in theranostic SPECT radiomics is highly feature- and organ-dependent and is further influenced by scanner-specific factors and reconstruction protocols, which remain critical for real-world clinical translation. RL and RVC showed stronger mutual agreements than each with uncorrected SPECT. Importantly, only RVC translated visual improvements into enhanced feature-level reproducibility, while RL provided the most consistent overall balance of reproducibility and image quality, supporting its role as the preferred PVC strategy for clinical and modeling applications. Robust radiomics feature selection as well as standardized reproducible PVC strategies are essential to generate methodological harmonization for future clinical translation and to support integration of radiomics analyses into personalized SPECT theranostics.

Perioperative anticoagulant management is critical because of the competing risks of ischemia and bleeding. Large language models (LLMs) and clinical decision support (CDS) have shown substantial advances and are increasingly being applied across various domains of cardiology. However, to date, no studies have specifically evaluated the performance of LLMs combined with CDS in perioperative anticoagulant management. Two cardiologists developed 40 guideline-based clinical scenarios involving patients receiving oral anticoagulants scheduled for non-cardiac surgery, including 20 direct oral anticoagulant (DOAC) and 20 warfarin scenarios. Three commonly used LLMs (ChatGPT 5.2, Gemini 3.0 Pro, and DeepSeek V3.2) were evaluated in two phases: baseline performance (Phase 1) and performance after integration of structured CDS tables (Phase 2). Responses were assessed for guideline concordance on a per-scenario basis, requiring correct recommendations across all predefined domains. Model performances were compared using Cochran's Q test, with post hoc Dunn-Bonferroni correction, and within-model comparisons were performed using McNemar's test. In Phase 1, guideline-concordant management was achieved in 60% of scenarios by ChatGPT-5.2, 55% by Gemini 3.0 Pro, and 50% by DeepSeek V3.2, with no significant difference among models (p&#xa0;=&#xa0;0.180). Following CDS augmentation in Phase 2, scenario-level accuracy improved significantly for all models (all p&#xa0;<&#xa0;0.05), increasing to 80% for ChatGPT-5.2, 100% for Gemini 3.0 Pro, and 85% for DeepSeek V3.2. Comparative analysis in Phase 2 demonstrated a significant difference among models (p&#xa0;=&#xa0;0.009), driven primarily by superior performance of Gemini 3.0 Pro compared with ChatGPT-5.2 (adjusted p&#xa0;=&#xa0;0.009). Baseline performance of LLMs in perioperative anticoagulant management was modest and insufficient to replace clinical judgment. However, integration of structured CDS tools resulted in marked and clinically meaningful improvements in guideline-concordant performance across all evaluated models.

The integration of artificial intelligence (AI) into virtual emergency care represents a potentially transformative approach to healthcare delivery, yet the evidence base remains poorly characterized. This systematic review comprehensively evaluates the current state of AI applications in virtual emergency care settings. We systematically searched eight databases (Embase, PsycINFO, MEDLINE, PubMed, Scopus, Web of Science, CINAHL, Cochrane Library) from inception through March 2025. Of 7,098 records identified and 4,935 screened after deduplication using Covidence, 8 studies met inclusion criteria following exclusion of one study lacking AI components. Studies were assessed using PROBAST&#xa0;+&#xa0;AI for risk of bias and quality assessment, TRIPOD&#xa0;+&#xa0;AI for reporting quality, and GRADE for certainty of evidence. The eight included studies (total participants: approximately 0.5 million) evaluated diverse AI applications including decision trees, machine learning ensembles, and graph neural networks across multiple virtual emergency contexts. Performance varied widely (accuracy 77.5-100%, sensitivity 63-100%, specificity 60% in single study reporting). All clinical studies demonstrated serious risk of bias. TRIPOD&#xa0;+&#xa0;AI compliance averaged only 36.9% (range 30.9-48.1%). GRADE assessment revealed very low to low certainty evidence across all outcomes, with no studies measuring actual clinical outcomes. Current evidence is insufficient to support widespread clinical implementation of AI in virtual emergency care. While preliminary results suggest potential benefits in triage accuracy and resource efficiency, critical gaps exist in validation, clinical outcome assessment, and reporting standards. Future research must prioritize prospective controlled trials with real patient data, clinical outcome measurements, and adherence to reporting guidelines.

Meningitis remains the leading infectious cause of neurological disabilities globally, disproportionately affecting children younger than 5 years and populations in the African meningitis belt. Whereas previous global estimates focused on ten pathogen categories, this study presents the most comprehensive analysis to date, assessing the meningitis burden attributable to 17 causative pathogens based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework. GBD is a systematic, scientific effort aimed at quantifying the comparative magnitude of health loss caused by diseases, injuries, and risk factors across age groups, sexes, and geographical locations over time. We estimated meningitis mortality using the Cause of Death Ensemble model (CODEm) and morbidity using DisMod-MR 2.1, incorporating data from vital registration, verbal autopsy, surveillance, hospital data, and systematic reviews. Aetiology-specific estimates were generated with pathogen-linked case-fatality ratios and splined binomial regression models. Risk factor attribution was based on established risk-outcome pairs and population attributable fractions. In 2023, there were 259&#x2009;000 (95% uncertainty interval 202&#x2009;000-335&#x2009;000) global deaths and 2&#xb7;54 million (2&#xb7;20-2&#xb7;93) incident cases of meningitis. Children younger than 5 years accounted for more than a third of deaths (86&#x2009;600 [53&#x2009;300-149&#x2009;000]). Streptococcus pneumoniae, Neisseria meningitidis, non-polio enteroviruses, and other viruses were the leading causes of death, while non-polio enteroviruses caused the most cases. The four WHO-defined preventable meningitis pathogens of interest (S pneumoniae, N meningitidis, Haemophilus influenzae, and Group B streptococcus) contributed to 98&#x2009;700 deaths (77&#x2009;000-127&#x2009;000) and 594&#x2009;000 cases (514&#x2009;000-686&#x2009;000). Low birthweight, short gestation, and household air pollution were the top risk factors for meningitis-related mortality. Although mortality and incidence have declined significantly since 1990, progress is insufficient to meet WHO 2030 targets. Despite marked progress in reducing bacterial meningitis via global vaccination campaigns, a substantial meningitis burden persists, attributable both to common pathogens such as S pneumoniae and N meningitidis and to emerging non-bacterial pathogens such as Candida spp and drug-resistant fungi. Achieving WHO goals will require sustained investment in surveillance, vaccination, maternal screening, and health-system strengthening, especially in high-burden settings. Gates Foundation, Wellcome Trust, and UK Department of Health and Social Care.

Veterans enrolled in both Veterans Affairs (VA) health care and Medicare Part D can choose to obtain medications through the VA, Medicare Part D, or both, with each option differing in cost, coverage, and coordination of care. Poorly informed choices can lead to veteran frustration when their expectations are not met, delays in medication access, and increased risks. This study aimed to develop a decision aid (DA) to help veterans with diabetes make informed choices about medication sourcing (ie, whether to fill medications through VA health care only, Part D only, or both). DA development was guided by the International Patient DA Standards and the Ottawa Decision Support Framework. Interviews with veterans and care partners informed the prototype design. Alpha testing with 18 end users (mostly veterans) and 12 stakeholders (pharmacists, doctors, payers, Medicare counselors, and others) assessed comprehensibility, usability, and acceptability. During beta testing, the feasibility of the revised DA was assessed during interviews with 20 end users and 8 stakeholders. For end-user interviews, a survey assessing decisional conflict, satisfaction, and knowledge was provided before and after respondents filled out the DA. Alpha testing feedback led to simplifying the cost and formulary comparison chart and expanding the medication list template to include more medications, fill location, and prescriber contact information. Based on beta testing responses (n&#x2009;=&#x2009;16), the mean system usability scale score for the DA was 77.5 (SD&#x2009;=&#x2009;14.4), suggesting usability. Beta testers also reported the DA to be acceptable in length (94%), balance (88%), and amount of information (81%). Based on pre- vs post-DA survey responses, decisional conflict was reduced, as indicated by an increase in the mean Sure of myself; Understand information; Risk-benefit ratio; Encouragement (SURE) score (pre-DA: 3.1, SD&#x2009;=&#x2009;1.4 to all 16 respondents reporting a maximum SURE score of 4.0). Knowledge about VA and Medicare coverage of diabetes medications also improved: the proportion who answered all 5 comprehension questions correctly increased from 57% to 81%. Last, the proportion of respondents who reported being "very satisfied" with how they were currently filling their diabetes medicines (VA only, Part D only, or both) improved from 64% pre-DA to 81% post-DA. The DA developed iteratively was usable and acceptable and showed potential in reducing decisional conflict, increasing knowledge, and increasing satisfaction, so it may help Part D-enrolled veterans with decisions about medication sourcing.

Hospital Italiano de Buenos Aires (HIBA) has progressively integrated artificial intelligence (AI) technologies into clinical practice over more than two decades. Describing this process may provide useful insights for healthcare institutions aiming to adopt AI in a structured and sustainable manner. To describe the evolution of AI implementation at HIBA, identifying key stages, technologies, and lessons learned throughout this institutional journey. A retrospective institutional review was conducted, describing four sequential stages of AI adoption at HIBA. Each stage was characterized according to the predominant AI technologies, their clinical applications, and their degree of integration into routine healthcare workflows. The first stage (1997-2009) focused on establishing digital foundations and clinical decision support systems, improving patient safety through pharmacological alerts and structured clinical recommendations. The second stage (2010-2017) involved natural language processing and speech technologies, enabling the extraction of structured information from unstructured clinical text and the development of automatic speech recognition systems. The third stage (2018-2022) encompassed computer vision applications in medical imaging, including convolutional neural networks for breast density assessment and triage systems for chest radiographs, with emphasis on iterative validation and integration into clinical workflows. The fourth stage (2023-present) explores generative AI and large language models, exemplified by the internally developed chatbot TANA, supporting clinical decision-making, digital triage, and patient engagement. Across all stages, key lessons emerged related to data quality, interdisciplinary collaboration, model validation, user training, ethical safeguards, and responsible AI implementation. This overview highlights the institutional strategies and challenges associated with long-term AI adoption in healthcare. The experience at HIBA may offer relevant guidance for other hospitals seeking to integrate AI into clinical practice.

Patients with acute respiratory distress syndrome (ARDS) often develop stress-induced hyperglycemia, and glycemic variability is associated with adverse outcomes. Traditional static glycemic indicators cannot capture dynamic glucose changes, and current ARDS prognostic models lack integration of dynamic glycemic trajectories, leading to insufficient precision in early risk stratification. This study aimed to investigate the association between early glycemic trajectory and 30-day mortality in ARDS patients, while constructing and validating a prognostic prediction model integrating dynamic glycemic features. A total of 8,103 ARDS patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database were enrolled as training set, and 158 patients from a single center served as external validation set. Group-based trajectory modeling (GBTM) was used to cluster the blood glucose trajectory within 48&#xa0;h of admission. Independent prognostic factors were identified using Cox proportional hazards models, and a nomogram model was constructed. And the nomogram was validate using receiver operating characteristic curve, calibration curve and decision curve analysis. Four blood glucose trajectories were identified: G1 (20.01%), G2 (37.43%), G3 (34.64%) and G4 (7.91%). Among them, G2 group with stable and low blood glucose levels had the highest 30-day survival probability, while G4 group with an initial high blood glucose level that decreased sharply and then slightly increased had a significantly higher 30-day death (log-rank P&#xa0;<&#xa0;0.0001). The nomogram integrating 16 predictors including glucose trajectory, age, respiratory rate, and anion gap achieved good discrimination (AUC&#xa0;=&#xa0;0.72 in training set, 0.75 in validation set), favorable calibration, and high net clinical benefit. Early blood glucose trajectory is an independent predictor of 30-day mortality in patients with ARDS. The nomogram constructed based on dynamic blood glucose evolution has good predictive efficacy and clinical applicability, and can provide a quantitative tool for the early intervention of high-risk patients.

Advances in artificial intelligence have brought renewed attention to tools that can work with the large amount of written information generated in clinical practice. Among these, large language models (LLMs) stand out for their ability to interpret and generate medical text in a flexible, context-aware way. Gastroenterology, like many specialties, produces a wide range of narrative and semi-structured data, and this has encouraged researchers to explore how LLMs might help clinicians manage everyday tasks. Recent studies have examined their potential contributions to patient education, communication between care teams and patients, decision support, and routine documentation, reflecting a growing interest in how these systems might fit into real-world clinical workflows. This scoping review aimed to map current applications of LLMs in gastroenterology clinical practice, including subspecialty focus, study designs, model types, and reported outcomes. Following PRISMA guidelines, a systematic search was conducted in PubMed, Scopus, and Web of Science for studies published between January 2022 and August 2025. Eligible studies included original research assessing LLM applications in gastroenterology clinical practice. Data were extracted on subspecialty, application domain, LLM type, data source, and outcomes. We employed thematic analysis to address our primary research question. 73 out of 2895 studies identified in the initial search met the inclusion criteria. Six subspecialties and six application domains emerged from our review. Hepatology (20/73 studies, 27.3%) and endoscopy (17/73 studies, 23.2%) were the most represented subspecialties. The most frequently investigated application domains were patient education and communication (38 studies) and decision support and clinical guidance (24 studies). Most studies were simulation-based or literature-based cases, although an increasing number have used real-world clinical data, particularly in recent years. The majority evaluated general-purpose models such as GPT-3.5 and GPT-4, with some incorporating retrieval augmentation or fine-tuning. Reported outcomes varied by application domain and included measures of accuracy, concordance, completeness, relevance, safety, reliability, usability, user satisfaction, efficiency, time savings, and educational value. Commonly described limitations included variable reliability, incomplete responses, and challenges in generalizing from simulated to clinical settings. Research on LLMs in gastroenterology has expanded across multiple subspecialties and application domains. Current evidence is primarily based on simulation studies, with limited but growing evaluation using real-world clinical data. Further work is needed to assess performance in prospective and applied clinical contexts.

Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions requires consistent engagement in self-care behaviours such as healthy eating, physical activity, and medication adherence. However, behavioural interventions often lack personalisation, limiting their impact, whereas digital twin (DT) systems, which use digital technologies to generate real-time representations of individuals, offer the potential to support people through adaptive and patient-centred approaches by integrating health data to personalise and optimise self-care strategies. A systematic review was conducted to synthesise and evaluate the use of DT for supporting self-care behaviours among adults with diet-related chronic conditions METHODS: Five electronic databases (PubMed, Web of Science, Embase, CINAHL, and IEEE Xplore) were searched from inception to 31 July 2025. Studies including adults with diet-related chronic conditions, using DT interventions, and reporting changes in self-care maintenance, monitoring, or management were eligible. Of 3,685 records identified, four studies (N&#xa0;=&#xa0;2,662) met the inclusion criteria, all focusing on type 2 diabetes. All four studies used continuous glucose monitoring (CGM), and three additionally used wearable devices and dietary logs integrated with AI-driven DT platforms to provide personalised feedback and recommendations, often alongside human coaching. Two retrospective studies found substantial reductions in antidiabetic medication use, with one reporting a 74% reduction over one year including discontinuation of insulin in 94% of baseline insulin users, and large class&#x2011;specific declines, such as sulfonylureas (-99%) and DPP&#x2011;4 inhibitors (-88%), with many participants maintaining HbA1c&#xa0;<&#xa0;7% on no therapy or metformin monotherapy, and the other showing that 42.8% of participants eliminated all diabetes medications within 90&#xa0;days while maintaining glycaemic control (via a staged medication&#x2011;reduction framework). One randomised controlled trial found that 94% of participants discontinued all T2D medications and 72.7% achieved diabetes remission after one year, accompanied by increased daily steps, improved sleep, and reduced sedentary time (with only 4.7% remaining on metformin alone at one year). Another study found that personalised insulin infusion guided by a DT over 14&#xa0;days reduced insulin doses by 14-29% and increased time in the target glucose range to 86-97% (personal insulin pump data over a 14&#x2011;day collection period) CONCLUSIONS: DT-based interventions demonstrated potential to enhance multi-behavioural self-care and clinical outcomes in adults with type 2 diabetes. Evidence is currently limited to diabetes, highlighting the need for studies in other diet-related chronic conditions and standardised assessment of self-care behaviours.

The kidneys play a crucial role in eliminating drugs, and their functional impairment can lead to drug accumulation and adverse effects. To prevent this, drug dosages (doses or administration intervals) are adjusted based on kidney function using the Glomerular Filtration Rate (GFR). Calculating dosages is an error prone task, particularly in paediatric patients where individual dosing strategies can be complex. This study assessed the potential application of a novel feature of the paediatric clinical decision support system (CDSS) PEDeDose that calculates adjusted drug dosages for children with impaired kidney function. Renal dosing practices in a paediatric intensive care patients with impaired kidney function were compared to recommendations provided by PEDeDose. This retrospective observational feasibility study analysed drug prescriptions in patients with impaired kidney function from electronic health records of the paediatric intensive care unit (PICU) of the University Children's Hospital Basel in 2023. Extracted data included patients' age, weight, height, and length of PICU stay, drug administration records, serum creatinine and cystatin C values, and diagnoses. The primary outcome was the frequency of dosage adjustments adherent to CDSS' recommendations. Out of 436 patients, 20 (5%) patients were included. In this study of 20 patients, 7 (33%) were prescribed drugs that required dosage adjustments due to their impaired kidney function. Across these patients, dosage adjustment was indicated in 11 prescriptions, and 8 (73%) of these dosages were adjusted as recommended by the CDSS. The study observed good concordance between clinical practice and the CDSS' recommendations on dosage adjustment in paediatrics. This supports the potential of PEDeDose to facilitate dosing and maintain quality of drug prescribing in children with kidney impairment. Considering the complexity of drug prescribing in this patient population, the support provided by PEDeDose may benefit paediatric patients and their caregivers beyond a specialised university hospital setting.