Authors: Pan, Wan | Zhou, Yan | Ji, Yueping | Zhou, Lianfang | Wang, Li
Article Type: Research Article
Abstract: OBJECTIVE: In order to improve nursing quality management and protect patient medical safety, it is necessary to change the default mode and completely integrate information technology and nursing quality control utilising lean management. METHODS: A database was created, the nurse quality control scoring standard was entered into the computer and after the inspection, and various inspection reports were entered into the computer to precisely and promptly preserve data. The computer was then utilised to precisely assess the intensity and quality of nursing work, compute, count, and analyse the stored data, output the quality of nursing work in each department as …a report, and adopt lean management for the gathered issues. RESULTS: To reach the objective of raising nursing quality, data analysis makes it simple to identify flaws and consistently strengthen the weak points. In order to create an information-based nursing quality control system with a simple and effective method as well as results that are scientific and objective, lean management is brought into the construction process. Show more
Keywords: Lean management, information based nursing quality control system, experience
DOI: 10.3233/THC-230730
Citation: Technology and Health Care, vol. 32, no. 4, pp. 2081-2090, 2024
Authors: Zhou, Yan | Liu, Yubao | Han, Yutong | Yan, Hongxia
Article Type: Research Article
Abstract: BACKGROUND: Intensive care unit acquired weakness (ICU-AW) is a secondary neuromuscular complication in critically ill patients, characterized by profound weakness in all four limbs. Studies have shown that bundles of care are nursing strategies that combine a series of evidence-based interventions, which collectively optimize patients’ clinical outcomes compared to individual interventions. OBJECTIVE: This study aims to conduct a meta-analysis of the effects of bundle interventions on ICU-AW deeply exploring the characteristics of bundle interventions, patient outcomes related to ICU-AW, and primarily investigating the effects of bundle interventions on ICU-AW. The main focus is to explore the clinical value of bundle …interventions in treatment of ICU-acquired weakness in patients. METHODS: Computer and manual searches were conducted using keywords to retrieve relevant studies on the effects of bundle interventions on ICU-AW from databases such as PubMed, Web of Science, Cochrane Library and EMbase. The search period ranged from database inception to the present. The control group received standard ICU care, including basic nursing, while the intervention group received bundle nursing interventions. RESULTS: A total of 10 randomized controlled trials (RCTs) involving 1545 participants (790 in the intervention group and 755 in the control group) were included. Meta-analysis results showed that the intervention group had significantly higher muscle strength (MD = 7.41, 95% CI: 6.65–8.16, P < 0.00001) and daily living ability (MD = 34.01, 95% CI: 32.54–35.48, P < 0.00001) than the control group. Additionally, the incidence of ICU-AW (OR = 0.39, 95% CI: 0.26–0.59, P < 0.00001), mechanical ventilation time (MD = - 3.71, 95% CI: - 3.58∼ - 2.76, P < 0.0001), and ICU length of stay (MD = - 2.73, 95% CI: - 3.14∼ - 2.31, P < 0.00001) were significantly lower in the intervention group than in the control group. CONCLUSION: ICU-AW has a severe negative impact on the recovery and functional restoration of ICU patients, increasing the treatment complexity for healthcare providers and the mortality and disability rates for patients. The bundled care approach may help reduce the incidence of ICU-AW, promote the restoration of daily activity function, enhance muscle strength, and reduce ICU stay and mechanical ventilation time for ICU patients. However, the long-term effects of bundle interventions still require further in-depth research. Show more
Keywords: Nursing interventions, muscle weakness, intensive care unit, incidence, meta-analysis
DOI: 10.3233/THC-241542
Citation: Technology and Health Care, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhao, Huairui | Hua, Jia | Geng, Xiaochuan | Xu, Jianrong | Guo, Yi | Suo, Shiteng | Zhou, Yan | Wang, Yuanyuan
Article Type: Research Article
Abstract: BACKGROUND: High-precision detection for individual and clustered microcalcifications in mammograms is important for the early diagnosis of breast cancer. Large-scale differences between the two types and low-contrast images are major difficulties faced by radiologists when performing diagnoses. OBJECTIVE: Deep learning-based methods can provide end-to-end solutions for efficient detection. However, multicenter data bias, the low resolution of network inputs, and scale differences between microcalcifications lead to low detection rates. Aiming to overcome the aforementioned limitations, we propose a pyramid feature network for microcalcification detection in mammograms, MicroDMa, with adaptive image adjustment and shortcut connections. METHODS: First, mammograms from multiple centers are …represented as histograms and cropped by adaptive image adjustment, which mitigates the impact of dataset bias. Second, the proposed shortcut connection pyramid network ensures that the feature map contains more information for multiscale objects, while a shortcut path that jumps over layers enhances the efficiency of feature propagation from bottom to top. Third, the weights of each feature map at different scales in the fusion are trainable; thus, the network can automatically learn the contributions of all feature maps in the fusion stage. RESULT: Experiments were conducted on our in-house dataset and the public dataset INbreast. When the average number of positives per image is one on the in-house dataset, the recall rates of MicroDMa are the 96.8% for individual microcalcification and 98.9% for clustered microcalcification, which are higher than 69.1% and 91.2% achieved by recent deep learning model. Free-response receiver operating characteristic curve of MicroDMa is also higher than other methods when models are performed on INbreast. CONCLUSION: MicroDMa network is better than other methods and it can effectively help radiologists detect and identify two types of microcalcifications in clinical applications. Show more
Keywords: Deep learning, detection, pyramid network, mammography, breast microcalcification, convolutional neural network
DOI: 10.3233/THC-220235
Citation: Technology and Health Care, vol. 31, no. 3, pp. 841-853, 2023
Authors: Pan, Liang | Shan, Rui-Ying | Gao, Su-Fang | Zhou, Yan | Bao, Yuan-Yuan | Fu, Wenjing
Article Type: Research Article
Abstract: BACKGROUND: In malignant tumours of the female reproductive system, cervical cancer is second only to breast cancer, seriously threatening the health and safety of most women. OBJECTIVE: To evaluate the clinical value of 3.0 T multimodal nuclear magnetic resonance imaging (MRI) in the International Federation of Gynecology and Obstetrics’ (FIGO) staging of cervical cancer. METHODS: The clinical data of 30 patients with pathologically diagnosed cervical cancer admitted to our hospital from January 2018 to August 2022 were analysed retrospectively. Before treatment, all patients were examined with conventional MRI, diffusion-weighted imaging and multi-directional contrast-enhanced imaging. RESULTS: The accuracy of multimodal MRI …in the FIGO staging of cervical cancer (29/30, 96.7%) was significantly higher than the accuracy obtained in a control group (21/30, 70.0%), with a statistically significant difference (p = 0.013). In addition, there was good agreement between two observers applying multimodal imaging (kappa = 0.881) and moderate agreement between two observers in the control group (kappa = 0.538). CONCLUSION: Multimodal MRI can evaluate cervical cancer comprehensively and accurately to enable accurate FIGO staging, providing significant evidence for clinical operation planning and subsequent combined therapy. Show more
Keywords: Multimodal imaging, uterine cervical neoplasms, magnetic resonance imaging
DOI: 10.3233/THC-230252
Citation: Technology and Health Care, vol. 32, no. 2, pp. 823-830, 2024
