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Diagnostic errors in primary care are common, particularly in the interpretation and follow-up of abnormal haemoglobin (Hgb) and estimated glomerular filtration rate (eGFR) results. These errors frequently result in missed or delayed diagnoses of serious conditions such as anaemia and chronic kidney disease. This protocol describes a stepped-wedge cluster randomised controlled trial designed to evaluate a novel, evidence-based, team-based intervention aimed at improving diagnostic safety and efficiency.
The study will be conducted across 12 University of Texas Physicians (UTPs) primary care clinics in Houston, Texas, USA. Adult patients (≥18 years) with newly identified abnormal Hgb or eGFR results will be eligible for inclusion. The intervention integrates automated tracking of abnormal laboratory results, nurse navigators to support patient follow-up and engagement, and clinical pathologists to provide diagnostic guidance to primary care providers. The primary outcome is diagnostic safety, defined as the proportion of patients who receive a correct diagnosis within 6 months. Secondary outcomes include diagnostic efficiency, appropriate test utilisation, cost-effectiveness, patient activation and implementation metrics such as acceptability, fidelity and sustainability. The study will also explore barriers and facilitators to successful implementation using mixed-methods evaluation.
This trial has been approved by the Institutional Review Board at The University of Texas Health Science Center at Houston. Study results will be disseminated through peer-reviewed publications and conference presentations, and findings will be reported to UTP leadership to inform potential system-wide implementation.
by Afsana Anwar, Mahmood Parvez, Farhan Azim, Uday Narayan Yadav, Saruna Ghimire, Ateeb Ahmad Parray, Shovon Bhattacharjee, ARM Mehrab Ali, Rashidul Alam Mahumud, Md Irteja Islam, Md Nazmul Huda, Mohammad Enamul Hoque, Probal Kumar Mondal, Abu Ansar Md Rizwan, Suvasish Das Shuvo, Sabuj Kanti Mistry
BackgroundFrailty and disability often emerge with ageing and affect quality of life. Older adults residing in Rohingya refugee camp in Bangladesh are particularly susceptible to frailty and disability due to adverse physical and social environment along with limited health and social care services available in the camp. This study aimed to investigate the prevalence and factors associated with frailty and disability among Rohingya older adults living in Bangladesh.
MethodsThis cross-sectional study was conducted among older adults aged ≥60 years residing in the Rohingya refugee settlement in Bangladesh. The primary outcomes were frailty and disability, explored using the ‘Frail Non-Disabled (FiND) questionnaire. Data were collected face-to-face during November-December 2021, using a semi-structured questionnaire. A multinomial logistic regression model was used to identify the factors associated with frailty and disability.
ResultsThe majority of participants (n = 864) were aged 60–69 years (72.34%), male (56.25%), married (79.05%), and without formal education (89.0%). The study revealed a high prevalence of frailty (36.92%) and disability (55.21%) among the participants. The multinomial regression analysis showed that the likelihood of experiencing disability was significantly higher among participants who were aged 70–79 years (RRR = 2.65, 95% CI: 1.25, 5.66) and ≥80 years (RRR = 8.06, 95% CI: 1.05, 61.80), were female (RRR = 3.93, 95% CI: 1.88, 8.1.9), had no formal education (RRR = 4.34, 95% CI: 2.19, 8.63), were living in a large family (RRR = 1.82, 95% CI: 1.05, 3.18) and were suffering from non-communicable diseases (RRR = 2.36, 95% CI: 1.32, 4.22) compared to their respective counterparts. The regression analysis also revealed that frailty was significantly higher among participants who were female (RRR = 2.82, 95% CI: 1.34, 5.94), were suffering from non-communicable diseases (RRR = 2.28, 95% CI: 1.27, 4.09), and had feeling of loneliness (RRR = 2.16, 95% CI: 1.11, 4.22).
ConclusionsThe findings underscore the need for long-term care and health promotion activities to alleviate the burden of frailty and disability among older adults in humanitarian settings. Efforts should particularly target the most vulnerable groups- older individuals (≥80 years), women, those without formal education, those living in large families, and those with non-communicable diseases.
by Nilavro Das Kabya, MD Shaifullah Sharafat, Rahimul Islam Emu, Mehrab Karim Opee, Riasat Khan
Malabar spinach is a nutrient-dense leafy vegetable widely cultivated and consumed in Bangladesh. Its productivity is often compromised by Alternaria leaf spot and straw mite infestations. This work proposes an efficient and interpretable deep learning framework for automatic Malabar spinach leaf disease classification. A curated dataset of Malabar spinach images collected from Habiganj Agricultural University and supplemented with public samples was categorized into three classes: Alternaria, straw mite, and healthy leaves. A lightweight SpinachCNN established a strong baseline, while Spinach-ResSENet, enhanced with squeeze-and-excitation modules, improved channel-wise attention and feature discrimination. A customized Vision Transformer (SpinachViT) and SwinV2-Base were further investigated to assess the benefits of transformer-based architectures under limited data. To mitigate annotation scarcity, we employed SimSiam-based self-supervised pretraining on unlabeled images, followed by supervised fine-tuning with cross-entropy or a hybrid objective combining cross-entropy and supervised contrastive loss. The best-performing domain-optimized model, SimSiam-CBAM-ResNet-50, incorporated Convolutional Block Attention Modules and achieved 97.31% test accuracy, 0.9983 macro ROC-AUC, and low calibration error, while maintaining robustness to Gaussian and salt-and-pepper noise. Although a SwinV2-Base benchmark pretrained on ImageNet-22k reached slightly higher accuracy (97.98%, 98.99% with test-time augmentation), its 86.9M parameters and reliance on large-scale pretraining reduce feasibility for edge deployment. In contrast, the SimSiam-CBAM model offers a more parameter-efficient and deployment-friendly solution for real-world agricultural applications. Model decisions are interpretable via Grad-CAM, Grad-CAM++, and LayerCAM, which consistently highlight biologically relevant lesion regions. The spinach dataset used in this study is publicly available on: https://huggingface.co/datasets/saifullah03/malabar_spinach_leaf_disease_dataset.To explore healthcare professionals', patients', and family members' experiences of managing regular medications across the perioperative pathway in a specialist cancer hospital in Melbourne.
An exploratory qualitative study using a descriptive-interpretive approach.
Interviews were conducted with 11 patients and seven family members, and focus groups with 10 anaesthetists, seven surgeons, four nurses, and 10 pharmacists (N = 49) between October 2024 and April 2025. Transcripts were analysed using Braun and Clarke's reflexive thematic approach and mapped into the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 human factors framework.
Three interrelated themes were constructed: (1) Work system elements shaping perioperative medication management, encompassing medication and surgical contexts, documentation gaps, reliable medication information, communication infrastructures, roles and responsibilities, and perioperative area resources; (2) Processes influencing medication management practice, characterised by continuity of care at transition points and flagging processes, interdisciplinary collaboration and role interpretation in medication management, patient involvement, family member involvement, and healthcare professional perspectives; and (3) Outcomes of medication management, including patient and organisational outcomes, such as workflow inefficiencies, procedure cancellations, and unplanned readmissions.
Findings indicated that addressing the complexity of perioperative medication safety demands coordinated contributions across multiple professional disciplines. Strengthening interdisciplinary collaboration, clarifying shared responsibilities, embedding structured reconciliation processes at transitions of care, standardizing communication protocols, and involving patients and families are all critical strategies.
This study highlights the need for interdisciplinary coordination and clear role definitions, with nurses as the key contributor, to support collaborative medication decisions in perioperative cancer care.
This study explored challenges in managing regular medications during cancer surgery, offering insights to guide safer practices for perioperative teams, patients, and families in cancer care settings.
COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines.
None.
We aimed to determine the prevalence of hospital discharge communication problems in adults of 10 high-income nations and the associated factors.
Secondary analysis of cross-sectional survey data.
2023 Commonwealth Fund International Health Policy Survey for Adults, including data from residents of Australia, Canada, France, Germany, the Netherlands, New Zealand, Sweden, Switzerland, the UK and the USA.
3763 survey respondents aged 18 and older who reported hospitalisation at least one time in the past 2 years.
Our primary outcome measure is poor discharge communication (PDC), which is a composite variable comprising three questions regarding the provision of written information, follow-up arrangement and discussion of medications at time of discharge.
The overall PDC rate was 17.1%, with the highest in Germany (19.7%) and the lowest in the Netherlands (9.2%). No follow-up arrangement was the most commonly reported problem (22.8%). Respondents who concerned about social service needs and mental health issues were more likely to report PDC.
Providers should consider factors which impact PDC at hospital discharge and tailor communication appropriately. Hospitals, communities and countries should work towards policies that address underlying issues related to social determinants of health, including support for lower-income patients, improved treatment access for patients with physical and mental health conditions, and food and housing stability.
FreshRSS 