Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 49 appraisals
Artificial intelligence in medicine
Artificial intelligence language models for medical text analysis: A systematic review
Medical text records serve as essential repositories of patient information, providing a foundation for informed clinical decision-making, accurate diagnosis, reliable prognosis, and effective treatment planning. Recent advancements in Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) and Machine Learning (ML), have positioned AI-driven language models as powerful tools for analyzing, classifying, and generating medical textual data. In this systematic literature review, an initial search retrieved 548 records published between 1 January 2000 and 1 July 2024. After rigorous screening based on predefined inclusion and exclusion criteria, 22 original research articles were included. The review highlights substantial progress in applying advanced architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT) to medical text processing tasks. These models consistently outperform conventional NLP and ML approaches, achieving superior results in disease classification, automated clinical documentation, and predictive analytics. However, critical challenges persist, including the limited availability of clinically validated datasets, variability in data preprocessing protocols, insufficient external validation, and the lack of interpretable AI frameworks, all of which collectively hinder clinical trust and large-scale adoption. Future research should prioritize the development of hybrid AI systems that integrate multimodal data sources (text, imaging, and structured records), incorporate explainable AI mechanisms, and adhere to standardized reporting frameworks. Addressing these methodological gaps will be pivotal in enhancing the reliability, clinical applicability, and impact of AI language models, thereby advancing evidence-based medicine, personalized treatment strategies, and overall patient care.
2 Aug 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Utility of Repeating Blood Cultures in Candidemia: Insights From 2 Large Clinical Data Repositories
We evaluated utility of repeating blood culture for candidemia using 2 electronic health record datasets. Among 1386 candidemia episodes, repeated blood culture detected 23% of episodes at Houston Methodist Hospital and 45% in Medical Information Mart for Intensive Care IV after the initial negative cultures. These findings support repeating blood cultures when candidemia is highly suspected.
24 July 2026
Read appraisal →Journal of medical systems
Recent Advances in AI for Automated ICD Coding: A Systematic Literature Review
International Classification of Diseases (ICD) codes enable correct billing, insurance reimbursement, and healthcare analytics. However, manual coding is time-consuming, expensive, and error-prone, creating bottlenecks in clinical workflow and limiting scalability. Artificial intelligence (AI) has emerged as a promising solution for automated ICD code assignment from unstructured clinical text. This systematic review explores the current state of automated ICD coding research, examining models applied to diverse clinical documents including discharge summaries, electronic health records, nursing notes, and pathology reports. Following PRISMA guidelines, we searched six databases for studies published between 2019 and 2024, selecting 54 relevant studies from 4,280 initial citations. Our analysis reveals the use of diverse datasets, preprocessing techniques, and feature extraction methods, alongside a clear evolution from traditional machine learning to deep learning approaches, with substantial architectural diversity across convolutional, recurrent, transformer, and hybrid models. Performance varies considerably across dataset configurations, with models achieving higher accuracy on frequent code subsets compared to full label spaces. However, critical gaps persist: overreliance on single-language, single-institution datasets limits generalizability; difficulties in predicting rare codes remain unresolved; lack of model interpretability undermines clinical trust; and inconsistent evaluation protocols hinder meaningful comparison. To address these challenges, we propose a 5P evidence-grounded research agenda: Population Diversity, Performance Robustness, Prediction of Rare Codes, Provenance Transparency, and Practical Integration. These findings underscore AI's potential to transform ICD coding while highlighting the need for standardized benchmarks, rigorous external validation, multilingual datasets, and explainable architectures to enable equitable and effective deployment in real-world healthcare systems.
19 July 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
More Than a Message: Death by a 1000 'Chats'.
Secure electronic messaging within the electronic health record (EHR), such as Epic Chat, has transformed communication in hospitals by enabling rapid, asynchronous communication. Yet for Infectious Diseases (ID) consultants, the constant barrage of messages comes at a cost: fragmented attention, disrupted clinical reasoning, and potential patient harm. Messages that are repetitive, trivial, or casually worded can also fuel miscommunication and even medicolegal risk. As this type of messaging becomes increasingly prevalent in clinical practice, we must establish clearer boundaries and a cultural shift that values purposeful communication. Practical strategies include defining what requires chat versus direct phone or in-person conversation, batching responses, protecting focus time, and teaching messaging etiquette. To optimize patient care and protect clinician well-being, we must be intentional about when and how we communicate.
9 July 2026
Read appraisal →JMIR medical informatics
Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study
BACKGROUND: Irregularly sampled data, as a common data structure in the medical field, is frequently observed in emergency clinical datasets. It poses problems such as unequal sampling time intervals and frequencies, making it difficult to align the data without losing information for input into models. Meanwhile, due to information loss in the dataset, it is also difficult for the model to effectively analyze the overall data variation and predict the complex and dynamic conditions of emergency patients in the future. OBJECTIVE: This study aimed to address the impact of issues such as missing values and irregularly sampled time intervals on prediction results. This paper proposes a method that converts time-series data into a graph network structure and develops a prediction model for graph data to better support complex tasks. METHODS: In this paper, feature channels and measurement times in time-series data are constructed as nodes, and feature measurements at time points are constructed as edge weights. The point feature space and edge weight space are extracted by a graph convolutional neural network and a gated convolutional attention mechanism network, respectively, and feature fusion is carried out under the action of the learnable head. RESULTS: The proposed method was tested on 4 datasets and compared with both irregularly sampled time-series predictors and state-of-the-art methods for complete data. In addition, a dedicated analysis was conducted leveraging data from 5 key clinical treatment phases. In single-target classification and single-target regression tasks, the method proposed in this study outperforms other comparative methods in most experiments (accuracy=0.74-0.88, area under the curve=0.70-0.87, and mean absolute error=0.038-0.061). Meanwhile, this study constructs clinical phenotypes during patients' hospitalization as multitarget prediction tasks, and the proposed model also outperforms comparative methods in multitarget classification tasks (mean accuracy=0.75-0.91 and mean area under the curve=0.70-0.75). Ablation experiments verify the effectiveness of fusion between the node feature space and the edge feature space. The interpretability analysis of time points identifies key features that require focused attention for patients across different target tasks and time periods. CONCLUSIONS: The proposed model can effectively analyze medical data with missing values and uneven sampling. It achieves high prediction accuracy in both regression prediction tasks and classification tasks. The graph network-based model structure endows the model with better interpretability, enabling effective exploration of factors that trigger changes.
5 July 2026
Read appraisal →Journal of the American Medical Informatics Association : JAMIA
Engineering biomarker representations of vital signs data enhances deep learning mortality prediction
OBJECTIVES: We evaluated bidirectional long short-term memory models for predicting inpatient mortality using different approaches to processing vital signs data collected during the initial 24 h of intensive care unit (ICU) admissions. MATERIALS AND METHODS: We compared 3 vital-sign representations: (1) raw data recorded every 5 min, (2) preprocessed data averaged hourly, and (3) preprocessed data using biomarker representations that extends a digital oximetry biomarker toolbox of PhysioZoo software, applied to blood pressure, heart rate, temperature, respiratory rate, and SpO2. RESULTS: Across 2 large ICU datasets, HiRID and eICU, models trained on the frequency-normalized representation achieved higher discrimination and lower Brier scores than those trained on raw 5-min and hourly averaged data. DISCUSSION: The use of biomarker representations of vital signs yielded the largest improvements in discrimination and overall probabilistic performance reflected by lower Brier scores for predicting inpatient mortality by deep learning. CONCLUSION: Thus, we recommend using a similar approach to vital signs preprocessing for time-series predictive models.
3 July 2026
Read appraisal →PloS one
Developing a natural language processing system using transformer-based models for adverse drug event detection in electronic health records
OBJECTIVE: To develop a transformer-based natural language processing (NLP) system for detecting adverse drug events (ADEs) from clinical notes in electronic health records (EHRs). MATERIALS AND METHODS: We fine-tuned BERT Short-Formers and Clinical-Longformer using the processed dataset from the 2018 National NLP Clinical Challenges (n2c2) shared task Track 2. Two data processing methods, window-based and split-based approaches, were compared to identify the optimal processing method. Model generalizability was evaluated on a dataset extracted from Vanderbilt University Medical Center (VUMC) EHRs. RESULTS: On the n2c2 dataset, the best 5-fold cross-validation AUPRC, micro F1, and macro F1 scores were 0.840 (Clinical-Longformer, 4-chunk split), 0.965 (BioBERT, 15-word window), and 0.852 (Clinical-Longformer, 10-chunk split). On the VUMC dataset, the best AUPRC, micro F1, and macro F1 scores were 0.536 (Clinical-Longformer, 6-chunk split), 0.966 (BERT-base-uncased, 6-chunk split), and 0.762 (Clinical-Longformer, 4-chunk split). DISCUSSION: Transformer-based models demonstrated strong performance for ADE detection, with split-based processing generally outperforming window-based methods. Clinical-Longformer combined with a practical split-based approach showed promise for real-world implementation. Beyond token limits, chunk size substantially influenced model performance, even when text length remained within limits. CONCLUSION: Our findings provide practical guidance for developing transformer-based ADE detection systems from clinical notes. The selection of both text preprocessing strategies and model architectures should be guided by note characteristics and practical considerations such as annotation burden.
2 July 2026
Read appraisal →Journal of biomedical informatics
Clinical document metadata extraction: A scoping review.
OBJECTIVES: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applications, and highlight gaps warranting further investigation. METHODS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines to identify articles from Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science and external sources that perform clinical document metadata extraction, either primarily as a methodology study, secondarily as a feature for a downstream application, or for analysis. We initially identified and screened 342 articles published between 2011 and 2025, then comprehensively reviewed 77 we deemed relevant to our study. RESULTS: Among the 77 articles included in our full text review, 49 were methodological, 22 used document metadata as features in a downstream application, and 6 analyzed document metadata composition. We observe myriad purposes for methodological study and application types. Available labelled public data remains sparse except for structural section datasets. Methods for extracting document metadata have progressed from largely rule-based and traditional machine learning with ample feature engineering to transformer-based architectures with minimal feature engineering. DISCUSSION AND CONCLUSION: Clinical document metadata extraction research has accelerated over recent years. The emergence of large language models has enabled broader exploration of generalizability across tasks and datasets, allowing the possibility of advanced clinical text processing systems. We anticipate that research will continue to expand into richer document metadata representations and integrate further into clinical applications and workflows.
2 July 2026
Read appraisal →Journal of the American College of Clinical Pharmacy : JACCP
Research and Scholarly Methods: Using Artificial Intelligence
Artificial intelligence (AI) is quickly changing health care and expanding research opportunities, including in pharmacy. AI can offer effective solutions for numerous challenges in pharmacy research, such as persistent gaps in evidence-based prescribing, slow drug discovery-to-adoption timelines, complex data environments, and the need for more efficient methods, including qualitative methods. AI tools-ranging from machine learning models to large language models (LLMs)-offer new capabilities for data analysis, prediction, and content generation. Pharmacy researchers can harness these technologies to address persistent gaps in medication management, optimize patient safety and outcomes, and streamline many research workflows. However, successful integration requires understanding the spectrum of AI capabilities, appropriate use cases, and potential limitations. AI must be tailored to the research question, data, and institutional resources. This paper will guide pharmacy researchers in identifying use cases for AI in their research, selecting and applying AI tools, mitigating risks, providing standards for reporting the use of AI in research, and maximizing benefits for scientific discovery and patient outcomes.
1 July 2026
Read appraisal →BMJ supportive & palliative care
Goals of care discussions in hospitalised patients: systematic review of randomised trials.
IMPORTANCE: Structured communication tools and decision aids are recommended to facilitate goals of care discussions (GOCDs) in hospitalised patients, but their impact remains unclear. OBJECTIVE: To determine the effectiveness of communication tools and decision aids on GOCDs between healthcare providers, patients and substitute decision makers. DATA SOURCES: PubMed, Web of Science, CINAHL and ClinicalTrials.gov were searched from January 2015 to July 2025, with backward citation tracking to identify additional trials. STUDY SELECTION: Randomised trials of communication tools or decision aids for hospitalised adults or substitute decision makers were eligible, regardless of primary outcomes. DATA EXTRACTION AND SYNTHESIS: Trial characteristics, primary outcomes and methodological quality were extracted and assessed using the Core Grading of Recommendations, Assessment, Development and Evaluation framework. Random-effects meta-analyses were conducted when appropriate. RESULTS: 22 trials were included. Communication tools increased GOCD documentation (OR 1.46, 95% CI 1.04 to 2.05). Effects on resuscitation preferences were inconsistent (OR 1.84, 95% CI 0.95 to 3.60). Mixed effects were observed for concordant care, psychosocial outcomes and healthcare utilisation. Evidence certainty was moderate for most outcomes; several small pilot trials were rated low due to bias, imprecision or heterogeneity. Interventions were most effective when combining structured patient-facing or clinician-facing prompts such as videos, guides or electronic health record alerts. CONCLUSIONS AND RELEVANCE: Communication tools and decision aids improve GOCD documentation in hospitalised patients but show uncertain effects on other patient-centred outcomes or healthcare utilisation. Interprofessional training and system-level support may enhance impact, and future large trials should evaluate the effectiveness of facilitated GOCDs on important patient and health system outcomes.
1 July 2026
Read appraisal →The Cochrane database of systematic reviews
Prognostic models for predicting intensive care unit admission or mortality in critically ill adults not yet been admitted to the intensive care unit
OBJECTIVES: This is a protocol for a Cochrane Review (prognosis). The objectives are as follows: To determine whether prognostic models can be used for predicting the occurrence of ICU admission and mortality within the index hospital admission, and post-discharge survival at reported follow-up time points, up to one year, in critically ill adults not yet admitted to the ICU. The objective in PICOTS format is as follows. POPULATION: critically ill adults not yet admitted to the ICU Index prognostic model: available prognostic models with or without external validation Comparator: not applicable Outcomes: ICU admission, ICU and hospital mortality, and post-discharge survival Timing: for ICU admission or transfer, ICU mortality, and hospital mortality outcomes, the timing will be in hospital. For post-discharge survival, the timing will be up to 28 days, 1 to 3 months, 6 months, and 1 year after hospital discharge. SETTING: acute hospital wards and emergency care departments.
30 June 2026
Read appraisal →European journal of hospital pharmacy : science and practice
A bibliometric analysis of the application trends of information technology in antimicrobial stewardship within hospitals
OBJECTIVES: This study uses bibliometric analysis to systematically map research trends, knowledge structure and evolution of information technology (IT) in hospital antimicrobial stewardship (AMS) over the last 20 years. METHODS: A Web of Science Core Collection search (2000-2025) yielded 258 English-language publications on IT applications in hospital AMS. A bibliometric analysis, utilising CiteSpace and Bibliometrix, quantitatively evaluated domain evolution through temporal analysis, network mapping, keyword clustering, burst detection and examination of highly cited publications. RESULTS: The bibliometric analysis reveals a fluctuating yet overall increasing trend in annual publications, peaking at 49 articles in 2024. The US (131 publications) and European nations demonstrate significant research output (centrality >0.2), with major collaborative networks coalescing around institutions including Harvard University and Imperial College London. Keyword analysis identifies 'AMS' as a core theme, closely associated with technological keywords such as 'machine learning (ML)', 'clinical decision support systems (CDSS)', 'electronic health records (EHR)' and 'artificial intelligence (AI)'. Emerging trends suggest a shift in research focus from foundational strategies to data-driven prediction of antimicrobial resistance (AMR) and precision interventions. Highly cited literature emphasises the integration of EHR and ML technologies for optimising prescriptions and predicting resistance patterns. CONCLUSIONS: IT-driven AMS research has shifted from empirical management to data science. Despite EHR integration, and ML and CDSS support, challenges remain in data standardisation, technical deployment and ethics. Future work must emphasise global collaboration, standardisation, design refinement and ethical guidelines, and provide clear algorithm explanations to enhance AMR mitigation.
26 June 2026
Read appraisal →BMJ open
Development of phenotype algorithms for the detection of adverse events in electronic health record data: a multicentre study
OBJECTIVES: To develop phenotype algorithms for the detection of adverse events (AEs) or AE-proxies in electronic health records (EHRs), accounting for varying data availability. DESIGN: Multicentre study conducted as part of the Use Case POLAR_MI (POLypharmacy, drug interActions, Risks) of the German Medical Informatics Initiative (MII). SETTING: Germany. PARTICIPANTS: Multidisciplinary teams from 10 German university sites within the MII. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Literature- and consensus-based development and operationalisation of AE algorithms using structured EHR data, including a standardised, multicentre expert review process. Data categories used: International Classification of Diseases, 10th Revision (ICD-10) codes for diagnoses; Anatomical Therapeutic Chemical (ATC) codes and 'Pharmazentralnummern' (PZN; German eight-digit identification code for pharmaceutical products) for medications (used in the treatment of AEs); Logical Observation Identifiers Names and Codes (LOINC) for laboratory values and medical findings; and 'Operationen- und Prozedurenschlüssel' (OPS; German procedure classification) codes for medical and surgical procedures. RESULTS: We developed 82 algorithms for 48 AEs. Algorithms for the same AE varied by data categories or code selections. At the AE level, 31 AEs were covered exclusively by newly developed algorithms, and 17 AEs by at least one modified algorithm.Overall, 52 algorithms were based on a single data category, while 30 required multiple categories. ICD-10 codes were most commonly used (n=65 AE algorithms), followed by LOINC (n=27), ATC codes (n=18), OPS codes (n=11) and PZN (n=2). All phenotype algorithms were semantically modelled and can be executed using the publicly available Terminology- and Ontology-based Phenotyping (TOP) Framework, which supports export in various formats. CONCLUSION: We present a peer-reviewed set of algorithms for a large number of AEs, which can be implemented in structured routine electronic data sources and (pending validation studies) may support pharmacoepidemiologic research. The algorithms will be implementable across all 39 participating sites of the German MII. As a next step, we will empirically validate the algorithms against all information (including free text) contained in EHRs. TRIAL REGISTRATION: Not applicable.
24 June 2026
Read appraisal →NEJM evidence
A Randomized Trial of Interval Likelihood Ratios for Clinical Decision-Making
BACKGROUND: Dichotomized reporting of diagnostic test results may oversimplify test characteristics. Interval likelihood ratios (ILRs), test characteristics across a range of values, offer a nuanced alternative, but their impact on decision-making is uncertain. We assessed whether ILRs improve clinical decisions compared to two traditional formats: single-threshold and binary test characteristics. METHODS: We conducted a multicenter randomized trial of internal medicine residents, attending physicians, and advanced practice providers. Participants were presented with two fictional vignettes (cases involving pulmonary embolism diagnosis and heart failure treatment), each with a clearly correct response centered on a key diagnostic test result. The vignettes were identical except for participants' random assignment to one of three test characteristic presentations: (1) single threshold, (2) binary test characteristics, or (3) ILRs. We compared the correct management decision rates between the ILR and threshold groups and between the ILR and binary groups. Secondary objectives included comparing threshold and binary groups and subgroup analyses by case vignette. RESULTS: The trial enrolled 611 participants. The correct decision rate was 93.4% in the ILR group compared with 82.2% in the threshold group (difference of 11.2 percentage points; 95% confidence interval [CI], 6.9-15.6, P<0.001) and compared with 75.1% in the binary group (difference of 18.3 percentage points; 95% CI, 13.5-23.1, P<0.001). In the pulmonary embolism case, the correct response rate was 91.9% in the ILR group, 70.9% in the threshold group, and 64.7% in the binary group. In the heart failure case, the correct response rate was 94.9% in the ILR group, 93.4% in the threshold group, and 85.6% in the binary group. CONCLUSIONS: Presenting diagnostic test information using ILRs improved vignette-based clinical decisions. The magnitude of improvement appeared to vary by case vignette.
24 June 2026
Read appraisal →European journal of emergency medicine : official journal of the European Society for Emergency Medicine
Association between patient-to-nurse ratios in emergency departments and patient, staff, and organizational outcomes: a systematic review.
Background In many settings, studies have shown that establishing patient-to-nurse ratios has benefits for both patients and staff. Whether this is transposable to emergency department (ED) is unclear. Objective The primary objective was to assess the association between patient-to-nurse ratios in EDs and mortality. Design In this systematic review, a search strategy identified studies published in 2004-2025 in MEDLINE, Cochrane, and CINAHL. Two independent reviewers performed selection and data extraction, followed by a narrative synthesis. Outcomes The primary outcome was in-hospital mortality. Secondary outcomes were other patient outcomes, staff, and organizational outcomes. Results Of 2214 identified references, 17 studies were included and all of which were observational. The primary outcome was assessed in only one cohort study ( n = 73 EDs), where an increase in the number of nurses per patient was associated with a reduction in ED mortality (adjusted odds ratio = 0.68; 95% confidence interval: 0.49; 0.94). An increase in the number of nurses per patient was also associated with a reduction in cardiac arrests (one study: relative risk = 1.33, 95% confidence interval: 1.05, 1.67 for ≥8.5 patients/nurse vs. <8.5) and medication errors (one of two studies), and an improvement in patient experience (two studies) and nurse satisfaction (one study). Mixed results were observed with regard to reducing length of stay (one of two studies), administration time (three of four studies) and the number of patients who left without being seen (three of four studies), while waiting time before diagnosis or treatment improved (three studies). Conclusion Although evidence is limited, several observational studies suggest that a higher nurse-to-patient ratio is associated with better patient, nurse, and ED organizational outcomes.
23 June 2026
Read appraisal →JMIR nursing
Association Between Electronic Health Record-Based Nursing Workload and Turnover: Retrospective Cohort Study
BACKGROUND: Nurse turnover remains a major challenge for health systems, yet objective, scalable measures of workload that predict turnover are limited. Electronic health record (EHR) audit logs offer a potential data source to quantify nursing work patterns. OBJECTIVE: This study aimed to evaluate the association between EHR-derived measures of nursing workload and turnover among inpatient nurses. METHODS: We analyzed work-related activities of staff nurses from medical and surgical inpatient units at a large academic medical center from January 1, 2022, to December 31, 2022. Data included deidentified demographics (age, sex, years since licensure, and service group), shift characteristics (number of shifts worked, proportion of night shifts, timing, and location), and measures of work activities derived from EHR audit logs. Audit logs were used to develop measures related to nurse workload, including (1) nurse activities (information review, medication administration, alert management, navigation, documentation, and communication), (2) patient load (based on the number of unique patient charts accessed), and (3) cognitive load (based on the number of patient switches). For nurses who left, we excluded the 6 weeks immediately preceding termination (washout period) and measured workload during the preceding 6-week period; for those who remained, a random 6-week working period was selected. Associations between workload measures and turnover were assessed using mixed-effects logistic regression, adjusting for demographics and shift-related characteristics. RESULTS: Among 432 nurses (n=363, 84% female; median age 27, IQR 23-36 years), contributing 6812 shifts and approximately 13 million audit log actions, 84 (19%) left the institution in 2022. A higher proportion of medication administration actions was associated with higher odds of turnover (odds ratio [OR] 2.20, 95% CI 1.36-3.54), whereas greater alert engagement (OR 0.48, 95% CI 0.32-0.72) and more years since licensure (OR 0.57, 95% CI 0.38-0.84) were associated with lower odds of turnover. CONCLUSIONS: EHR-derived workload measures, particularly greater medication administration burden and lower alert engagement, were independently associated with the risk of nurse turnover. Patterns of EHR use may help identify nurses at higher risk of leaving an institution and can potentially inform targeted workforce retention strategies.
13 June 2026
Read appraisal →BMC geriatrics
Components and effects of psychosocial support interventions for relatives of patients with delirium in hospitals, systematic review
BACKGROUND: Relatives of patients with delirium are often confronted with patients' challenging behaviours such as hyperactivity, aggression, or hallucinations. As a result, feelings of anxiety and depression episodes have been described. The aim is to identify psychosocial support intervention for relatives of patients with delirium in hospitals, describe components and effects, and identify research gaps. METHODS: Systematic review based on PRISMA, including psychosocial support intervention studies addressing burden and support needs of adult relatives of hospital patients with delirium. We searched the databases Medline, Web of Sience, CINAHL, Livivo, PsycINFO, and Cochrane Library in 07/2024 and reference lists. We assessed reporting using TIDieR and CReDECI2 and methodological quality using ROB-1 and the JBI tool for quasi-experimental studies. We performed narrative syntheses. RESULTS: We identified eight intervention studies addressing relatives of patients with delirium in different settings. Intervention components included provision of information using flyers, videos, web-sites, or telephone calls, and mentoring in the intensive care unit. Reporting of interventions was mostly poor. No study reported a process evaluation. Four of six studies showed a significant difference in relatives' delirium knowledge. For relatives' psychological outcomes, no clear effects were shown. Overall, study quality was limited. CONCLUSIONS: More effectiveness studies are needed with better reporting of intervention components, ideally addressing psychological aspects. Feasibility and acceptance of new interventions should be described within accompanying process evaluations. TRIAL REGISTRATION: https://osf.io/7jbsk .
13 June 2026
Read appraisal →BMJ open
Prediction of ICU length of stay, hospital discharge outcomes and discharge location among ICU-admitted patients diagnosed with viral hepatitis using machine learning: a retrospective cohort study of the MIMIC-IV database
BACKGROUND: Hepatitis, a disease characterised by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million deaths annually, with hepatitis B and C accounting for many of these fatalities. Intensive care unit (ICU) management of patients is particularly challenging due to the complex clinical care and resource demands. Despite advancements in ICU predictive analytics, limited research has specifically addressed hepatitis patients, creating a gap in optimising care for this population. METHODS: This study focuses on predicting ICU length of stay (LoS), hospital discharge outcomes and discharge location for ICU-admitted viral hepatitis patients using a comparative assessment of machine learning (ML) models. Leveraging data from the Medical Information Mart for Intensive Care-IV database, which includes around 94 500 ICU patient records, this study uses sociodemographic details, clinical characteristics and resource utilisation metrics to develop predictive models such as Random Forest, Logistic Regression, Gradient Boosting Machines and Generalised Additive Model with Negative Binomial Regression. RESULTS: Among 3875 ICU-admitted hepatitis patients, Random Forest classification outperformed Logistic Regression in predicting discharge outcomes, achieving higher accuracy (0.87 vs 0.82) and greater discriminative ability (area under the receiver operating characteristic curve 0.95 vs 0.89). For ICU LoS prediction, Random Forest regression applied to log-transformed LoS demonstrated strong performance (R² up to 0.82), while the generalised additive model with negative binomial distribution explained approximately 76% of LoS variance. Prediction of discharge location yielded moderate performance across Gradient Boosting and multinomial logistic regression models (accuracy 0.55 and 0.56), reflecting challenges associated with multi-class imbalance. Variable importance analyses across ML models consistently identified medication counts, procedure counts, comorbidity burden, age, race and total LoS as the most influential predictors of discharge outcomes and discharge location. CONCLUSIONS: This study demonstrates the value of ML models for predicting clinical outcomes for hepatitis patients, including ICU LOS and hospital discharge status. The results underscore the influence of factors like race and age, revealing disparities that must be addressed in predictive care strategies. While the models show promise, challenges such as variability in prolonged stays and limited multiclass prediction accuracy point to the need for ongoing refinement and research.
11 June 2026
Read appraisal →PLoS medicine
Characterization of the VHH-Fc construct rimteravimab in healthy adults and patients hospitalized for mild-to-moderate COVID-19: Two Phase 1 randomized clinical trials
BACKGROUND: Variable Heavy domain of Heavy chains (VHH) are innovative tools to target unique epitopes, yet few have been developed as heavy chain-only antibodies for clinical use. Rimteravimab (referred to here as XVR011) is a humanized antibody developed for the treatment of mild-to-moderate coronavirus disease 2019 (COVID-19), consisting of two identical VHHs targeting the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, with a human immunoglobulin (Ig) G1 fragment constant of antibody (Fc), silenced for Fc effector functions. We conducted two Phase 1 studies in healthy volunteers or hospitalized COVID-19 patients to evaluate its safety, tolerability, pharmacokinetics and immunogenicity. METHODS AND FINDINGS: A randomized, double-blinded, single-center, placebo-controlled, single ascending dose study was performed in healthy volunteers (Phase 1a, EXEVIR0102, EudraCT 2021-003707-17), in parallel to an open-label, multi-center, single ascending dose study in patients hospitalized for mild to moderate COVID-19 (Phase 1b, EXEVIR0101, EudraCT 2020-005299-36, NCT04884295). Participants received a single intravenous infusion of 250, 500 or 1,000 mg of XVR011. The primary objective for both trials was the safety and tolerability of XVR011. Pharmacokinetics were evaluated as a secondary objective in Phase 1a and as an exploratory objective in Phase 1b. Efficacy (evaluated as respiratory parameters and COVID-19 clinical status) and antiviral activity in patients were evaluated as a secondary objective in Phase 1b. Immunogenicity was evaluated as an exploratory objective. Part 2 of the EXEVIR0101 study (initially a phase 1b/2 study) was not conducted due to the loss of XVR011 potency against SARS-CoV-2 Omicron BA.2. Demographics, safety, efficacy, and immunogenicity were analyzed using descriptive statistics, while pharmacokinetics were analyzed with noncompartmental pharmacokinetics (PK) modeling. In the Phase 1a study, there were no infusion-related reactions, serious treatment-emergent adverse events (TEAEs) or TEAEs grade ≥3. 22/30 volunteers (73.3%) reported 53 TEAEs (49 Grade 1, 4 Grade 2) with none being related to XVR011. The most common TEAE was headache (n = 8, 26.7%) in various treatment groups. In the Phase 1b study, 27 hospitalized patients were enrolled, and followed up to 30 days. Seven patients (25.9%) reported a total of 15 TEAEs, the majority (80%) being mild to moderate (Grade 1-2). There were no treatment-related serious TEAEs. All TEAEs resolved by the end of the study. Peak exposure (maximal concentration, Cmax) and systemic exposure (area under the curve, AUC0-t, and AUC0-inf) for XVR011 increased dose-proportionally. Geomean half-life ranged from 15.4 to 17.0 days in Phase 1a, while individual half-life ranged from 11.4 to 15.6 days in Phase 1b. SARS-CoV-2 viral load, as detected in nasopharyngeal samples by reverse transcription and quantitative polymerase chain reaction (RT-qPCR), decreased similarly in all cohorts compared to baseline. No treatment-induced anti-drug antibodies (ADA) were detected in Phase 1a. In Phase 1b, higher XVR011 concentrations increased the likelihood of ADA formation, without impacting pharmacokinetics and pharmacodynamics. No obvious dose-response in COVID-19 clinical status or respiratory parameters was observed. Technological limitations included study size, absence of placebo for the Phase 1b, absence of repeated dosing, evolving SARS-CoV-2 variants and standard-of-care. CONCLUSIONS: XVR011 displayed a favourable safety, tolerability, pharmacokinetics, and immunogenicity profile, both in healthy volunteers and in patients hospitalized for mild to moderate COVID-19. These data pave the way for the design and clinical development of VHH-Fc constructs.
31 May 2026
Read appraisal →JMIR medical informatics
The Regional Implementation of an Electronic Health Record-Integrated Ambient Scribe in Primary and Secondary Care in England: Real-Time Qualitative Evaluation
BACKGROUND: There is significant potential for ambient scribe technology to enhance health care productivity, with a growing range of applications being developed and implemented internationally. Strong organizational drivers to improve efficiency, coupled with the technology's potential to help address clinician burnout, are accelerating interest and adoption. However, limited attention has been paid to the integration of such systems within electronic health records and unintended consequences, as stakeholders navigate their implementation and integration into clinical practice. OBJECTIVE: This study therefore aimed to explore the processes involved in implementing and adopting ambient scribe technology across diverse health care settings. METHODS: We conducted a real-time, longitudinal qualitative evaluation of a pilot implementation of an integrated ambient scribe system across National Health Service primary care and secondary hospital settings within a care system in the Midlands region of England. Data collection involved in-depth one-to-one interviews conducted in several phases: an initial scoping study to identify key interests and stakeholders for inclusion, followed by an implementation study with participants involved in the pilot. We also conducted 16 hours of nonparticipant observations of consultations. The implementation study gathered data at 2 time points, before implementation and 3 to 4 weeks after, to capture experiences, changes, and emerging impacts over time. RESULTS: We collected data from 45 individuals. Use cases varied across settings and shaped how ambient scribe systems were deployed. Differences between general practice and secondary care in documentation purpose, format, and workflow created challenges for developing and validating templates, though the technology showed flexibility across contexts. Automated note-taking often improved patient interaction but required clinicians to adjust how they spoke to ensure the technology captured their reasoning. Outputs were sometimes generic, reinforcing defensive documentation and reducing personal tone or contextual recall. Integration pathways carried distinct trade-offs: stand-alone systems (which were used by many stakeholders in our study) were easier to adopt but offered limited long-term benefits, while integrated systems required greater effort and standardization, yet promised improved efficiency and safety. CONCLUSIONS: The ambient scribe market remains immature and volatile, creating strategic uncertainty for health systems. Careful procurement approaches are needed to balance the risks and benefits of integrated versus stand-alone systems, while aligning user demand with organizational needs for integration.
31 May 2026
Read appraisal →JMIR formative research
Prediction of 30-Day All-Cause Hospital Readmissions Using Limited Structured Electronic Health Record Data: Retrospective Comparative Study
BACKGROUND: Unplanned hospital readmissions represent a critical operational and financial challenge for health care systems in the United States, with 3.8 million 30-day all-cause readmissions in 2018 at an average cost of US $15,200 each, totaling US $58 billion in costs. Many published prediction models rely on comprehensive information (eg, full billing abstractions, discharge summaries, laboratory tests, and vitals) that becomes available only late in the encounter, limiting usefulness for real-time, in-hospital intervention. This creates a timeliness-accuracy trade-off: models that are most accurate retrospectively may arrive too late to act upon. OBJECTIVE: This study tests whether a clinically meaningful predictive signal for 30-day all-cause readmission is present within a limited set of structured clinical codes recorded during the patient's hospital stay. This approach evaluates whether predictive signals are retained when using a restricted set of structured clinical codes. METHODS: We conducted a retrospective comparative modeling study using a large, deidentified electronic health record dataset of 50,000 inpatient encounters from the 2019 New York State Emergency Department Database. Two feature sets were constructed: (1) a limited set consisting of the first 5 ICD-10 (International Classification of Diseases, 10th Revision) diagnosis codes, the first 5 Current Procedural Terminology (CPT) codes, and Charlson Comorbidity Index (CCI; 11 input features); and (2) a rich set using all available ICD-10 and CPT codes plus CCI (up to 135 input features). We trained 4 models: random forest, CatBoost, multilayer perceptron, and DistilBERT (a distilled Bidirectional Encoder Representations from Transformers [BERT] model; structured codes mapped to text and tokenized with DistilBERT-base-uncased). Evaluation used an untouched hold-out set of 10,000 encounters, preserving the natural 21.1% readmission prevalence. Primary metrics were area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. To address class imbalance, the training split only was balanced via undersampling of the majority class and bootstrap oversampling of the minority class; validation/test distributions were left unchanged. RESULTS: Models trained on the limited feature set achieved AUROC values ranging from 0.5369 to 0.5596 and F1-scores from 0.2555 to 0.3434. Across 3 of 4 architectures, models trained on the limited feature set matched or exceeded the discrimination of their rich counterparts. The best model (random forest, limited) achieved an area under the curve AUROC 0.5596 (95% CI 0.5440-0.5739) compared to the best performing rich model (DistilBERT) at 0.5703 (95% CI 0.5565-0.5842), an absolute difference of 0.0107. The highest F1-score (0.3434) was achieved by DistilBERT on the limited feature set. Differences across architectures were small in absolute terms, with threshold-dependent metrics (eg, F1-score) being comparable. CONCLUSIONS: The findings suggest that models using a limited set of structured clinical codes can achieve performance comparable to those using more comprehensive coding information.
25 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
Electronic Patient Records, Real-Time Clinical Documentation and Burden: An Umbrella Review
This umbrella review synthesises evidence from reviews on clinicians' documentation burden (DB) associated with electronic patient records (EPRs) in hospital and primary care settings. Guided by Donabedian's model (structure, process, outcomes), it maps causes of burden, barriers to real-time documentation, and technological solutions. Following the Joanna Briggs Institute (JBI) methodology, searches across six databases identified 2,739 records, with 166 under full-text review. Findings of this review will inform strategies to optimise real-time EPR use while minimising DB.
24 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Mycobacterium tuberculosis Transmission in Healthcare Settings: A Systematic Review of Frequency, Risk Factors, and Exposure Conditions
BACKGROUND: Uncertainty regarding factors that govern Mycobacterium tuberculosis transmission in healthcare settings leads to broad and variable exposure definitions and low-yield contact investigations. We systematically reviewed data on M. tuberculosis transmission in healthcare to inform risk stratification and exposure definitions. METHODS: We searched MEDLINE, Embase, CINAHL, and Cochrane databases from inception through December 2024 for studies describing M. tuberculosis transmission from adult patients to healthcare personnel (HCP) or patients. We tabulated data on transmission frequency, circumstances, and risk factors. RESULTS: Of 6695 studies screened, 86 met inclusion criteria, encompassing 1083 source patients, 35 698 exposed individuals, and 2517 secondary cases (7.1% overall transmission risk, 3.8% among studies published since the US Centers for Disease Control and Prevention last updated healthcare ventilation standards in 2005). Transmission was associated with prolonged contact between HCP and source patients (median duration 24 hours, interquartile range [IQR] 24-25 hours) and between patients in shared rooms (median duration 24 hours, IQR 12-24 hours), clinical care without N95 respirator (1232 transmissions/10 149 exposures that included data on face covering use, 12.1%), frequent direct care (185 transmissions/410 exposures, 45.1%), and lack of negative pressure ventilation (1906 transmissions/5670 exposures, 33.6%). Only 8/116 (6.9%) transmissions with documented exposure durations were associated with <8 hours of cumulative exposure and all in poorly ventilated spaces. CONCLUSIONS: Mycobacterium tuberculosis transmission in healthcare is uncommon, particularly in spaces that meet current healthcare ventilation standards. Risk factors for transmission include prolonged exposure, close contact, lack of respirators, and poor ventilation. Defining exposures as >8 hours without a respirator should identify most transmissions and could improve the efficiency of contact investigations.
22 May 2026
Read appraisal →JMIR formative research
Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study
BACKGROUND: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a shortage of nursing staff, it is essential to identify the early warning signs of adverse conditions among children as quickly as possible. Current targeted care needs to be improved. OBJECTIVE: This study aimed to investigate the application of an artificial intelligence (AI)-based version of the Pediatric Early Warning Score (PEWS) in a pediatric emergency observation unit, analyze the relationship between PEWS and disease severity, and assess its impact on the length of hospital stay and hospitalization costs after admission, thereby providing a reference for targeted nursing care. METHODS: We performed a retrospective study. A total of 1233 pediatric patients admitted via the pediatric emergency department of a tertiary specialty hospital in Guangzhou from September 2023 to March 2024 were included. The patients were divided according to whether they triggered a PEWS early warning into an early warning group (PEWS ≥1) and a non-early warning group (PEWS=0) during emergency observation. Length of stay and hospitalization costs were compared between the early warning group and the non-early warning group. Differences between groups were assessed using the Mann-Whitney U test. We performed multivariable logistic regression to discuss the association of resource use metrics and PEWS status, adjusted by age, sex, and disease category (respiratory, neurological, and hematologic). RESULTS: Of 1233 patients, 597 (48.4%) triggered the PEWS early warning (mean score 2.44, SD 1.41), and 636 (51.6%) did not. In the early warning group, 68 children were transferred to the intensive care unit, with a mean PEWS of 3.32 (SD 1.73). Compared with the non-early warning group, the early warning group had a longer hospital stay (z=-5.180; P<.001) and higher hospitalization costs (z=-6.500; P<.001), and the differences between groups were statistically significant (P<.001). Among the top 3 admission categories-respiratory, neurological, and hematologic diseases-children in the early warning group had significantly longer hospital stays and higher hospitalization costs (all P<.01). The β coefficient for length of hospital stay was 0.053 (SE 0.010; Wald χ²1=5.533; odds ratio 1.055, 95% CI 1.035-1.075), while the β coefficient for hospitalization costs was 0.001 (SE 0.000; Wald χ²1=6.075; odds ratio 1.001, 95% CI 1.001-1.001). CONCLUSIONS: Compared with the non-early warning group, the early warning group had significantly longer hospital stays and higher hospitalization costs (P<.001); similar patterns were observed within respiratory, neurological, and hematologic disease categories (all P<.01). These findings show differences between children who triggered the warning and children who did not, providing a reference for identifying critically ill children for targeted care.
21 May 2026
Read appraisal →BMJ open
Evaluation of a deep learning model applied to chest X-rays of patients with suspected pneumonia presenting to the emergency department designed to predict admission risk: a retrospective training and prospective non-intervention validation.
OBJECTIVES: To develop, train and test a deep learning model Image-based PROgnostication applied to Chest X Rays (IPRO-X) tool that predicts the inpatient (IP) admission risk in patients with suspected pneumonia presenting to the emergency department (ED). DESIGN: The study consists of a retrospective (training) and prospective non-interventional shadow deployment (validation) of a deep learning model. SETTING: Three-hospital tertiary care system with two EDs. PARTICIPANTS: Consecutive adult patients (18 years old or older) who presented to the ED from December 2022 to February 2023 with a chief clinical complaint potentially related to pneumonia were reviewed for eligibility (n=5567), with a final number of 3677 included in the validation study. PRIMARY AND SECONDARY OUTCOME MEASURES: IPRO-X was developed using standard two-dimensional convolutional neural network InceptionNet architecture and processes chest radiographs (CXRs) acquired at admission generating a continuous value from 0 (IP-negative) to 1 (IP-positive). We examined IPRO-X's ability to predict IP admission using accuracy, area under the curve (AUC), receiver operating characteristic, sensitivity, specificity, positive predictive value and negative predictive value. IPRO-X scores were compared against observed outcomes (admission and discharge) to determine clinical utility in an ED setting. Four thresholds were defined from the retrospective phase: Youden optimal, highest specificity and thresholds equivalent to 30-day mortality rates of Pneumonia Severity Index (PSI) Risk class IV and Confusion, Respiratory Rate, Blood Pressure and Age 65 or older Score (CRB-65) scores 1 and 2. Performance was also analysed per group of chief complaints. RESULTS: In the validation set, 3677 patients (1777 (48%) female, median age 56 years (min 18, max 99)) were included. IPRO-X predicted IP admissions with an AUC of 0.795, and in the pneumonia-specific chief complaints, AUC increased to 0.828, whereas in non-related pneumonia chief complaints, the AUC decreased to 0.755. IPRO-X score was significantly higher in admitted patients compared with discharged patients (p<0.001). The accuracy of IPRO-X when using the PSI-anchored threshold was 0.751 and with the CRB-anchored threshold was 0.729, with a specificity of 0.803 and 0.968, respectively. CONCLUSIONS: IPRO-X applied to CXR of patients with signs and symptoms commonly related to pneumonia in the ED can accurately predict IP admission.
21 May 2026
Read appraisal →BMJ quality & safety
Retrospective validation study of a large language model approach to screening for intraabdominal surgical site infections for quality and safety reporting
Surgical site infections (SSIs), particularly intra-abdominal (IAB) infections, are challenging to identify and remain a resource-intensive focus of infection prevention programmes. Current automated screening measures rely on discrete data from the electronic health record (EHR), such as microbiology results, diagnosis codes and/or return to the operating room. This approach has poor specificity, and therefore surveillance methods depend heavily on additional manual chart review by trained infection preventionists. Large language models (LLMs) offer an opportunity to improve surveillance by synthesising complex clinical documentation alongside structured data elements.We evaluated the performance of a locally hosted LLM (gpt-35-turbo-16k) to improve IAB SSI screening using perioperative clinical notes and microbiology results. The model analysed documentation across the perioperative period (3 days before through 30 days after surgery) to generate case-level SSI summaries and likelihood assessments. We compared the performance of this tool against the current EHR-based screening workflow.Among 1977 abdominal surgical cases, including 56 with confirmed IAB SSIs, the LLM screened 104 cases as high risk, identifying all infections (negative predictive value (NPV) 100%) and achieving a positive predictive value (PPV) of 53.8%. In contrast, the EHR-based workflow identified 288 cases for further review, with a PPV of only 19.4% and the same NPV of 100%. Analysis of 57 224 notes required ~107 million tokens, translating to approximately USD 0.05 per case.An LLM-based approach to SSI surveillance has the potential to substantially improve efficiency while remaining highly accurate and cost-effective. By reducing reliance on manual chart review, this strategy could allow infection preventionists to shift their attention from surveillance toward quality improvement and patient safety initiatives.
20 May 2026
Read appraisal →BMJ open
Prediction of inpatient falls and key predictors using machine learning applied to electronic health records: a retrospective cohort study in a tertiary hospital in Türkiye
OBJECTIVE: Traditional fall risk tools are often inaccurate and burdensome. This study aims to develop a predictive model for inpatient falls and identify the most influential variables using machine learning applied to electronic health record data. DESIGN: A retrospective cohort study. SETTING: A large tertiary university hospital in Türkiye. PARTICIPANTS: Adult patients (≥18 years) hospitalised in a university hospital between January 2017 and June 2023. PRIMARY OUTCOME MEASURES: Occurrence of inpatient falls recorded in incident reporting systems. RESULTS: A total of 518 fallers were identified and compared with 3121 non-fallers. Fallers were significantly older (median 68.5 vs 64 years, p<0.001), had longer hospital stays (16 vs 12 days, p<0.001) and higher comorbidity burden (p<0.001). They were also more likely to receive high fall-risk medications (86% vs 76%, p<0.001) and had a higher prevalence of mental disorders (26.3% vs 17.2%, p<0.001). The random forest quantile classifier demonstrated the highest discrimination (area under the curve (AUC) 0.821, 95% CI 0.781 to 0.861), with balanced sensitivity (0.799, 95% CI 0.734 to 0.867) and specificity (0.722, 95% CI 0.691 to 0.755). Pairwise comparisons using DeLong's test showed significantly higher AUC compared with several models (adjusted p=0.001), while differences with eXtreme Gradient Boosting were not significant (p=0.240). Key predictors included comorbidity burden, age, number of fall-risk-increasing medications and laboratory variables such as eosinophil and basophil counts, blood urea nitrogen, sodium and red blood cell count. CONCLUSION: Machine learning models using electronic health data can predict inpatient falls and reveal key risk factors. The random forest quantile classifier offers a promising approach for improving fall risk prediction in imbalanced clinical datasets.
15 May 2026
Read appraisal →Neurology. Clinical practice
Optimizing Antiseizure Medication Taper in the Epilepsy Monitoring Unit
BACKGROUND AND OBJECTIVES: Inpatient video-EEG epilepsy monitoring aims to provide a safe environment for antiseizure medication (ASM) tapering to precipitate seizures, which is often required for diagnosis and treatment. However, there is no standardized taper protocol across institutions, which may contribute to increased morbidity through longer hospitalizations if the taper is too mild and bilateral tonic-clonic seizures (BTCSs) if too aggressive. This study investigates how ASM taper influences length of stay (LOS) and patient outcomes in the epilepsy monitoring unit (EMU), aiming to develop guidelines for medication taper strategies to maximize diagnostic yield while minimizing patient risk. METHODS: We used natural language processing to extract data from electronic medical records of 639 patients admitted to the University of Pennsylvania EMU between 2017 and 2023. We estimated ASM load, the total normalized sum of all ASM serum concentrations based on dose-dependent pharmacokinetic models, throughout each hospitalization. We applied LASSO (Least Absolute Shrinkage and Selection Operator) regression and mixed-effects models to identify predictors of LOS and BTCS risk as a function of taper speed and percentage of baseline ASM load in patients with (hBTCS) and without (nBTCS) a history of BTCS. RESULTS: Taper speed did not correlate with BTCS risk, although absolute ASM load did. Median ASM loads at seizure occurrence were 51% of baseline in the hBTCS group, 41% in the nBTCS group, and 39% in the unclear group. BTCS occurred at higher ASM loads in patients with hBTCS vs nBTCS. Median ASM loads at first EMU seizure were 33% for both hBTCS and nBTCS groups, while the unclear group had first seizures at lower loads (25% of baseline). Time to first seizure and number of baseline ASMs were positively correlated with longer hospital stays, suggesting that sequential medication taper delayed seizures and prolonged LOS without altering BTCS risk. DISCUSSION: Patient history and ASM load are critical factors in predicting BTCS. Rapid taper to approximately 50% baseline ASM load may more efficiently produce seizures without increased BTCS risk in patients without BTCS history. Individualized taper protocols remain advised, particularly for severe epilepsies. This study provides a foundation for quantitative, evidence-based guidelines to optimize ASM reduction strategies for safely precipitating seizures in EMUs.
15 May 2026
Read appraisal →JMIR nursing
Exploring the Impact of Electronic Medical Record-Enabled Versus Paper-Based Systems on the Quality of Nursing Handover: Comparative Case-Study
BACKGROUND: Ineffective clinical handover has the potential to compromise patient safety and quality of care. Standardizing the handover process is a widely adopted improvement strategy intended to reduce failures of information transfer. By enabling real-time access to patient information, electronic medical records (EMRs) could address communication issues inherent to nursing handover. OBJECTIVE: This case study sought to compare the quality of nursing handover occurring at EMR-enabled sites with that occurring at paper-based sites, within a single Australian public health service. METHODS: A comparative case study design was used, using quantitative data collected from observational audits of 60 handovers and posthandover surveys conducted in EMR-enabled and paper-based ward environments. Handover quality was measured through compliance with the organization's Clinical Handover Standard and staff-reported perceptions, enabling comparison between cohorts. RESULTS: Compared with paper-based wards, EMR-enabled wards demonstrated more consistent communication of clinical alerts and risks and fewer interruptions, whereas paper-based wards showed higher rates of bedside handover and patient engagement. CONCLUSIONS: EMR implementation alone does not ensure high-quality nursing handover. EMR interface design and functionality may act as a barrier to bedside handover and patient engagement, and contribute to continued reliance on paper-based artifacts. Targeted EMR design and nursing informatics-led optimization are required to better support nursing handover as a complex and cognitively challenging communication process.
14 May 2026
Read appraisal →JMIR medical informatics
Expert Evaluation and Consensus on GPT-4o Summaries of Clinical Letters: Validation and Results of the Framework and Implementation of AI Tools Project
BACKGROUND: Large language models (LLMs) are increasingly used to summarize clinical documents; yet, automated metrics often inadequately capture clinical relevance and safety. In the initial phase of the "Framework and Implementation of AI Tools," an expert-driven, cocreated evaluation methodology was established to assess LLM-generated discharge letter summaries, combining prompt design considerations with intuitive expert appraisal. OBJECTIVE: This study aimed to quantify expert agreement and interrater reliability on LLM summaries of discharge letters, identify frequent and clinically relevant errors, and evaluate practical implications for integrating LLMs into documentation workflows. METHODS: Thirty expert-curated synthetic Dutch discharge letters were summarized. Thirty-one clinicians from Flemish care settings (1 university hospital, 2 private hospitals, and 2 general practice circles) evaluated the summaries. The evaluation framework consisted of 61 binary layout items assessing whether required sections and formatting were correctly present, 33 content items (correct or complete vs incorrect, subcategorizing missing, irrelevant, and hallucinated information), a 4-point global quality rating, and an open comment. Statistical analyses included descriptive statistics, mixed effects ordinal regression on the global score, consensus (agreement per question or letter) percentages, interrater reliability (Cohen κ, intraclass correlation coefficient [ICC], Fleiss κ, and prevalence index), and thematic synthesis of comments. RESULTS: Layout adherence was high (88%), especially in the conclusion section. The positive response rate for content was overall moderate (78%), with the best performance observed in the medical history section and the lowest performance observed in the medication section, which also showed the highest rate of hallucinations and the weakest interrater consensus. Across all sections, missing information was the most common error. Nearly 70% of global ratings were "good" or "very good." Higher positive response rates for content predicted better global scores (β=.079; P<.001), while layout and participant specialty were not relevant to global scoring. Consensus was high for the layout questions (median 96.8%, IQR 90.2%-100%) and somewhat lower for content (median 83.9%, IQR 67.7-96.8), with the lowest agreement in the medication section. Interrater agreement was moderate (median Cohen κ=0.36, IQR 0.29-0.43; range 0.07-0.56), but overall reliability was high (ICC 0.945, 95% CI 0.942-0.948), indicating strong consistency at the global level despite interrater variability. The prevalence index demonstrated that high ICC values were partly driven by the strong prevalence of affirmative responses in layout items, while content items showed more balanced distributions and lower agreement. CONCLUSIONS: Our framework offers a robust approach for evaluating LLM-generated discharge summaries, balancing usability and clinical relevance. Semantic integrity, especially regarding medication details, was identified as a key vulnerability. Perceived overall quality was driven by a positive response rate for content. High ICC values for global score, with lower item-level agreement lead toward the need for clearer, context-specific prompts and standardized evaluation criteria to reduce interrater variability. Human oversight and targeted automated checks for omissions and hallucinations are essential for safe clinical deployment.
14 May 2026
Read appraisal →Journal of medical Internet research
AI-Based Automation for Medication Reconciliation: Scoping Review
BACKGROUND: Medication reconciliation (MedRec) has the potential to improve patient safety by enhancing the continuity of medication information across settings. MedRec involves 3 core tasks: the creation of a best possible medication history, the identification of medication discrepancies among medication lists, and the resolution of medication discrepancies. While artificial intelligence (AI) has the potential to improve MedRec, existing reviews have not identified the ways in which researchers have used AI to facilitate MedRec tasks and their constituent subtasks or the level of automation achieved. OBJECTIVE: This scoping review aimed to map how previous research has applied AI to MedRec tasks and subtasks and assess the extent of automation achieved. METHODS: We searched MEDLINE, Embase, Web of Science, IEEE Xplore, and Compendex in June 2024 for studies that used AI to support a MedRec task or subtask, excluding entirely rule-based tools or studies focused on other aspects of medication management. After screening 2345 unique records, we conducted backward citation searching of studies included at the full-text stage, identifying an additional 795 unique records. We used a 4-stage model of human information processing as a structural lens to guide our considerations of automation, mapping the core tasks of MedRec onto this model. RESULTS: A total of 94 studies met the inclusion criteria. All studies addressed subtasks related to the creation of a best possible medication history. Only 2.1% (n=2) of the studies also addressed the identification of discrepancies. Thus, the highest stage of automated information processing achieved was information analysis, although most studies (92/94, 97.9%) only automated information acquisition steps. Most studies (67/94, 71.3%) used free-text clinical notes from the electronic health record, although a significant proportion (21/94, 22.3%) used images of pills or images of other medication-related items. Studies using text-based data used a variety of machine learning methods (eg, recurrent neural networks, conditional random fields, support vector machines, and transformers), whereas those that leveraged images typically used convolutional neural networks. Most studies (61/94, 64.9%) used publicly available data from benchmarking datasets (eg, n2c2 2022) and were strictly model development studies, with only 1.1% (n=1) being usability studies. CONCLUSIONS: This is the first review to consider the role of AI in the automation of MedRec tasks, offering a basis for prioritizing future development efforts. Current applications of AI to automate MedRec tasks are preliminary, with most work focusing on the extraction of medication information and limited to proof-of-concept model development. Future work should consider addressing infrastructural barriers to the AI-based automation of MedRec tasks (eg, data incompleteness in sources of medication information) and exploring approaches to automate discrepancy resolution. Beyond developing models, there is also a need to implement them in tools and evaluate them in real-world contexts.
13 May 2026
Read appraisal →Annals of medicine
Ceftriaxone for methicillin-susceptible Staphylococcus aureus bloodstream infections is associated with increased short-term mortality: a systematic review and meta-analysis
BACKGROUND: Bloodstream infections (BSIs) by methicillin-susceptible Staphylococcus aureus (MSSA) are a significant cause of morbidity and mortality, traditionally treated with antistaphylococcal penicillins (ASPs) or cefazolin. Ceftriaxone has emerged as an alternative due to its once-daily dosing regimen and favourable safety profile; however, its efficacy compared to the standard of care (SoC) remains controversial. This evidence synthesis aimed to assess the role of ceftriaxone in treating MSSA-BSIs. METHODS: A systematic literature search was conducted in PubMed, Embase, and Scopus up to December 31, 2025 (PROSPERO protocol CRD42024595748). Studies comparing ceftriaxone to ASPs or cefazolin for MSSA-BSIs were included. Primary outcomes were 30-day and 90-day all-cause mortality . Pooled effect sizes with their 95% confidence intervals (CIs), were calculated using random-effects models, odds ratios (ORs) and mean differences (MDs) according to the type of outcome. RESULTS: Eleven studies totalling 2,568 patients were included. Ceftriaxone was associated with significantly increased 30-day mortality (OR 3.33; 95% CI: 2.17-5.10), although differences at 90 days were not significant (OR 1.71; 95% CI: 0.75-3.90). No significant differences were noted for clinical success (OR 0.49; 95% CI: 0.19-1.26), microbiological clearance (OR 1.66; 95% CI: 0.73-3.82). Adverse event rates were similar between groups. CONCLUSION: Given the availability of various alternatives and the consistent short-term mortality signal observed, routine use of ceftriaxone for MSSA-BSIs, especially as initial therapy, is not supported by current evidence. Only novel findings from randomized studies may change the place in therapy of the drug in this context.
13 May 2026
Read appraisal →Journal of medical Internet research
Why We Need Patients and Community at the Center of AI Health Communication Research
Holderried and colleagues tested whether artificial intelligence (AI)-generated, patient-centered information can help people understand what they need to do after being discharged from a hospital. Participants demonstrated stronger comprehension when they viewed the simplified, patient-centered information rather than a standard letter. This work adds to the available early-phase evidence of AI supporting hospital discharge communication. To meaningfully progress this area of research, we now need to carefully consider how to enhance the design and evaluation of patient-facing AI health communication tools. In this commentary, we argue that equity and consumer engagement remain underrepresented in studies on patient-facing AI health communication tools and describe possible approaches to address this issue.
12 May 2026
Read appraisal →JMIR research protocols
Investigating the Implementation and Impact of AI-Assisted Fall Prevention in Hospitals: Protocol for a Multicenter, Multimethod Observational Study in Sweden (SAFE)
BACKGROUND: Artificial intelligence (AI) has the potential to enhance patient safety, particularly in the prevention of in-hospital falls. Recent advances in sensor-based AI systems allow for the analysis of complex, multimodal data to generate real-time alerts, enabling health care professionals to intervene before a fall occurs. By shifting from reactive responses to proactive risk management, these technologies may enable reductions in fall incidence and improvements in care outcomes. As a result, hospitals across Europe are increasingly adopting such systems. Nevertheless, empirical evidence concerning their routine implementation remains limited, particularly concerning their impact on patient safety, clinical workflows, and the usage of health care resources. Addressing these gaps is essential for effective and sustainable integration into hospital care. OBJECTIVE: This paper outlines the protocol for the multicenter, multimethod project SAFE (Safe AI-Assisted Fall Prevention Through Evidence), which investigates the implementation and impact of AI-assisted fall prevention in Swedish hospitals. METHODS: The research project is a collaboration between Halmstad University and hospitals in the Västra Götaland Region (VGR) and will, during 2026-2028, investigate an ongoing large-scale AI system implementation in VGR hospitals, covering up to 2400 patient beds. Using surveys, interviews, observations, and a retrospective study, it will track the implementation and impact over time. Two learning laboratories involving patients, their relatives, and health care professionals will be conducted to codevelop strategies for the implementation of AI-assisted fall prevention. RESULTS: The project will provide evidence-based insights and practical guidance on AI-assisted fall prevention. The findings will be relevant not only to patients, health care professionals, and hospital organizations, but also to policymakers and stakeholders involved in the digital transformation of health care. CONCLUSIONS: Although VGR serves as the primary research setting, the project's results will inform future similar initiatives in Sweden and offer transferable lessons for other health care systems internationally. This study will contribute to the evidence base on AI-assisted fall prevention in health care, supporting the responsible and scalable integration of such systems across diverse health care environments.
11 May 2026
Read appraisal →Current urology reports
Artificial Intelligence in Urologic Documentation: A Review of Emerging Capabilities and the Ongoing Need for Human Oversight
PURPOSE OF REVIEW: Clinical documentation continues to expand in volume and complexity, spanning outpatient encounters, inpatient summaries, patient-portal communications, and educational materials. These growing demands contribute to clinician burden and reduce time available for direct patient care. Artificial intelligence (AI) has emerged as a potential strategy to streamline documentation workflows. This review evaluates current AI applications in clinical documentation, with illustrative examples from urologic practice. RECENT FINDINGS: Ambient AI scribes can capture the bulk of outpatient encounters and generate structured draft notes that clinicians edit rather than write de novo. Large language models have shown promise in assisting with inpatient documentation and discharge summaries, often producing drafts that are coherent, readable, and shorter than physician-authored text. AI tools can also simplify patient education materials and translate dense radiology reports into accessible language. Across these domains, however, studies consistently demonstrate that AI-generated content remains vulnerable to factual errors, omissions, hallucinations, and misaligned emphasis, reinforcing the need for clinician oversight. Overall, emerging evidence supports a complementary relationship between clinicians and AI. Used as supervised drafting aids rather than autonomous authors, AI tools have the potential to ease documentation burden and create more time for direct patient care without diminishing the clinician's role in shaping the medical record.
10 May 2026
Read appraisal →BMJ health & care informatics
Does the accuracy of medication administration documentation improve with electronic medication systems? A stepped-wedge cluster randomised trial.
OBJECTIVES: Accurate documentation of medication administration is crucial for patient safety. This study compares the accuracy of documentation between paper-based and electronic medication management (eMM) systems. METHODS: A secondary analysis of data from a stepped-wedge cluster randomised trial of an eMM implementation was conducted. The eMM module allowed electronic prescribing, recording of drug dispensing, drug administration and medication reconciliation and monitoring. Medication administration processes were observed in a tertiary paediatric hospital in Sydney, Australia. Trained nurse observers recorded details of medication administrations which were then compared with the information documented by nurses about the administrations. RESULTS: 5095 administrations were analysed. With eMM compared with paper, significantly fewer administrations had no time documented (adjusted OR (aOR) 0.15; 95% CI 0.07 to 0.34; p<0.001), but documented details were more often inaccurate (aOR 4.6; 95% CI 3.3 to 6.6). Fewer doses were administered from incorrect prescriptions with eMM (aOR 0.25; 95% CI 0.18 to 0.36). CONCLUSION: eMM improves certain aspects of documentation but introduces new challenges, particularly in accurately recording administration details. Further investigation is needed to optimise these systems. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry 370325.
27 Apr 2026
Read appraisal →European journal of hospital pharmacy : science and practice
How to manage the overwhelming amount of database-detected interactions? A focus group meeting study on the management of database-detected risks of drug interactions.
OBJECTIVES: Database-assisted screening may help detect the risks of drug interactions, but less than 10% of flagged interactions have clinical manifestations, potentially leading to alert fatigue among healthcare providers. To address this issue, this study aimed to develop a concise framework to support the management of database-indicated interaction risks. METHODS: A grounded theory approach was used to design, evaluate and develop the framework. Participants included healthcare professionals and laypersons with experience using narrow therapeutic index medications, as well as individuals recommended by them. Two focus group discussions were conducted (N1=6 and N2=5), each lasting approximately 2 hours and following the same agenda. In one case, a follow-up one-on-one interview was held because the participant indicated further insights at the end of the session. RESULTS: Participants identified key decision-making questions for the framework, including the evidence base for interacting drugs, severity and documentation level of the interaction, availability of safer alternatives, and the presence of confounding factors. The framework was considered useful and, following refinement, potentially suitable for clinical implementation. A key insight was that integration into healthcare curricula is essential for achieving long-term impact. Overall, the proposed tool may assist in managing interaction risks in diverse clinical scenarios and reduce alert fatigue among healthcare professionals. CONCLUSION: Although further clinical validation is needed, the framework provides a foundation for improving the management of database-detected drug interactions.
26 Apr 2026
Read appraisal →European journal of hospital pharmacy : science and practice
Algorithm-managed dosing and pharmacist-managed dosing of erythropoietin stimulating agents in renal anaemia: a systematic review
OBJECTIVES: The goal of this systematic review was to identify and summarise algorithm-managed and pharmacist-managed dosing of erythropoietin stimulating agents (ESA) in patients with renal anaemia and to determine the effects on available outcome parameters. METHODS: We followed the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines for systematic reviews. Studies investigating algorithm-managed and pharmacist-managed dosing of ESA in adult patients with renal anaemia were evaluated for inclusion. No restrictions were set on outcome parameters. Observational and interventional studies available as full-text articles with a control group and follow-up ≥6 months were eligible for inclusion. Relevant databases were searched from their inception through August 2024. Two independent reviewers evaluated all studies. The risk of bias was assessed by the ROBINS-I and RoB1 tools. The protocol of this study was registered in PROSPERO (CRD42021243678). RESULTS: After screening 140 articles, 17 articles and 4313 patients could be included. Available evidence was of low to moderate quality with a high risk of bias. Data were summarised and tabulated. Meta-analysis was not possible due to the substantial heterogeneity in participants, study design, interventions, comparisons, and outcome parameters. However, standardised metrics could be identified and calculated for haemoglobin and ESA dose. The percentage in target range for haemoglobin varied between 3.5% lower (95% CI -18.67% to +11.67%) to 32.0% higher (95% CI 14.07% to 49.93%) in the pharmacist-managed group versus the control group (n=1401). The range in reduction in ESA dose was 5.45% (95% CI -7.97% to +18.87%) to 49.97% (95% CI 20.32% to 79.61%) in the pharmacist-managed group versus the control group (n=2115). CONCLUSION: Low-quality data with high risk of bias suggest that pharmacist-managed renal anaemia may improve the percentage of haemoglobin within target range and reduce the ESA dose. However, meta-analysis was impossible due to substantial heterogeneity. Therefore, no definite conclusions could be drawn on the effectiveness of pharmacist-managed dosing of ESA in renal anaemia. PROSPERO REGISTRATION NUMBER: CRD42021243678.
26 Apr 2026
Read appraisal →BMJ quality & safety
Sustainability of the de-implementation of low-value care in infants with bronchiolitis: 2-year follow-up of a cluster randomised controlled trial
BACKGROUND: In 2017, the PREDICT (Paediatric Research in Emergency Departments International Collaborative) network conducted a cluster randomised controlled trial (cRCT) at 26 Australian and New Zealand hospitals to improve bronchiolitis care. Findings demonstrated that targeted interventions significantly improved adherence with five evidence-based low-value bronchiolitis practices (no chest radiography, salbutamol, glucocorticoids, antibiotics and epinephrine) in the first 24 hours of hospitalisation (adjusted risk difference, 14.1%; 95% CI: 6.5% to 21.7%; p<0.001). During the intervention year (2017), intervention hospital (n=13) compliance was 85.1% (95% CI: 82.6% to 89.7%). This study aimed to determine if improvements in bronchiolitis management were sustained at intervention hospitals 2 years post-trial completion. METHODS: International, multicentre follow-up study of hospitals in Australia and New Zealand that participated in a cRCT of de-implementation of low-value bronchiolitis practices, 1 year (2018) and 2 years (2019) post-trial completion, obtained retrospectively from medical audits. Sustainability was defined a priori as no more than a <7% decrease to any level of improvement in adherence for all five low-value practices (composite outcome) from the cRCT intervention year. RESULTS: Of the 26 hospitals, 11 intervention and 10 control hospitals agreed to participate in the follow-up study. Data were collected on 3299 infants with bronchiolitis 1 year (intervention and control hospitals) and 1689 infants 2 years post-trial (intervention hospitals). Adherence with no use of the five low-value practices 2 years post-trial completion was 80.9% (adjusted predicted adherence, 80.8%, 95% CI: 77.4% to 84.2%; estimated risk difference from cRCT outcome -3.9%, 95% CI: -8.6% to 0.8%) at intervention hospitals, fulfilling the a priori definition of sustainability. DISCUSSION: Targeted interventions, delivered over one bronchiolitis season, resulted in sustained improvements in bronchiolitis management in infants 2 years later. This follow-up study provides evidence for sustainability in de-implementing low-value care in bronchiolitis management. TRIAL REGISTRATION DETAILS: Australian and New Zealand Clinical Trials Registry No: ACTRN12621001287820.
23 Apr 2026
Read appraisal →JMIR cancer
Digital and Paper-Based Hospital Workflows and 60-Day Mortality in Acute Leukemia: Retrospective Natural Experiment
BACKGROUND: Electronic medical record (EMR) systems have been associated with better clinical workflows and fewer documentation errors. However, evidence regarding their effect on time-sensitive leukemia care in public hospitals in Latin America remains limited. OBJECTIVE: This study aimed to compare 60-day mortality and urgent supportive-care processes between 2 tertiary public hospitals in Mexico with different documentation models (integrated EMR vs traditional physical records [TPR]), under the hypothesis that digital workflows may facilitate more timely treatment. METHODS: We conducted a retrospective natural experiment including 274 patients with newly diagnosed acute leukemia treated between February 2023 and April 2025. Clinical characteristics, treatment intensity, complications during induction, antibiotic administration times, and survival outcomes were abstracted from finalized institutional records that are routinely reviewed at discharge by the institutional medical record committee as part of standard quality-assurance procedures. The primary outcome was 60-day mortality. Secondary outcomes included time to first antibiotic dose after recognition of febrile neutropenia, treatment-related complications, and the number of operational steps required for urgent care processes. A subgroup analysis of 70 patients with complete timing documentation was performed for antibiotic administration. Comparisons between hospitals were performed using univariate tests, Kaplan-Meier 60-day survival curves, and multivariable logistic regression. RESULTS: Of the 274 included patients, 104 (38%) were treated at the hospital using an integrated EMR, and 170 (62%) at the hospital using TPR. Sixty-day mortality was lower in the EMR hospital (6/104, 5.8%) than in the TPR hospital (61/170, 35.9%; P<.001). In the subgroup with complete timing data, the mean time from febrile episode recognition to first antibiotic administration was shorter in the hospital using EMR than in the hospital using TPR (54, SD 18.4 minutes; P<.001). Although the EMR hospital used a higher proportion of high-intensity regimens, patients in that hospital had better 60-day outcomes. In multivariable analysis, hospital type remained independently associated with 60-day mortality (odds ratio 0.11, 95% CI 0.05-0.26; P<.001), whereas kidney injury and hepatotoxicity were associated with worse outcomes. CONCLUSIONS: In this natural experiment, the hospital using an integrated EMR had a lower 60-day mortality and shorter time to antibiotic administration than the hospital using TPR. These findings are hypothesis-generating and suggest that digital workflows may contribute to more timely urgent supportive care, but they should be interpreted with caution, given the retrospective 2-center design and the potential for residual confounding.
19 Apr 2026
Read appraisal →PloS one
High-frequency vs. low-frequency MIDI-assisted group music therapy in psychiatric inpatients: A randomized controlled trial
BACKGROUND: Music therapy has been increasingly incorporated into psychiatric inpatient care as an adjunctive intervention; however, evidence regarding its comparative effectiveness, particularly with respect to intervention intensity during brief psychiatric hospitalizations, remains limited. Most randomized trials have evaluated interventions delivered over several weeks, leaving uncertainty about the potential differential effects of higher versus lower intervention intensity within short inpatient stays. METHODS: This randomized controlled trial with a parallel-group design was conducted in an adult psychiatric inpatient unit in Bogotá, Colombia. Participants were randomly assigned (1:1) to either a high-frequency music therapy group, which received five sessions delivered on consecutive days within a single inpatient week, or a low-frequency group, which received one session during the same period. Allocation concealment was ensured through computer-generated randomization performed by an independent researcher not involved in participant recruitment or outcome assessment. Primary outcomes were symptoms of depression, anxiety, and stress, assessed using the Depression Anxiety Stress Scale-21 (DASS-21) at baseline and at the end of the intervention week. Secondary outcomes included emotional responses to music therapy, assessed using selected items from the Questionnaire of the Impact of Music Therapy Sessions in Adults (CISMA instrument), and global life satisfaction, measured with the Satisfaction With Life Scale (SWLS). Between-group changes over time were analyzed using repeated-measures mixed-effects linear models. RESULTS: A total of 91 participants were randomized, of whom 74 (37 per group) completed post-intervention assessments and were included in the per-protocol analysis. Between-group analyses revealed a statistically significant group × time interaction for the DASS-21 stress subscale only. Participants in the high-frequency music therapy group showed a greater reduction in stress scores compared with the low-frequency group, with a statistical between-group difference (6.49 ± 5.55 vs. 7.03 ± 5.81; p = 0.023). No statistically significant between-group differences were observed for the DASS-21 anxiety subscale (p = 0.339) or the depression subscale (p = 0.270). Global life satisfaction, as measured by the SWLS, did not differ significantly between groups (p > 0.05). For secondary outcomes assessed using selected CISMA items related to emotional change, between-group differences were small and not statistically significant across all items. Overall, except for stress symptoms, outcomes did not differ significantly between the high-frequency and low-frequency intervention groups. CONCLUSIONS: In this randomized controlled trial, a higher frequency of music therapy sessions was associated with a greater reduction in stress symptoms compared with a lower-frequency intervention during a brief psychiatric hospitalization. However, no between-group differences were observed for anxiety, depression, life satisfaction, or emotional response measures. Overall, these findings indicate limited evidence of differential effectiveness by intervention intensity and should be interpreted cautiously in light of the study's methodological limitations. TRIAL REGISTRATION: ISRCTN registry: ISRCTN87861817 (https://www.isrctn.com/ISRCTN87861817).
18 Apr 2026
Read appraisal →The New England journal of medicine
Selective Decontamination of the Digestive Tract during Ventilation in the ICU
BACKGROUND: Whether selective decontamination of the digestive tract (SDD) reduces mortality among patients undergoing mechanical ventilation and whether it adversely affects microbial ecology in the intensive care unit (ICU) remain unclear. In an earlier analysis of data from Australia, SDD did not result in a lower incidence of in-hospital death than standard care, but data from the full international trial are needed. METHODS: We randomly assigned ICUs in Australia and Canada to use SDD or to continue standard care for two 12-month periods in patients undergoing mechanical ventilation. Patients in the SDD group received specific oral and gastric antimicrobial interventions for the duration of ventilation and an intravenous antibiotic agent for the first 4 days after enrollment. All other patients in the ICU were included in an observational ecologic assessment. Previously reported data from Australia are now combined with data from Canada. The primary outcome was in-hospital death from any cause at 90 days. The secondary clinical outcomes, assessed at 90 days, were death in the ICU and the number of days alive and free of mechanical ventilation, ICU admission, and hospitalization. Microbiologic secondary outcomes included new positive cultures for bloodstream infections and antibiotic-resistant organisms. For the ecologic assessment, the microbiologic outcomes were tested for noninferiority (noninferiority margin, 2 percentage points). RESULTS: In this trial involving 20,000 patients in 26 ICUs, 9289 patients were enrolled in the randomized trial and 10,711 were included in the ecologic assessment. At 90 days, 1175 of 4215 patients (27.9%) in the SDD group and 1494 of 5065 (29.5%) in the standard-care group had died before hospital discharge (odds ratio, 0.94; 95% confidence interval [CI], 0.84 to 1.05; P = 0.27). New bloodstream infections occurred in 4.9% of the patients in the SDD group and in 6.8% of those in the standard-care group (adjusted mean difference, -1.30 percentage points; 95% CI, -2.55 to -0.05); antibiotic-resistant organisms were cultured in 16.8% and 26.8%, respectively (adjusted mean difference, -9.60 percentage points; 95% CI, -12.40 to -6.80). In the ecologic assessment, noninferiority of SDD was not confirmed for the development of new antibiotic-resistant organisms. Adverse events considered to be related to SDD or standard care were reported in 12 patients (0.3%) in the SDD group and in no patients in the standard-care group. Serious adverse events occurred in 47 patients (1.1%) and 59 patients (1.2%), respectively. CONCLUSIONS: Among critically ill patients undergoing mechanical ventilation, SDD did not result in a lower incidence of in-hospital death than standard care. (Funded by the National Health and Medical Research Council of Australia and the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT02389036.).
16 Apr 2026
Read appraisal →JMIR nursing
Nurses' Evaluation of a Service Robot for Inpatient Care: Technology Acceptance Study
BACKGROUND: The integration of robotic systems into nursing practice is increasingly discussed as a potential strategy to alleviate workload and support care processes in response to demographic changes and staffing shortages. However, the acceptance of nursing staff as primary end users remains a critical determinant for successful implementation. Despite technological advances, the practical requirements and perspectives of nursing staff have not been adequately considered in research and development efforts to date. OBJECTIVE: Building on the user-centered development approach applied, this study aimed to examine nursing staff's evaluation of a service robot designed to assist with routine tasks in inpatient care, as well as their intention to use it, while accounting for technology-specific and psychological determinants of acceptance. METHODS: A total of 30 nurses tested the robot across 3 application scenarios (information service, item delivery, and beverage delivery) in a simulated care setting, alternating between the roles of nurse and care recipient. Acceptance-related constructs, including intention to use, were measured using the Technology Usage Inventory. General attitudes toward robots were assessed via the General Attitudes Towards Robots Scale. Participants' prior experience with robotics was also documented. Spearman rank correlations and Mann-Whitney U tests were used for analysis. RESULTS: The robot was rated positively across all dimensions. Usability was high (median 20, IQR 18-21; scale range 3-21), as was perceived usefulness (median 21, IQR 16-24; range 4-28). Skepticism was low (median 10.5, IQR 7-12; scale range 4-28), and accessibility was moderate (median 10, IQR 8-13; scale range: 3-21). Intention to use was strong (median 224.5, IQR 157-248; scale range 0-300) and correlated positively with usability (rs(28)=0.505; P=.004), perceived usefulness (rs(28)=0.74; P<.001), and accessibility (rs(28)=0.628; P<.001), and negatively with skepticism (rs(28)=-0.516; P=.004). More positive personal attitudes toward robots were also associated with higher perceived usefulness (rs(28)=0.549; P=.002) and greater intention to use (rs(28)=0.483; P=.007). No significant differences in intention to use were found between participants with and without prior robotics experience (U=83.5; P=.62). CONCLUSIONS: The findings indicate a high level of acceptance among the participating nursing staff for the developed service robot within the tested scenarios. Considering the chosen user-centered development approach, they further underscore the need for strategies that combine participatory design, transparent communication of system capabilities and limitations, and structured opportunities for hands-on experience. Such measures, together with proactive knowledge transfer and skills development, are essential to sustainably leverage the practical potential of service robotics in nursing practice.
15 Apr 2026
Read appraisal →ChemMedChem
Structure- and Ligand-Based Discovery of Novel 3-Chymotrypsin-Like Protease Nonpeptidomimetic Hits
The SARS-CoV-2 3-chymotrypsin-like (3CLpro) protease is a key target for the development of COVID-19 therapeutics. While ensitrelvir and nirmatrelvir are approved drugs for treatment, the continuous research and development for new antiviral drugs is necessary to combat the emergence of variants and other related viruses. This study employed structure- and ligand-based computer-assisted approaches to identify new 3CLpro nonpeptidomimetic inhibitors. Using data from COVID Moonshot, NCATS, and the literature, computational methods such as shape-based, ensemble docking, and machine learning (ML) techniques were developed, achieving robust validation metrics: AUC = 87%, EF = 7, BEDROC = 60% for shape-based; AUC = 87%, EF = 7.03, BEDROC = 62% for ensemble docking, and ACC = 81%, MCC = 62% for ML models, combing Random forest + ECFP4 fingerprint. These models were utilized in virtual screening (VS) campaigns using the H3D and ChemBridge libraries, from which six promising hits with IC50 values ≤80 µM were identified, including LabMol-499 with an IC50 of 13.71 µM and a Ki of 21.74 µM. Moreover, we found that LabMol-499 acts as a noncompetitive, reversible inhibitor of 3CLpro. These findings provide a foundation for hit-to-lead optimization of new nonpeptidomimetic 3CLpro inhibitors.
15 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 →Medicine
Effectiveness of Chinese herbal medicine mouthwash in preventing ventilator-associated pneumonia in patients with endotracheal intubation: A systematic review and meta-analysis
BACKGROUND: To systematically evaluate the effectiveness and safety of Chinese herbal medicine mouthwash (CHMM) in preventing ventilator-associated pneumonia (VAP) in patients with endotracheal intubation (ETT). METHODS: Computer searches were conducted for relevant randomized controlled trials (RCTs) in the China National Knowledge Infrastructure (CNKI), WANFANG DATA, Chinese Scientific and Technical Journal Database (VIP), Chinese Biomedical Literature Database (SinoMed), PubMed, Embase, Cochrane Library, and CINAHL Database from the establishment to April 5, 2023. The data were analyzed by RevMan 5.3 software. The methodological quality of papers was evaluated by the Cochrane version 5.1.0 and the certainty of evidence was evaluated by the GRADE system. RESULTS: We included 27 RCTs with 2426 patients. Most of the included studies were of poor methodological quality. Meta-analysis showed that CHMM (RR = 0.41, 95% CI: 0.34-0.50, P < .00001, low-certainty evidence) or CHMM + routine oral care (RR = 0.31, 95% CI: 0.18-0.56, P < .0001) could reduce the incidence of VAP compared to non-CHMM. CHMM may be superior to saline (low-certainty evidence) and chlorhexidine (moderate-certainty evidence) in preventing VAP (P < .05). CONCLUSION: Low-certainty evidence shows that CHMM may be more advantageous in reducing the incidence of VAP compared to non-CHMM. However, due to some limitations and the low certainty of evidence, we cannot recommend CHMM for routine clinical use at this time. Large-scale and high-quality RCTs are also needed to definitively evaluate the safety and clinical benefits of CHMM in the future.
12 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 →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
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