Brit J Hosp Med
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Aims/Background Backward walking is gaining traction in rehabilitation therapy, showing promise as an intervention for stroke patients with walking difficulties. However, the brain activity patterns (neurophysiological mechanisms) underlying backward walking in these patients remain unclear. This study investigated the neurophysiological mechanism in stroke patients within 1 year of their stroke. ⋯ Additionally, the DAR was significantly lower during backward walking than during forward walking (p < 0.05). Conclusion This study suggests that backward walking may more effectively activate neural activity in the prefrontal and right posterior parietal cortices. This finding supports the potential of backward walking to enhance motor execution and walking function in stroke patients, thereby supporting its application as a rehabilitation method.
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Aims/Background Artificial intelligence technology has attained rapid development in recent years. The integration of artificial intelligence applications into pressure reduction mattresses, giving rise to artificial intelligence-powered pressure reduction mattresses, is expected to provide personalised intelligent pressure reduction solutions, through automatic user's data-based adjustment of the patient's local pressure condition to prevent pressure injury. The purpose of this study was to investigate the effectiveness of artificial intelligence-powered smart decompression in the prevention of postoperative medium- and high-risk pressure injury in middle-aged and elderly patients. ⋯ Before treatment, there was no difference in the scores of all aspects of the Richards Campbell Sleep Questionnaire between the two groups (p > 0.05). After treatment, the scores of all aspects of Richards Campbell Sleep Questionnaire in the observation group were significantly lower than those in the control group (p < 0.05). Conclusion The artificial intelligence-powered smart decompression mattress can significantly prevent moderate- and high-risk pressure injury, effectively reducing the incidence of pressure injury and complications in postoperative long-term bedridden patients, alleviating the severity of pressure injury, relieving the pressure on various parts, and improving the sleep quality of patients.
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Comparative Study
Integrating Complaint Analysis into Hospital Management: A Comparative Study of Surgical and Non-Surgical Complaints.
Aims/Background: In an era where patient-centred care is paramount, effectively managing and analyzing hospital complaints is crucial for improving service quality and patient satisfaction. This study examines hospital complaints to enhance management practices by differentiating between surgery-related and non-surgery-related grievances. By identifying patterns in complaint types and outcomes, we aim to inform targeted quality improvement strategies that address specific patient concerns and boost operational efficiency. ⋯ Non-surgical departments should focus on improving treatment protocols and transparency. These strategies can reduce complaints and improve patient satisfaction. Future research should develop and test interventions based on these insights to further enhance healthcare quality.
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Aims/Background Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to improve the diagnostic accuracy of sacroiliitis by applying advanced machine learning techniques to computed tomography (CT) images. Methods We employed five convolutional neural network (CNN) models-Visual Geometry Group 16-layer Network (VGG16), ResNet101, DenseNet, Inception-v4, and ResNeXt-50-to analyze a dataset of 830 CT images, including both sacroiliitis and non-sacroiliitis cases. ⋯ Grad-CAM visualizations offered insights into the decision-making processes, highlighting the models' focus on relevant anatomical features critical for accurate diagnosis. Conclusion The use of CNN models, particularly ResNeXt-50 and Inception-v4, significantly improves the diagnosis of sacroiliitis from CT images. These models not only provide high diagnostic accuracy but also offer transparency in their decision-making processes, aiding clinicians in understanding and trusting Artificial Intelligence (AI)-driven diagnostics.
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Aims/Background Pressure injury stands as a global healthcare concern, primarily affecting elderly individuals. As the ageing of the global population shows no signs of slowing down, both society and the families of the affected individuals continue to bear the brunt of the consequences of pressure injuries. The majority of pressure injury cases are managed at home, and the occurrence and progression of pressure injuries in the elderly are closely associated with informal caregivers. ⋯ Conclusion The findings of this study establish a collaborative relationship network among the hospitals, family, medical staff, and caregivers in the management of pressure injuries, but with a special attention to the caregivers' needs for disease-related knowledge and psychophysical support. Such relationships streamline communication between medical staff, patients, and their caregivers, facilitating the adoption of active and correct methods by caregivers to prevent and care for pressure injuries. This can positively impact the quality of care for pressure injuries, further improving the life quality of patients and their caregivers, controlling the incidence of pressure injuries, and reducing readmission rates.