healthcare

Human resources at all levels of the organization are the main force behind building the foundation, which plays a role in providing good services with ability, clearer vision, and greater awareness. The aim of study was to model the structural equations of human resource management training in the medical sector in crisis conditions based on the experiences of COVID-19.

This study was cross-sectional and using the structural equation modeling on higher education institutions. The statistical population included 202 experts from higher education institutions. The sample size was 127 people using Morgan's table, who were selected among 202 people by stratified random sampling. Data was analyzed using Smart PLS software.

A total of 6 dimensions can be useful for teaching human resource management in crisis situations. The findings demonstrated that the Cronbach's alpha coefficient was greater than 0.70 and that the total dependability of all constructs was strong. According to the path analysis, the training route for human resource management has the most effects when it comes to strengthening transformational decision-making and handling crisis circumstances. The benchmark values for mild, medium, and strong were 0.19, 0.33, and 0.67, respectively. The coefficient of determination of innovative thinking has a weak, interaction to create coherence, emotion management and capacity building for resilience has a moderate, and deepening transformative decision-making and reactive organizational development has a strong.

The results showed that human resource training has an active role in human resource participation and crisis management and planning to solve it.

The treatment staff should properly understand infection prevention and control (IPC) methods. Adherence to IPC has significant implications and outcomes for maintaining treatment staff safety, patient protection, and environmental care.

This study was conducted to examine infection control training in doctors.

This study was conducted in Imam Khomeini Hospital in Tehran, Iran, and practitioners in internal and surgical departments participated. The research focused on infection prevention and control (IPC) through specialized workshops and questionnaires using a pre-and post-test design. The sessions included topics such as infection control, transmission, hospital-acquired infections, and components of the IPC program. Questionnaires based on the KAP method evaluated participants' knowledge, attitude, and performance. This study was approved by the university's research vice department and the research ethics committee. Data were analyzed using SPSS software, and the significance of P < 0.05 was emphasized.

Of the 251 medical residents, 189 completed the study, 122 of whom were in internal medicine and 67 in surgery. Among the 158 faculty members, 60 completed the study, 39 were in internal medicine, and 21 were in surgery. The mean pre-test scores for internal medicine residents were 15.63 (out of 30) and 13.11 for surgical residents, respectively, which increased to 22.41 and 18.09 in the post-test.

The results showed that regular and standard periodic training can improve doctors' knowledge and performance in implementing infection prevention and control programs.

Artificial intelligence (AI), particularly large language models like ChatGPT developed by OpenAI, has demonstrated potential in various domains, including medicine. While ChatGPT has shown the capability to pass rigorous exams like the United States Medical Licensing Examination (USMLE) Step 1, its proficiency in addressing breast cancer-related inquiries—a complex and prevalent disease—remains underexplored. This study aims to assess the accuracy and comprehensiveness of ChatGPT’s responses to common breast cancer questions, addressing a critical gap in the literature and evaluating its potential in enhancing patient education and support in breast cancer management.

A curated list of 100 frequently asked breast cancer questions was compiled from Cancer.net, the National Breast Cancer Foundation, and clinical practice. These questions were input into ChatGPT, and the responses were evaluated for accuracy by two primary experts using a four-point scale. Discrepancies in scoring were resolved through additional expert review.

Of the 100 responses, 5 were entirely inaccurate, 22 partially accurate, 42 accurate but lacking comprehensiveness, and 31 highly accurate. The majority of the responses were found to be at least partially accurate, demonstrating ChatGPT’s potential in providing reliable information on breast cancer.

ChatGPT shows promise as a supplementary tool for patient education on breast cancer. While generally accurate, the presence of inaccuracies underscores the need for professional oversight. The study advocates for integrating AI tools like ChatGPT in healthcare settings to support patient-provider interactions and health education, emphasizing the importance of regular updates to reflect the latest research and clinical guidelines.

Artificial intelligence (AI) has the potential to transform various aspects of healthcare. A significant challenge lies in the development of reliable and cost-effective technologies for disease detection. This study aims to create a fully automated system for the early identification of COVID-19 and non-COVID-19 cases using radiological imaging. This research involved training, analyzing, and testing three existing pre-trained deep learning (DL) models alongside a novel approach for detecting radiographic images. The models used include InceptionV3, ResNet50, and VGG16, which are widely recognized in the field. We also developed a new model, FJCovNet2, designed to expedite disease detection. The proposed DL method follows a straightforward pipeline that includes preprocessing chest images and scan images. The classification was achieved through transfer learning, with the DL model being trained after the data preprocessing phase. In this study, we developed a DL method that effectively extracts features and identifies COVID-19 from radiological images. Our findings indicate that DL models can address previously unrecognized nuances in evaluating radiological images, facilitating early disease identification. Notably, we employed the FJCovNet2 model, based on DenseNet121, to detect COVID-19 using CT scan and X-ray images. When comparing FJCovNet2 with InceptionV3, VGG16, and ResNet50-using the same dataset for training and testing-FJCovNet2 achieved the highest validation accuracy with the shortest training time. The proposed model attained an impressive accuracy of 98.23%. The results are encouraging, demonstrating that these models can accurately detect COVID-19 in radiological images. This suggests that deep learning will play a crucial role in combating the pandemic.

Healthcare pricing is intricate and influenced by numerous factors, including market structures, government regulations, access to financial resources, service costs, supply and demand. Due to this complexity, government intervention is frequently necessary to ensure equilibrium in the system. Relying solely on market forces (invisible hand) can create challenges for both healthcare providers and patients. For example, high tariffs can deter patients from pursuing essential care, leading to poorer health outcomes and contributing to economic challenges such as decreased productivity. On the other hand, low tariffs might create excessive demand for services, resulting in resource wastage and a decline in quality. Policymakers must strike a balance that emphasizes health considerations while also taking into account the medical education system, relevant laws, and political factors when establishing healthcare tariffs. The way these tariffs are determined not only affects healthcare access but also has broader implications for economic growth and workforce productivity. This highlights the need for careful management of healthcare tariffs to support both public health and economic performance.

Phytonanotechnology, the marriage of plant biology and nanotechnology, is a rapidly expanding research domain with exceptional potential to revolutionize numerous industries and address critical global challenges. Harnessing the applications of phytonanoparticles has broad-ranging impacts and solutions to some of the most pressing issues the human race is facing today. Phytonanoparticles or plant-based nanoparticles are employed in diverse fields, from healthcare to agriculture and environmental remediation. The development of phytonanoparticles highlights the potential of bio-inspired solutions for sustainable development. For example, phytonanoparticles can be engineered to encapsulate therapeutic agents, rendering them more stable and bioavailable. From cancer therapy to wound healing, they are set to usher in personalized medicine and improve patient outcomes. Nanoparticles derived from plant sources possess low cytotoxicity, biocompatibility, and biodegradability, rendering them suitable for medical and pharmaceutical applications. They hold promises to develop innovative therapies and clinical treatments that address some of the most dreaded disorders. In this editorial, we shed light on the game-changing potential of phytonanotechnology and its implications for sustainable development.

Seasonal influenza and novel H1N1 influenza from 2009 present worldwide difficulties for public health sectors. It is difficult to distinguish between significant research output due to the rising quantity of papers mentioning this infectious disease. We aimed to identify a scientometric analysis of influenza diseases. We aimed to highlight the progress made in the discipline by the researchers affiliated with most documents.

The h-index was used to evaluate the publication performance of highly cited papers. We retrieved the scientometric data using the keywords “Influenza” OR “Flu” OR “Orthomyxoviridae” AND “Antiviral agents” OR “Antiviral drugs.” In all, 59013 documents were retrieved from the Web of Science between 2011 and 2020. The exported data to Biblioshiny and Microsoft Excel tools included sources by year, active authors, active journals, and countries. Also, we made use of quantitative analysis with scientometric indicators and knowledge mapping through the VOSviewer visualization software for creating the network visualization maps.

We found most papers written in English and other languages were from 402027 authors and listed in 4443 core journals. The researchers found that Palese P produced 155 and received an h-index of 55. The author Li Y has the highest contributions, with 313 publications. In global influenza research, Europe and North America are the most productive and impactful continents. The influenza research has been published in very few journals.

This study will help hospital librarians and other library professionals to understand the status of research on influenza at any given point in time.

Electronic health records play a crucial role in modern healthcare by providing users and healthcare providers with comprehensive medical information. However, the implementation of EHR systems involves a complex balance of advantages and challenges. This study examined the strengths, weaknesses, potential benefits, and challenges related to the implementation of electronic health records.

This cross-sectional survey was conducted from May to September 2023, involving 63 electronic health record managers and 110 practitioners from health centers in Mazandaran province. A purposive sampling technique was employed, utilizing a specially designed questionnaire to evaluate different aspects of electronic health records, including their strengths, limitations, potential benefits, and possible risks. The data analysis included a combination of descriptive and analytical statistics.

The participants had an average age of 40.48±9.14 years. Electronic health record managers highlighted the rapidity and effectiveness of information access, as well as the smooth exchange of electronic data, as major advantages of electronic health record implementation. Noted weaknesses included a heavier workload for healthcare providers and inadequate equipment for EHR functionality. The potential for achieving system integration and improving access to health sector statistics was identified. Nevertheless, there were concerns regarding the security of patient information and difficulties in developing and implementing appropriate software.

The success of electronic health record implementation depends on a thorough understanding of its various aspects, including strengths, weaknesses, threats, and opportunities.

 Atherosclerotic cardiovascular disease (ASCVD) is considered a worldwide health problem associated with high morbidity, mortality, and cost of care. In the present study, we examined risk-enhancing factors for ASCVD in healthcare workers of the AZAR cohort population.

 Data from a total of 500 participants were used for this cross-sectional study. Demographic characteristics, anthropometric indices, biochemical factors, and blood pressure were assessed. To evaluate the associations of ASCVD with the parameters mentioned above, univariate and multivariate logistic regression analyses were conducted.

 The total frequency of subjects with severe (≥7.5) and low (<7.5) ASCVD was 7.6% (95% CI: 5.4-10.3), and 90.6% (95% CI: 87.7-93.0), respectively. The top strongest links were found between ASCVD and atherogenic index of plasma (AIP) (odds ratio [OR]: 12.8, 95% CI: 3.2-49.9), diabetes (OR: 7.6, 95% CI: 2.8-25), and daily smoking (OR: 7.0, 95% CI: 2.8-20). Based on a multivariate logistic regression model, low-density lipoprotein cholesterol (LDL-C)/apolipoprotein B (Apo b), diabetes, hematocrit, age, Triglycerides (TG)/high-density lipoprotein cholesterol (HDL-C), systolic blood pressure, HDL-C, apolipoprotein A-I (Apo A-I), hemoglobin, and Apo B/Apo A-I have significant associations with ASCVD severity.

 In conclusion, the present study showed significant associations between the severity of ASCVD with some parameters among healthcare workers of AZAR cohort study.