This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain an...This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.展开更多
Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of ca...Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the ...In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.展开更多
The benefits of regular physical activity are well known.Yet,few studies have examined the effectiveness of integrating physical activity(PA)into curricula within a post-secondary setting.To investigate the incorporat...The benefits of regular physical activity are well known.Yet,few studies have examined the effectiveness of integrating physical activity(PA)into curricula within a post-secondary setting.To investigate the incorporation of PA into medical curriculum,we developed a series of optional exercise-based review sessions designed to reinforce musculoskeletal(MSK)anatomy course material.These synchronous sessions were co-taught by a group fitness instructor and an anatomy instructor.The fitness instructor would lead students through both strength and yoga style exercises,while the anatomy instructor asked questions about relevant anatomical structures related to course material previously covered.After the sessions,participants were asked to evaluate the classes on their self-reported exam preparedness in improving MSK anatomy knowledge,PA levels,and mental wellbeing.Thirty participants completed surveys;a majority agreed that the classes increased understanding of MSK concepts(90.0%)and activity levels(97.7%).Many(70.0%)felt that the classes helped reduce stress.The majority of respondents(90.0%)agreed that the classes contributed to increased feelings of social connectedness.Overall,medical students saw benefit in PA based interventions to supplement MSK course concepts.Along with increasing activity levels and promoting health behaviours,integrating PA into medical curriculum may improve comprehension of learning material,alleviate stress and foster social connectivity among medical students.展开更多
Introduction and Problem Statement: Many medication errors occur during the community and hospital transition. Indeed, the World Health Organization launched the international “High 5S” project to implement medicati...Introduction and Problem Statement: Many medication errors occur during the community and hospital transition. Indeed, the World Health Organization launched the international “High 5S” project to implement medication reconciliation in healthcare facilities to reduce them and ensure patients a safe, high-quality healthcare pathway. Objective: This study aimed to detect medication errors by reconciling drug treatments and assess the relevance and feasibility of this standardized practice within the Medical Emergency Unit of the Teaching Pediatric Hospital of Ouagadougou (Burkina Faso). Methods: Patients whose parents gave their consent at their entrance were enrolled. For each patient, the pharmacy team completed a reconciliation form that included the patient’s usual treatment, which was taken and in progress and received upon admission to the medical emergency unit. Patients’ treatments were reviewed to detect and characterize discrepancies. The data of each form were reported and analyzed using KoboCollect, an Android application. Results: 135 records and 412 medication lines were captured over six weeks. The average time of treatment reconciliation per patient was 57 minutes. One thousand one hundred ninety-eight (1198) intentional discrepancies were detected, of which 6.09% were documented. Seventy-one (71) unintentional discrepancies were collected, including 39 omissions, 24 regimen dosing errors, and 8 pharmaceutical form dosage errors. Forty-nine (49) unintentional discrepancies, or 69.01%, were corrected by formulated pharmaceutical interventions toward physicians. Conclusion: Medical treatment reconciliation during hospital admission is critical because discrepancies can compromise the efficacy and/or safety of the patient’s hospital medication.展开更多
Objective: To identify the principal factors associated with the occurrence and development of medical device-related pressure injuries (MDRPI) in adults admitted to hospitals. MDRPI, a peculiar subtype of pressure in...Objective: To identify the principal factors associated with the occurrence and development of medical device-related pressure injuries (MDRPI) in adults admitted to hospitals. MDRPI, a peculiar subtype of pressure injuries (PI), result from the pression exerted by devices (or their fixation systems) applied for diagnostic and therapeutic purposes. MDRPI represent a serious problem for patients and healthcare systems. Understanding potential risk factors is an important step in implementing effective interventions. Methods: In this study, we will perform a systematic review;if possible, also a meta-analysis will be performed. The review will follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) reporting guidelines for systematic reviews. A rigorous literature search will be conducted both in electronic databases (Medline/PubMed, Embase, CINAHL, Web of Science, Scopus, Cochrane Library) to identify studies published since 2000 and in gray literature for unpublished studies. Pairs of researchers will identify relevant evidence, extract data, and assess risk of bias independently in each eligible study. Factors associated with the occurrence of MDRPI are considered the primary outcome. Secondary outcomes are prevalence and incidence of MDRPI, length of hospital stay, infections, and death. The evidence will be synthesized using the GRADE methodology. Results: Results are not currently available as this is a protocol for a systematic review. Conclusions: This systematic review will identify evidence on risk factors for developing MDRPI. We are confident that the results of this review will help to improve clinical practice and guide future research.展开更多
Background: Hepatitis B virus (HBV) is a primary reason for liver cancer and continues to be a worldwide public health issue. The likelihood of contracting HBV is greater in healthcare workers (HCWs) compared to indiv...Background: Hepatitis B virus (HBV) is a primary reason for liver cancer and continues to be a worldwide public health issue. The likelihood of contracting HBV is greater in healthcare workers (HCWs) compared to individuals who are not in healthcare professions. Medical students are classified as a high-risk demographic since, like HCWs, they often come into contact with bodily fluids and blood during their clinical training. By 2030, a greater proportion of people will have received HBV vaccinations, thereby halting the spread of new infections—The Somali Ministry of Health with the help of various agencies announced to eradicate hepatitis from Somalia. The priority actions are national hepatitis strategy, hepatitis survey, public awareness, training, and capacity building. Objectives: This study aims to assess the knowledge, attitude, and vaccination status of Hepatitis B infection among medical university students in Mogadishu, Somalia, 2024. Methods: Cross-sectional study design was used in this study and the survey was carried out among medical students enrolled in Universities from April 1, 2023 to June 30, 2023. The data was analyzed using SPSS version 26.0 software, Chi-square analysis and Logistic regression analysis to identify associations between demographic factors and HBV knowledge, attitudes, and vaccination status, as well as perspectives and immunization status concerning viral hepatitis. Results: The study achieved a response rate of (96%), with 230 participants. Most students (76.5%) were aged 26 - 30 years, and (60.8%) were male. Nearly half (48.7%) were in their third year of study, and the majority (36.1%) were from the Medicine and Surgery department. While 92.2% had heard of HBV, gaps in understanding were evident. About 37.8% erroneously believed HBV could spread via handshakes, and only 33.9% were aware HBV is treatable. Awareness of HBV’s severe complications, such as liver cirrhosis and liver cancer, was reported by 61.3%, and 83% understood that vaccination could prevent infection. Positive attitudes towards HBV vaccination were prevalent. Most participants (81.3%) supported vaccination before sexual activity, and 78.3% endorsed mandatory HBV vaccination policies for healthcare workers. However, 87.4% expressed concerns about the vaccine promoting unsafe sexual behavior, and 96.1% cited cultural resistance as a barrier to vaccination. A significant proportion (80.86%) of students had not been vaccinated against HBV. Among vaccinated students, 17.4%, 15.7%, and 47.82% had received one, two, and three doses, respectively. Barriers to vaccination included safety concerns (77.4%), lack of time (86.52%), and doubts about efficacy (42.61%). Conclusion: This study highlights gaps in knowledge and vaccination coverage among medical students, which are critical for their health and future clinical practice. Enhancing awareness and vaccination rates can empower students to advocate for preventative measures in their professional environments. Despite high awareness of HBV, knowledge gaps and cultural barriers persist, affecting attitudes and vaccination uptake among medical students. Educational interventions addressing misconceptions, cultural resistance, and vaccine safety are critical. Increased advocacy for mandatory vaccination policies in healthcare settings is also essential to improve HBV prevention methods.展开更多
Objective: This study assesses the quality of artificial intelligence chatbots in responding to standardized obstetrics and gynecology questions. Methods: Using ChatGPT-3.5, ChatGPT-4.0, Bard, and Claude to respond to...Objective: This study assesses the quality of artificial intelligence chatbots in responding to standardized obstetrics and gynecology questions. Methods: Using ChatGPT-3.5, ChatGPT-4.0, Bard, and Claude to respond to 20 standardized multiple choice questions on October 7, 2023, responses and correctness were recorded. A logistic regression model assessed the relationship between question character count and accuracy. For each incorrect question, an independent error analysis was undertaken. Results: ChatGPT-4.0 scored a 100% across both obstetrics and gynecology questions. ChatGPT-3.5 scored a 95% overall, earning an 85.7% in obstetrics and a 100% in gynecology. Claude scored a 90% overall, earning a 100% in obstetrics and an 84.6% in gynecology. Bard scored a 77.8% overall, earning an 83.3% in obstetrics and a 75% in gynecology and would not respond to two questions. There was no statistical significance between character count and accuracy. Conclusions: ChatGPT-3.5 and ChatGPT-4.0 excelled in both obstetrics and gynecology while Claude performed well in obstetrics but possessed minor weaknesses in gynecology. Bard comparatively performed the worst and had the most limitations, leading to our support of the other artificial intelligence chatbots as preferred study tools. Our findings support the use of chatbots as a supplement, not a substitute for clinician-based learning or historically successful educational tools.展开更多
Introduction: The use of radioactive radiations in healthcare facilities must comply with radioprotection safety rules in order to avoid threatening the health of workers and patients. This study aimed to assess the w...Introduction: The use of radioactive radiations in healthcare facilities must comply with radioprotection safety rules in order to avoid threatening the health of workers and patients. This study aimed to assess the working conditions, the protective measures and the medical monitoring of workers directly involved in X-ray work at hospitals in Douala, Cameroon. Materials and Methods: A descriptive cross-sectional study was carried out during the 1st quarter of 2018, across various state and private health facilities of the city of Douala. Sampling was non-random, based on convenience and all the willing participants that fulfilled the inclusion criteria were enrolled. Quantitative analyses were conducted using EPI INFO 7.0 software and the results were presented in both univariate and bivariate forms. Results: The sample consisted of 56 men and 31 women with a mean age of 34.75 ± 8.77 years. X-ray technicians were over-represented (41.38%). Day/night shift work was the main work pattern (68.96%). The distribution of work zones A&B was known by 87.5% of the participants. Hazard warning signs were effective in work zones A and B (75.86%), and the walls of the premises were also reinforced in these work zones (88.51%), but the use of radiation dosimeters was rare (9.20%). Radiation aprons (94.30%) and hand-held dosimeters (63.20%) were the most commonly used personal protective equipment. The majority of the participants did not benefit from medical follow-up by an occupational health specialist (62.1%). Conclusion: The implementation of radiation protection measures remains a significant concern in Douala based health facilities, and requires stricter administrative controls and sanctions to prevent serious health consequences for exposed staff.展开更多
Objective: In the Healthy Child Action Enhancement Program (2021-2025), it is proposed to ensure the safety and health of newborns and to promote high-quality development of health. Our department established risk ass...Objective: In the Healthy Child Action Enhancement Program (2021-2025), it is proposed to ensure the safety and health of newborns and to promote high-quality development of health. Our department established risk assessment criteria for medical adhesives in neonates by applying the best evidence in the management program for the reduction of medical adhesive-associated skin injuries in neonates, in terms of the use and removal of adhesives. Methods: A systematic search and quality assessment of topics related to medical adhesive-related skin injury in neonates was conducted to summarize the best evidence and to conduct a quality review in the neonatal unit. Results: After 2 rounds of review, medical and nursing staff in the neonatal unit had a 98% compliance rate for the knowledge of neonatal medical adhesive-related skin injury and a satisfactory compliance rate for the other 9 indicators;after the application of the evidence, the incidence of neonatal medical adhesive-related skin injury was significantly lower than that before the application of the evidence, and the differences were statistically significant (P Conclusion: The application of the best evidence-based management program in neonatal medical adhesive-associated skin injury can reduce the incidence of neonatal medical adhesive-associated skin injury, reduce neonatal infections, and improve the integrity of the protective skin barrier in neonates.展开更多
Medical image segmentation has become a cornerstone for many healthcare applications,allowing for the automated extraction of critical information from images such as Computed Tomography(CT)scans,Magnetic Resonance Im...Medical image segmentation has become a cornerstone for many healthcare applications,allowing for the automated extraction of critical information from images such as Computed Tomography(CT)scans,Magnetic Resonance Imaging(MRIs),and X-rays.The introduction of U-Net in 2015 has significantly advanced segmentation capabilities,especially for small datasets commonly found in medical imaging.Since then,various modifications to the original U-Net architecture have been proposed to enhance segmentation accuracy and tackle challenges like class imbalance,data scarcity,and multi-modal image processing.This paper provides a detailed review and comparison of several U-Net-based architectures,focusing on their effectiveness in medical image segmentation tasks.We evaluate performance metrics such as Dice Similarity Coefficient(DSC)and Intersection over Union(IoU)across different U-Net variants including HmsU-Net,CrossU-Net,mResU-Net,and others.Our results indicate that architectural enhancements such as transformers,attention mechanisms,and residual connections improve segmentation performance across diverse medical imaging applications,including tumor detection,organ segmentation,and lesion identification.The study also identifies current challenges in the field,including data variability,limited dataset sizes,and issues with class imbalance.Based on these findings,the paper suggests potential future directions for improving the robustness and clinical applicability of U-Net-based models in medical image segmentation.展开更多
Background: The availability of essential medicines and medical supplies is crucial for effectively delivering healthcare services. In Zambia, the Logistics Management Information System (LMIS) is a key tool for manag...Background: The availability of essential medicines and medical supplies is crucial for effectively delivering healthcare services. In Zambia, the Logistics Management Information System (LMIS) is a key tool for managing the supply chain of these commodities. This study aimed to evaluate the effectiveness of LMIS in ensuring the availability of essential medicines and medical supplies in public hospitals in the Copperbelt Province of Zambia. Materials and Methods: From February to April 2022, a cross-sectional study was conducted in 12 public hospitals across the Copperbelt Province. Data were collected using structured questionnaires, checklists, and stock control cards. The study assessed LMIS availability, training, and knowledge among pharmacy personnel, as well as data accuracy, product availability, and order fill rates. Descriptive statistics were used to analyse the data. Results: All surveyed hospitals had LMIS implemented and were using eLMIS as the primary LMIS. Only 47% and 48% of pharmacy personnel received training in eLMIS and Essential Medicines Logistics Improvement Program (EMLIP), respectively. Most personnel demonstrated good knowledge of LMIS, with 77.7% able to log in to eLMIS Facility Edition, 76.6% able to locate stock control cards in the system, and 78.7% able to perform transactions. However, data accuracy from physical and electronic records varied from 0% to 60%, and product availability ranged from 50% to 80%. Order fill rates from Zambia Medicines and Medical Supplies Agency (ZAMMSA) were consistently below 30%. Discrepancies were observed between physical stock counts and eLMIS records. Conclusion: This study found that most hospitals in the Copperbelt Province of Zambia have implemented LMIS use. While LMIS implementation is high in the Copperbelt Province of Zambia, challenges such as low training levels, data inaccuracies, low product availability, and order fill rates persist. Addressing these issues requires a comprehensive approach, including capacity building, data quality improvement, supply chain coordination, and investment in infrastructure and human resources. Strengthening LMIS effectiveness is crucial for improving healthcare delivery and patient outcomes in Zambia.展开更多
This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative ...This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative potentialities, issues and ethical concerns. The review consolidates findings that show the impact of AI in improving the efficiency, accuracy and justice delivery in the legal profession. The studies show increased efficiency in speed of document review and enhancement of the accuracy of the reviewed documents, with time efficiency estimates of 60% reduction of time. However, the review also outlines some of the problems that continue to characterize AI, such as data quality problems, biased algorithms and the problem of the opaque decision-making system. This paper assesses ethical issues related to patient autonomy, justice and non-malignant suffering, with particular focus on patient privacy and fair process, and on potential unfairness to patients. This paper’s review of AI innovations finds that regulations lag behind AI developments, leading to unsettled issues regarding legal responsibility for AI and user control over AI-generated results and findings in legal proceedings. Some of the future avenues that are presented in the study are the future of XAI for legal purposes, utilizing federated learning for resolving privacy issues, and the need to foster adaptive regulation. Finally, the review advocates for Legal Subject Matter Experts to collaborate with legal informatics experts, ethicists, and policy makers to develop the best solutions to implement AI in medical negligence claims. It reasons that there is great potential for AI to have a deep impact on the practice of law but when done, it must do so in a way that respects justice and on the Rights of Individuals.展开更多
Introduction: Respiratory distress is a clinical condition accompanied by an increase in work of breathing, with the respiratory accessory muscles brought into play to ensure normal arterial oxygenation. It is a major...Introduction: Respiratory distress is a clinical condition accompanied by an increase in work of breathing, with the respiratory accessory muscles brought into play to ensure normal arterial oxygenation. It is a major cause of morbidity and mortality in pediatrics. The aim of our study was to investigate the epidemiological, clinical and therapeutic aspects of respiratory distress in children aged between 1 month and 15 years seen in the emergency department of the Bangui paediatric university hospital. Methodology: This was a 3-month descriptive cross-sectional study, from January 1 to March 31, 2023. All children aged 1 month to less than 15 years presenting with respiratory distress were included. Results: A total of 3021 children were admitted to the emergency medical services of Bangui’s pediatric university hospital. Of these, 164 were included in the study. The predominance was male, with a sex ratio of 1.09. The 0 - 2 age group was the most represented, with 67 patients (42.85%). The majority of patients came from Bangui, accounting for 146 (89.02%) of cases. Respiratory difficulty (59.15%), characterized by dyspnea and cough, associated with fever, vomiting, physical asthenia and diarrhea, were the main reasons for consultation. The main pathologies noted were respiratory 92 (56.10%), followed by cardiac pathologies 21 (12.8%). Antibiotic administration (76.82%) was the most common therapeutic procedure used in the management of respiratory distress. Conclusion: Respiratory distress remains an important cause of infant mortality in our context, with major management problems.展开更多
Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it shoul...Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.展开更多
Medical image fusion technology is crucial for improving the detection accuracy and treatment efficiency of diseases,but existing fusion methods have problems such as blurred texture details,low contrast,and inability...Medical image fusion technology is crucial for improving the detection accuracy and treatment efficiency of diseases,but existing fusion methods have problems such as blurred texture details,low contrast,and inability to fully extract fused image information.Therefore,a multimodal medical image fusion method based on mask optimization and parallel attention mechanism was proposed to address the aforementioned issues.Firstly,it converted the entire image into a binary mask,and constructed a contour feature map to maximize the contour feature information of the image and a triple path network for image texture detail feature extraction and optimization.Secondly,a contrast enhancement module and a detail preservation module were proposed to enhance the overall brightness and texture details of the image.Afterwards,a parallel attention mechanism was constructed using channel features and spatial feature changes to fuse images and enhance the salient information of the fused images.Finally,a decoupling network composed of residual networks was set up to optimize the information between the fused image and the source image so as to reduce information loss in the fused image.Compared with nine high-level methods proposed in recent years,the seven objective evaluation indicators of our method have improved by 6%−31%,indicating that this method can obtain fusion results with clearer texture details,higher contrast,and smaller pixel differences between the fused image and the source image.It is superior to other comparison algorithms in both subjective and objective indicators.展开更多
With the advancement of the new medical reform,public hospitals face numerous challenges and opportunities,making the optimization of human resource allocation a critical priority.This paper analyzes the requirements ...With the advancement of the new medical reform,public hospitals face numerous challenges and opportunities,making the optimization of human resource allocation a critical priority.This paper analyzes the requirements imposed by the new medical reform on human resource allocation in public hospitals,examines existing issues such as an unbalanced personnel structure,unscientific job design,and an inadequate talent mobility mechanism,and proposes corresponding optimization strategies.These strategies include improving the recruitment and selection process,scientifically planning job structures,and establishing a flexible talent mobility mechanism.The goal is to enhance the quality of medical services,improve hospital operational efficiency,and promote the sustainable development of public hospitals.展开更多
文摘This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.
文摘Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
文摘In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.
文摘The benefits of regular physical activity are well known.Yet,few studies have examined the effectiveness of integrating physical activity(PA)into curricula within a post-secondary setting.To investigate the incorporation of PA into medical curriculum,we developed a series of optional exercise-based review sessions designed to reinforce musculoskeletal(MSK)anatomy course material.These synchronous sessions were co-taught by a group fitness instructor and an anatomy instructor.The fitness instructor would lead students through both strength and yoga style exercises,while the anatomy instructor asked questions about relevant anatomical structures related to course material previously covered.After the sessions,participants were asked to evaluate the classes on their self-reported exam preparedness in improving MSK anatomy knowledge,PA levels,and mental wellbeing.Thirty participants completed surveys;a majority agreed that the classes increased understanding of MSK concepts(90.0%)and activity levels(97.7%).Many(70.0%)felt that the classes helped reduce stress.The majority of respondents(90.0%)agreed that the classes contributed to increased feelings of social connectedness.Overall,medical students saw benefit in PA based interventions to supplement MSK course concepts.Along with increasing activity levels and promoting health behaviours,integrating PA into medical curriculum may improve comprehension of learning material,alleviate stress and foster social connectivity among medical students.
文摘Introduction and Problem Statement: Many medication errors occur during the community and hospital transition. Indeed, the World Health Organization launched the international “High 5S” project to implement medication reconciliation in healthcare facilities to reduce them and ensure patients a safe, high-quality healthcare pathway. Objective: This study aimed to detect medication errors by reconciling drug treatments and assess the relevance and feasibility of this standardized practice within the Medical Emergency Unit of the Teaching Pediatric Hospital of Ouagadougou (Burkina Faso). Methods: Patients whose parents gave their consent at their entrance were enrolled. For each patient, the pharmacy team completed a reconciliation form that included the patient’s usual treatment, which was taken and in progress and received upon admission to the medical emergency unit. Patients’ treatments were reviewed to detect and characterize discrepancies. The data of each form were reported and analyzed using KoboCollect, an Android application. Results: 135 records and 412 medication lines were captured over six weeks. The average time of treatment reconciliation per patient was 57 minutes. One thousand one hundred ninety-eight (1198) intentional discrepancies were detected, of which 6.09% were documented. Seventy-one (71) unintentional discrepancies were collected, including 39 omissions, 24 regimen dosing errors, and 8 pharmaceutical form dosage errors. Forty-nine (49) unintentional discrepancies, or 69.01%, were corrected by formulated pharmaceutical interventions toward physicians. Conclusion: Medical treatment reconciliation during hospital admission is critical because discrepancies can compromise the efficacy and/or safety of the patient’s hospital medication.
文摘Objective: To identify the principal factors associated with the occurrence and development of medical device-related pressure injuries (MDRPI) in adults admitted to hospitals. MDRPI, a peculiar subtype of pressure injuries (PI), result from the pression exerted by devices (or their fixation systems) applied for diagnostic and therapeutic purposes. MDRPI represent a serious problem for patients and healthcare systems. Understanding potential risk factors is an important step in implementing effective interventions. Methods: In this study, we will perform a systematic review;if possible, also a meta-analysis will be performed. The review will follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) reporting guidelines for systematic reviews. A rigorous literature search will be conducted both in electronic databases (Medline/PubMed, Embase, CINAHL, Web of Science, Scopus, Cochrane Library) to identify studies published since 2000 and in gray literature for unpublished studies. Pairs of researchers will identify relevant evidence, extract data, and assess risk of bias independently in each eligible study. Factors associated with the occurrence of MDRPI are considered the primary outcome. Secondary outcomes are prevalence and incidence of MDRPI, length of hospital stay, infections, and death. The evidence will be synthesized using the GRADE methodology. Results: Results are not currently available as this is a protocol for a systematic review. Conclusions: This systematic review will identify evidence on risk factors for developing MDRPI. We are confident that the results of this review will help to improve clinical practice and guide future research.
文摘Background: Hepatitis B virus (HBV) is a primary reason for liver cancer and continues to be a worldwide public health issue. The likelihood of contracting HBV is greater in healthcare workers (HCWs) compared to individuals who are not in healthcare professions. Medical students are classified as a high-risk demographic since, like HCWs, they often come into contact with bodily fluids and blood during their clinical training. By 2030, a greater proportion of people will have received HBV vaccinations, thereby halting the spread of new infections—The Somali Ministry of Health with the help of various agencies announced to eradicate hepatitis from Somalia. The priority actions are national hepatitis strategy, hepatitis survey, public awareness, training, and capacity building. Objectives: This study aims to assess the knowledge, attitude, and vaccination status of Hepatitis B infection among medical university students in Mogadishu, Somalia, 2024. Methods: Cross-sectional study design was used in this study and the survey was carried out among medical students enrolled in Universities from April 1, 2023 to June 30, 2023. The data was analyzed using SPSS version 26.0 software, Chi-square analysis and Logistic regression analysis to identify associations between demographic factors and HBV knowledge, attitudes, and vaccination status, as well as perspectives and immunization status concerning viral hepatitis. Results: The study achieved a response rate of (96%), with 230 participants. Most students (76.5%) were aged 26 - 30 years, and (60.8%) were male. Nearly half (48.7%) were in their third year of study, and the majority (36.1%) were from the Medicine and Surgery department. While 92.2% had heard of HBV, gaps in understanding were evident. About 37.8% erroneously believed HBV could spread via handshakes, and only 33.9% were aware HBV is treatable. Awareness of HBV’s severe complications, such as liver cirrhosis and liver cancer, was reported by 61.3%, and 83% understood that vaccination could prevent infection. Positive attitudes towards HBV vaccination were prevalent. Most participants (81.3%) supported vaccination before sexual activity, and 78.3% endorsed mandatory HBV vaccination policies for healthcare workers. However, 87.4% expressed concerns about the vaccine promoting unsafe sexual behavior, and 96.1% cited cultural resistance as a barrier to vaccination. A significant proportion (80.86%) of students had not been vaccinated against HBV. Among vaccinated students, 17.4%, 15.7%, and 47.82% had received one, two, and three doses, respectively. Barriers to vaccination included safety concerns (77.4%), lack of time (86.52%), and doubts about efficacy (42.61%). Conclusion: This study highlights gaps in knowledge and vaccination coverage among medical students, which are critical for their health and future clinical practice. Enhancing awareness and vaccination rates can empower students to advocate for preventative measures in their professional environments. Despite high awareness of HBV, knowledge gaps and cultural barriers persist, affecting attitudes and vaccination uptake among medical students. Educational interventions addressing misconceptions, cultural resistance, and vaccine safety are critical. Increased advocacy for mandatory vaccination policies in healthcare settings is also essential to improve HBV prevention methods.
文摘Objective: This study assesses the quality of artificial intelligence chatbots in responding to standardized obstetrics and gynecology questions. Methods: Using ChatGPT-3.5, ChatGPT-4.0, Bard, and Claude to respond to 20 standardized multiple choice questions on October 7, 2023, responses and correctness were recorded. A logistic regression model assessed the relationship between question character count and accuracy. For each incorrect question, an independent error analysis was undertaken. Results: ChatGPT-4.0 scored a 100% across both obstetrics and gynecology questions. ChatGPT-3.5 scored a 95% overall, earning an 85.7% in obstetrics and a 100% in gynecology. Claude scored a 90% overall, earning a 100% in obstetrics and an 84.6% in gynecology. Bard scored a 77.8% overall, earning an 83.3% in obstetrics and a 75% in gynecology and would not respond to two questions. There was no statistical significance between character count and accuracy. Conclusions: ChatGPT-3.5 and ChatGPT-4.0 excelled in both obstetrics and gynecology while Claude performed well in obstetrics but possessed minor weaknesses in gynecology. Bard comparatively performed the worst and had the most limitations, leading to our support of the other artificial intelligence chatbots as preferred study tools. Our findings support the use of chatbots as a supplement, not a substitute for clinician-based learning or historically successful educational tools.
文摘Introduction: The use of radioactive radiations in healthcare facilities must comply with radioprotection safety rules in order to avoid threatening the health of workers and patients. This study aimed to assess the working conditions, the protective measures and the medical monitoring of workers directly involved in X-ray work at hospitals in Douala, Cameroon. Materials and Methods: A descriptive cross-sectional study was carried out during the 1st quarter of 2018, across various state and private health facilities of the city of Douala. Sampling was non-random, based on convenience and all the willing participants that fulfilled the inclusion criteria were enrolled. Quantitative analyses were conducted using EPI INFO 7.0 software and the results were presented in both univariate and bivariate forms. Results: The sample consisted of 56 men and 31 women with a mean age of 34.75 ± 8.77 years. X-ray technicians were over-represented (41.38%). Day/night shift work was the main work pattern (68.96%). The distribution of work zones A&B was known by 87.5% of the participants. Hazard warning signs were effective in work zones A and B (75.86%), and the walls of the premises were also reinforced in these work zones (88.51%), but the use of radiation dosimeters was rare (9.20%). Radiation aprons (94.30%) and hand-held dosimeters (63.20%) were the most commonly used personal protective equipment. The majority of the participants did not benefit from medical follow-up by an occupational health specialist (62.1%). Conclusion: The implementation of radiation protection measures remains a significant concern in Douala based health facilities, and requires stricter administrative controls and sanctions to prevent serious health consequences for exposed staff.
文摘Objective: In the Healthy Child Action Enhancement Program (2021-2025), it is proposed to ensure the safety and health of newborns and to promote high-quality development of health. Our department established risk assessment criteria for medical adhesives in neonates by applying the best evidence in the management program for the reduction of medical adhesive-associated skin injuries in neonates, in terms of the use and removal of adhesives. Methods: A systematic search and quality assessment of topics related to medical adhesive-related skin injury in neonates was conducted to summarize the best evidence and to conduct a quality review in the neonatal unit. Results: After 2 rounds of review, medical and nursing staff in the neonatal unit had a 98% compliance rate for the knowledge of neonatal medical adhesive-related skin injury and a satisfactory compliance rate for the other 9 indicators;after the application of the evidence, the incidence of neonatal medical adhesive-related skin injury was significantly lower than that before the application of the evidence, and the differences were statistically significant (P Conclusion: The application of the best evidence-based management program in neonatal medical adhesive-associated skin injury can reduce the incidence of neonatal medical adhesive-associated skin injury, reduce neonatal infections, and improve the integrity of the protective skin barrier in neonates.
文摘Medical image segmentation has become a cornerstone for many healthcare applications,allowing for the automated extraction of critical information from images such as Computed Tomography(CT)scans,Magnetic Resonance Imaging(MRIs),and X-rays.The introduction of U-Net in 2015 has significantly advanced segmentation capabilities,especially for small datasets commonly found in medical imaging.Since then,various modifications to the original U-Net architecture have been proposed to enhance segmentation accuracy and tackle challenges like class imbalance,data scarcity,and multi-modal image processing.This paper provides a detailed review and comparison of several U-Net-based architectures,focusing on their effectiveness in medical image segmentation tasks.We evaluate performance metrics such as Dice Similarity Coefficient(DSC)and Intersection over Union(IoU)across different U-Net variants including HmsU-Net,CrossU-Net,mResU-Net,and others.Our results indicate that architectural enhancements such as transformers,attention mechanisms,and residual connections improve segmentation performance across diverse medical imaging applications,including tumor detection,organ segmentation,and lesion identification.The study also identifies current challenges in the field,including data variability,limited dataset sizes,and issues with class imbalance.Based on these findings,the paper suggests potential future directions for improving the robustness and clinical applicability of U-Net-based models in medical image segmentation.
文摘Background: The availability of essential medicines and medical supplies is crucial for effectively delivering healthcare services. In Zambia, the Logistics Management Information System (LMIS) is a key tool for managing the supply chain of these commodities. This study aimed to evaluate the effectiveness of LMIS in ensuring the availability of essential medicines and medical supplies in public hospitals in the Copperbelt Province of Zambia. Materials and Methods: From February to April 2022, a cross-sectional study was conducted in 12 public hospitals across the Copperbelt Province. Data were collected using structured questionnaires, checklists, and stock control cards. The study assessed LMIS availability, training, and knowledge among pharmacy personnel, as well as data accuracy, product availability, and order fill rates. Descriptive statistics were used to analyse the data. Results: All surveyed hospitals had LMIS implemented and were using eLMIS as the primary LMIS. Only 47% and 48% of pharmacy personnel received training in eLMIS and Essential Medicines Logistics Improvement Program (EMLIP), respectively. Most personnel demonstrated good knowledge of LMIS, with 77.7% able to log in to eLMIS Facility Edition, 76.6% able to locate stock control cards in the system, and 78.7% able to perform transactions. However, data accuracy from physical and electronic records varied from 0% to 60%, and product availability ranged from 50% to 80%. Order fill rates from Zambia Medicines and Medical Supplies Agency (ZAMMSA) were consistently below 30%. Discrepancies were observed between physical stock counts and eLMIS records. Conclusion: This study found that most hospitals in the Copperbelt Province of Zambia have implemented LMIS use. While LMIS implementation is high in the Copperbelt Province of Zambia, challenges such as low training levels, data inaccuracies, low product availability, and order fill rates persist. Addressing these issues requires a comprehensive approach, including capacity building, data quality improvement, supply chain coordination, and investment in infrastructure and human resources. Strengthening LMIS effectiveness is crucial for improving healthcare delivery and patient outcomes in Zambia.
文摘This critical review looks at the assessment of the application of artificial intelligence in handling legal documents with specific reference to medical negligence cases with a view of identifying its transformative potentialities, issues and ethical concerns. The review consolidates findings that show the impact of AI in improving the efficiency, accuracy and justice delivery in the legal profession. The studies show increased efficiency in speed of document review and enhancement of the accuracy of the reviewed documents, with time efficiency estimates of 60% reduction of time. However, the review also outlines some of the problems that continue to characterize AI, such as data quality problems, biased algorithms and the problem of the opaque decision-making system. This paper assesses ethical issues related to patient autonomy, justice and non-malignant suffering, with particular focus on patient privacy and fair process, and on potential unfairness to patients. This paper’s review of AI innovations finds that regulations lag behind AI developments, leading to unsettled issues regarding legal responsibility for AI and user control over AI-generated results and findings in legal proceedings. Some of the future avenues that are presented in the study are the future of XAI for legal purposes, utilizing federated learning for resolving privacy issues, and the need to foster adaptive regulation. Finally, the review advocates for Legal Subject Matter Experts to collaborate with legal informatics experts, ethicists, and policy makers to develop the best solutions to implement AI in medical negligence claims. It reasons that there is great potential for AI to have a deep impact on the practice of law but when done, it must do so in a way that respects justice and on the Rights of Individuals.
文摘Introduction: Respiratory distress is a clinical condition accompanied by an increase in work of breathing, with the respiratory accessory muscles brought into play to ensure normal arterial oxygenation. It is a major cause of morbidity and mortality in pediatrics. The aim of our study was to investigate the epidemiological, clinical and therapeutic aspects of respiratory distress in children aged between 1 month and 15 years seen in the emergency department of the Bangui paediatric university hospital. Methodology: This was a 3-month descriptive cross-sectional study, from January 1 to March 31, 2023. All children aged 1 month to less than 15 years presenting with respiratory distress were included. Results: A total of 3021 children were admitted to the emergency medical services of Bangui’s pediatric university hospital. Of these, 164 were included in the study. The predominance was male, with a sex ratio of 1.09. The 0 - 2 age group was the most represented, with 67 patients (42.85%). The majority of patients came from Bangui, accounting for 146 (89.02%) of cases. Respiratory difficulty (59.15%), characterized by dyspnea and cough, associated with fever, vomiting, physical asthenia and diarrhea, were the main reasons for consultation. The main pathologies noted were respiratory 92 (56.10%), followed by cardiac pathologies 21 (12.8%). Antibiotic administration (76.82%) was the most common therapeutic procedure used in the management of respiratory distress. Conclusion: Respiratory distress remains an important cause of infant mortality in our context, with major management problems.
文摘Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.
基金supported by Gansu Natural Science Foundation Programme(No.24JRRA231)National Natural Science Foundation of China(No.62061023)Gansu Provincial Education,Science and Technology Innovation and Industry(No.2021CYZC-04)。
文摘Medical image fusion technology is crucial for improving the detection accuracy and treatment efficiency of diseases,but existing fusion methods have problems such as blurred texture details,low contrast,and inability to fully extract fused image information.Therefore,a multimodal medical image fusion method based on mask optimization and parallel attention mechanism was proposed to address the aforementioned issues.Firstly,it converted the entire image into a binary mask,and constructed a contour feature map to maximize the contour feature information of the image and a triple path network for image texture detail feature extraction and optimization.Secondly,a contrast enhancement module and a detail preservation module were proposed to enhance the overall brightness and texture details of the image.Afterwards,a parallel attention mechanism was constructed using channel features and spatial feature changes to fuse images and enhance the salient information of the fused images.Finally,a decoupling network composed of residual networks was set up to optimize the information between the fused image and the source image so as to reduce information loss in the fused image.Compared with nine high-level methods proposed in recent years,the seven objective evaluation indicators of our method have improved by 6%−31%,indicating that this method can obtain fusion results with clearer texture details,higher contrast,and smaller pixel differences between the fused image and the source image.It is superior to other comparison algorithms in both subjective and objective indicators.
文摘With the advancement of the new medical reform,public hospitals face numerous challenges and opportunities,making the optimization of human resource allocation a critical priority.This paper analyzes the requirements imposed by the new medical reform on human resource allocation in public hospitals,examines existing issues such as an unbalanced personnel structure,unscientific job design,and an inadequate talent mobility mechanism,and proposes corresponding optimization strategies.These strategies include improving the recruitment and selection process,scientifically planning job structures,and establishing a flexible talent mobility mechanism.The goal is to enhance the quality of medical services,improve hospital operational efficiency,and promote the sustainable development of public hospitals.
