Healthcare Experts Research Articles

Lessons Learned From Extracorporeal Life Support Practice and Outcomes During the COVID-19 Pandemic.

Extracorporeal membrane oxygenation is increasingly being used to support patients with hypoxemic respiratory failure and cardiogenic shock. During the COVID-19 pandemic, consensus guidance recommended extracorporeal life support for patients with COVID-19-related cardiopulmonary disease refractory to optimal conventional therapy, prompting a substantial expansion in the use of this support modality. Extracorporeal membrane oxygenation was particularly integral to the bridging of COVID-19 patients to heart or lung transplantation. Limited human and physical resources precluded widespread utilization of mechanical support during the COVID-19 pandemic, necessitating careful patient selection and optimal management by expert healthcare teams for judicious extracorporeal membrane oxygenation use. This review outlines the evidence supporting the use of extracorporeal life support in COVID-19, describes the practice and outcomes of extracorporeal membrane oxygenation for COVID-19-related respiratory failure and cardiogenic shock, and proposes lessons learned for the implementation of extracorporeal membrane oxygenation as a bridge to transplantation in future public health emergencies.

Use of Technology in the Management and Treatment of Prediabetes, A New Era: A Case Report Series

Introduction: The escalating prevalence of prediabetes has positioned it as a silent precursor to a looming public health crisis, particularly as it paves the way for type 2 diabetes mellitus (T2DM). This case series report explores the transformative role of technology, specifically the Sugar Fit’s Diabetes Reversal and Management Program (SDRMP), in the treatment and management of prediabetic patients. Methods: The case series follows the progress of two patients aged 31-44 with prediabetes utilizing the SDRMP to prevent the progression to diabetes. The interventions for both patients involved continuous glucose monitoring, personalized nutrition plans, fitness strategies, and behavioral modifications. The patients also engaged in regular communication with their life coaches through the Sugar Fit application, ensuring ongoing support and adherence to the program. Results: The results revealed significant improvements in glycemic control, lipid profiles, and overall well-being. The first patient, a 44-year-old male, demonstrated a successful return to normal glycemic control in 177 days through a comprehensive approach involving dietary changes, exercise, and lifestyle modifications. The second patient, a 31-year-old male, achieved normal glycemic control in a comparable timeframe, showcasing the effectiveness of the SDRMP. Discussion and Conclusion: These cases demonstrate that patients were able to adhere to the goals of care, preventing the progression of prediabetes to diabetes and improving insulin resistance. The study also underscores the potential impact of technology on public health outcomes, bridging the gap between traditional interventions and the evolving needs of a tech-savvy population. The synergy between healthcare expertise and technological innovation not only arrests the progression of prediabetes but also contributes to a broader societal shift towards metabolic well-being, paving the way for a healthier future. Future monitoring is essential to ensure the maintenance of lifestyle modifications and continued success in preventing diabetes.

Advancing EEG prediction with deep learning and uncertainty estimation

Deep Learning (DL) has the potential to enhance patient outcomes in healthcare by implementing proficient systems for disease detection and diagnosis. However, the complexity and lack of interpretability impede their widespread adoption in critical high-stakes predictions in healthcare. Incorporating uncertainty estimations in DL systems can increase trustworthiness, providing valuable insights into the model’s confidence and improving the explanation of predictions. Additionally, introducing explainability measures, recognized and embraced by healthcare experts, can help address this challenge. In this study, we investigate DL models’ ability to predict sex directly from electroencephalography (EEG) data. While sex prediction have limited direct clinical application, its binary nature makes it a valuable benchmark for optimizing deep learning techniques in EEG data analysis. Furthermore, we explore the use of DL ensembles to improve performance over single models and as an approach to increase interpretability and performance through uncertainty estimation. Lastly, we use a data-driven approach to evaluate the relationship between frequency bands and sex prediction, offering insights into their relative importance. InceptionNetwork, a single DL model, achieved 90.7% accuracy and an AUC of 0.947, and the best-performing ensemble, combining variations of InceptionNetwork and EEGNet, achieved 91.1% accuracy in predicting sex from EEG data using five-fold cross-validation. Uncertainty estimation through deep ensembles led to increased prediction performance, and the models were able to classify sex in all frequency bands, indicating sex-specific features across all bands.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

Pediatric wrist fractures: variations in management across countries. An evidence-based summary of evidence.

Fractures of the distal radius are common in pediatric population, with considerable variation in the management of pediatric wrist fractures across different countries. It is crucial to consider the different approaches to conservative management and surgical intervention. The decision on the appropriate treatment method often depends on the type and severity of the fracture, as well as the available healthcare resources and expertise in each country. This article tries to identify these variations, so the various healthcare systems can work toward implementing best practices in the management of pediatric wrist fractures on a global scale. Published peer-reviewed articles identified in electronic databases, including PubMed Scopus and Google Scholar. The management of pediatric wrist fractures can differ significantly among countries given the high variability in healthcare resources and cultural practices. The management of pediatric wrist fractures can be challenging in certain countries, especially in developing regions with limited resources. Challenges such as long therapeutic delays, lack of appropriate anesthesia, and the absence of fluoroscopy can complicate the treatment process. Randomized controlled clinical trials (RCTs) are vital in providing high-quality evidence to guide clinical decision-making, especially in the field of pediatric wrist fractures. Efforts to support and prioritize the conduct and dissemination of RCTs in pediatric wrist fracture management can ultimately lead to more consistent, effective, and evidence-based care for children with wrist fractures worldwide.

Health Data Exchange: A Search Engine to Identify Health Experts

ABSTRACTThe study proposes and describes a novel system called the Health Data Exchange. HDX is useful for identifying relevant experts in healthcare based on specific skill sets and their cohorts. Initial experience suggests that an expert database based on biographical data can be developed. Several techniques for automatically identifying the critical data, data normalization, storage, and retrieval are described that supported the development of the system. Such a system has the potential to facilitate efficient search and foster global collaboration among healthcare experts.

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

Which factors influence the decision of hospitals to provide procedures on an outpatient basis? –Mixed-methods evidence from Germany

Shifting care from the resource-intensive inpatient setting to the more economically efficient outpatient sector is being promoted internationally by policymakers. Financial incentives are a major focus of such efforts because low levels of hospital outpatient care are attributed to differences in payment for inpatient and outpatient services. In Germany, however, there is significant variation in the extent to which hospitals provide outpatient care despite a uniform payment system. Therefore, other factors must be influencing German hospitals’ strategic decisions whether to offer outpatient care. While most research has focused on specific procedures or lacks empirical support, our study provides a comprehensive analysis of the factors beyond financial incentives that influence the provision of hospital outpatient services in Germany. We employed a mixed-methods approach, first contacting health care experts with in-depth knowledge of the hospital outpatient landscape to identify possible influencing factors and then conducting a comprehensive quantitative analysis of all German hospitals. Our findings suggest that policymakers seeking to promote hospital outpatient care should consider a broad range of factors. We found that a hospital's service mix, size, procedure volume, and emergency care infrastructure significantly affected the proportion of outpatient services it offered. Strategic hospital planning emphasizing specialization and adherence to minimum volume standards might therefore be a valuable policy tool. Our analysis also highlights the importance of demographic and socioeconomic factors, such as the regional share of single-person households, suggesting that a comprehensive policy framework should account for broader population characteristics and not just elements directly related to hospital care.

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

BackgroundROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. ObjectiveThis study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. MethodsThe pipeline uses different methods, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. ResultsThe pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. ConclusionThe segmentation scores were auspicious and confirmed the clinical relevance of the proposed pipeline. It has also proven to have a good generalization performance, essential for easier clinic integration. Finally, preliminary results on using CNNs showed how our work can be used to develop fully automatic tools for diagnosing ROP Plus form disease.

Risk perceptions of healthcare workers and occupational health experts on psychological distress: a qualitative mental model study

Abstract Background The objectives of this study are to explore healthcare workers’ (HCW) mental models regarding psychological distress and to compare these with that of experts. Methods Semi-structured interviews was conducted (n = 28 HCWs, n = 13 experts). Topic list encompassed risk perception, early stress symptoms, causes, consequences and preventive measures of psychological distress. Interviews were transcribed verbatim and thematically analyzed using MAXQDA. Results Similarities were found in risk perception, symptoms, causes, and consequences. Differences arose in HCWs' reliance on personal experiences and values, versus experts' scientific perspective. Preventive measures also showed discrepancies. Variation within HCWs was found on all aspects of their mental model. Conclusion For effective preventive interventions regarding psychological distress, experts should consider HCWs' personal values and experiences, acknowledging the variation in their mental models. This approach may enhance HCWs' engagement in preventive behaviors.

Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation

Artificial intelligence (AI) holds significant potential to revolutionize healthcare by improving clinical practices and patient outcomes. This research explores the integration of AI in healthcare, focusing on methodologies such as machine learning, natural language processing, and computer vision, which enable the extraction of valuable insights from complex medical imaging and clinical data. Through a comprehensive literature review, the study highlights AI’s practical applications in diagnostics, treatment planning, and predicting patient outcomes. Additionally, ethical issues, data privacy, and legal frameworks are examined, emphasizing the importance of responsible AI usage in healthcare. The findings demonstrate AI’s ability to enhance diagnostic accuracy, streamline administrative tasks, and optimize resource allocation, leading to personalized treatments and more efficient healthcare management. However, challenges remain, including data quality, algorithm transparency, and ethical concerns, which must be addressed to ensure safe and effective AI deployment. Continued research, collaboration between healthcare professionals and AI experts, and the development of robust regulatory frameworks are essential for maximizing AI’s benefits while minimizing risks. This research underscores the transformative potential of AI in healthcare and stresses the need for a multidisciplinary approach to address the ethical and regulatory complexities involved in its widespread adoption

Attitude of Students and Professors Toward Quality of Residency Medical Education at the Selected University of Medical Sciences in Tehran in 2023

Background: Clinical education is one of the most essential components of medical student education, as it significantly impacts students' professional futures and the training of healthcare experts. Objectives: This study aimed to assess the quality of clinical teaching from the perspective of residents and professors at selected medical schools in Tehran city. Methods: A descriptive cross-sectional study was conducted using the census technique on 81 residents and 48 faculty members from internal medicine, surgery, radiology, anesthesia, and physical medicine in 2023. The data collection instrument was a reliable and valid questionnaire. After gathering the data, SPSS software computed the score for each item and reported it as a percentage. Results: The research findings showed that residents reported morning reports are always held in 94% of the internal medicine group and 60% of the surgical group. Additionally, 80% of the residents and 100% of the professors in the surgery department stated that the group held regular mortality and morbidity meetings. Thirty-three percent of radiology and physical medicine residents mentioned that the hospital amenities are of good quality. The residents of the physical medicine department reported that theory classes are always held 83% of the time. Conclusions: According to the present findings, the quality of education in internal medicine and surgical courses is optimal from the perspective of both professors and students. Periodic studies on the sustainability and improvement of current educational conditions are recommended.

Teletrauma Use in US Emergency Departments.

Nearly 30 million predominantly rural US residents lack timely access to trauma care expertise available at level I or II trauma centers. Telehealth is an established approach to improve access to health care expertise using remote consultation; however, the prevalence of use of telehealth in trauma (teletrauma) across the US is not known. To examine the prevalence of, trends in, and factors associated with teletrauma use and adoption among US emergency departments (EDs). This survey study included data from the National Emergency Department Inventory (NEDI)-USA survey from January 1, 2016, to December 31, 2020. Each year, a 1-page survey was sent to the directors of nonfederal, nonspecialty EDs by mail and email up to 3 times; nonresponders were further contacted via telephone to complete the survey. Data were analyzed from January to March 2023. The primary outcome was self-reported ED use of teletrauma for each year studied. Additional measures included data regarding self-reported use of any other telehealth service and ED characteristics. Multivariable logistic regression analyses were performed to assess ED characteristics associated with teletrauma use in 2020 and teletrauma adoption between 2017 and 2020. Of 5586 EDs in the US in 2020, 4512 had available teletrauma survey data (80.8% response rate); 379 (8.4%) of these EDs reported teletrauma use. In contrast, 2726 (60.4%) reported use of any other telehealth service. Teletrauma use (among EDs with any telehealth use) ranged between 0% in Alabama; Connecticut; Washington, DC; Indiana; New Jersey; Nevada; Oklahoma; Oregon; Rhode Island; and South Carolina to more than 60% in Arkansas (39 of 64 [60.9%]), South Dakota (31 of 41 [75.6%]), and North Dakota (30 of 35 [85.7%]). Factors associated with teletrauma use included rural location (odds ratio [OR], 2.44; 95% CI, 1.77-3.36), critical access hospital (OR, 2.67; 95% CI, 1.83-3.88), and basic stroke hospital vs nonstroke hospital (OR, 1.74; 95% CI, 1.32-2.30) designations. Factors associated with adoption of teletrauma by 2020 included critical access hospital (OR, 1.98; 95% CI, 1.35-2.90) and basic stroke hospital vs nonstroke hospital (OR, 1.42; 95% CI, 1.04-1.94) designation. This survey study found that teletrauma use lagged significantly behind use of other telehealth services in US EDs in 2020. While most EDs using teletrauma were located in rural areas, there was significant state-level variation in teletrauma use. Future research is needed on how teletrauma is being used and to identify barriers to its wider implementation.

Stakeholders’ perspective on the development of a virtual clinic for patients with spinal cord injury: a qualitative study

Purpose This study aims to explore the priorities, preferences, and feedback of multiple healthcare professionals to inform the future development of virtual clinics for community-dwelling adults with spinal cord injury (SCI) in Ontario, Canada. Methods Interpretive description methodology was used to guide our exploration. Semi-structured interviews were conducted with 15 expert healthcare professionals (HCPs) involved in the care of patients with SCI. Interviews were recorded and transcribed verbatim. Interview transcripts were then analyzed using a six-phase thematic analysis approach. Results HCPs perceived virtual care to improve access to care over the long term, particularly to those living in rural areas, as well as increase connections between different providers. However, participants highlighted that in-person care is still required for management of severe SCI-related sequelae that can be life-threatening, such as pressure ulcers, spasticity, respiratory issues, and bowel and bladder complications. Conclusion Our findings can be used to inform policymakers, HCPs, and stakeholders involved with SCI rehabilitation when establishing a virtual clinic for patients with SCI. Results of this study found that policymakers and HCPs should consider hybridized (blend of virtual and in-person) healthcare and uptake of multidisciplinary approaches within the virtual healthcare systems.

Improving recognition of early clinical decline: Determining reliability and validity of the Garvey clinical warning curves.

New graduate nurses have difficulty recognizing and managing the early signs of clinical decline, resulting in a Failure to Rescue (FTR) event and preventable patient death. To address this gap, Garvey developed a series of Clinical Warning Curves as an instructional tool for new graduate nurses in an academic medical center. The Garvey Clinical Warning Curve models depict the progression of clinical changes in six body systems that occur before cardiac arrest. The purpose of this study was to establish the content validity, reliability, and usability of the Garvey Clinical Warning Curves among healthcare experts and new-graduate nurses. The current study was a cross-sectional, observational, validation survey design. Content experts used the content validity index (CVI) to evaluate the Curves. All but the temperature curves were rated as "acceptable" (CVI >0.60) for relevance, clarity, and ambiguity. The new graduate nurses who reviewed the case studies and placed patients onto the Clinical Warning Curves did so similarly, generating high intraclass correlation (ICC) scores. The usability survey components measured the perceptions of acceptability, appropriateness, and feasibility for the use of the six Clinical Warning Curves in practice settings. All components of the Curves had an average score of 4.0 or greater except for the level of complexity which scored 3.88. The Garvey Clinical Warning Curves emerged as valid and reliable tools that aid new graduate nurses in recognizing subtle signs of clinical decline. Because timely recognition and communication of clinical decline are key to preventing FTR events and avoiding patient deaths, it would be beneficial to provide the Clinical Warning Curves as a bedside resource for new graduate nurses during their orientation to the unit or within a nurse residency program.

Data-driven drug treatment: enhancing clinical decision-making with SalpPSO-optimized GraphSAGE

Safe drug recommendation systems play a crucial role in minimizing adverse drug reactions and enhancing patient safety. In this research, we propose an innovative approach to develop a safety drug recommendation system by integrating the Salp Swarm Optimization-based Particle Swarm Optimization (SalpPSO) with the GraphSAGE algorithm. The goal is to optimize the hyper parameters of GraphSAGE, enabling more accurate drug-drug interaction prediction and personalized drug recommendations. The research begins with data collection from real-world datasets, including MIMIC-III, Drug Bank, and ICD-9 ontology. The databases provide comprehensive and diverse clinical data related to patients, diseases, and drugs, forming the foundation of a knowledge graph. It represents drug-related entities and their relationships, such as drugs, indications, adverse effects, and drug-drug interactions. The knowledge graph’s integration of patient data, disease ontology, and drug information enhances the system’s accuracy to predict drug-drug interactions as well as identifying potential detrimental drug reactions. The GraphSAGE algorithm is employed as the base model for learning node embeddings in the knowledge graph. To enhance its performance, we propose the SalpPSO algorithm for hyper parameter optimization. SalpPSO combines features from Salp Swarm Optimization and Particle Swarm Optimization, offering a robust and effective optimization process. The optimized hyper parameters lead to more reliable and accurate drug recommendation system. For evaluation, the dataset is split into training and validation sets and compared the performance of the modified GraphSAGE model with SalpPSO-optimized hyper parameters to the standard models. The experimental analysis conducted in terms of various measures proves the efficiency of the proposed safe recommendation system, offering valuable for healthcare experts in making more informed and personalized drug treatment decisions for patients.

Lung Cancer Staging Using Chest CT and FDG PET/CT Free-Text Reports: Comparison Among Three ChatGPT Large-Language Models and Six Human Readers of Varying Experience.

Background: Although radiology reports are commonly used for lung cancer staging, this task can be challenging given radiologists' variable reporting styles as well as reports' potentially ambiguous and/or incomplete staging-related information. Objective: To compare performance of ChatGPT large-language models (LLMs) and human readers of varying experience in lung cancer staging using chest CT and FDG PET/CT free-text reports. Methods: This retrospective study included 700 patients (mean age, 73.8±29.5 years; 509 male, 191 female) from four institutions in Korea who underwent chest CT or FDG PET/CT for non-small cell lung cancer initial staging from January, 2020 to December, 2023. Examinations' reports used a free-text format, written exclusively in English or in mixed English and Korean. Two thoracic radiologists in consensus determined the overall stage group (IA, IB, IIA, IIB, IIIA, IIIB, IIIC, IVA, IVB) for each report using the AJCC 8th-edition staging system, establishing the reference standard. Three ChatGPT models (GPT-4o, GPT-4, GPT-3.5) determined an overall stage group for each report using a script-based application programming interface, zero-shot learning, and prompt incorporating a staging system summary. Six human readers (two fellowship-trained radiologists with lesser experience than the radiologists who determined the reference standard, two fellows, two residents) also independently determined overall stage groups. GPT-4o's overall accuracy for determining the correct stage among the nine groups was compared with that of the other LLMs and human readers using McNemar tests. Results: GPT-4o had an overall staging accuracy of 74.1%, significantly better than the accuracy of GPT-4 (70.1%, p=.02), GPT-3.5 (57.4%, p<.001), and resident 2 (65.7%, p<.001); significantly worse than the accuracy of fellowship-trained radiologist 1 (82.3%, p<.001) and fellowship-trained radiologist 2 (85.4%, p<.001); and not significantly different from the accuracy of fellow 1 (77.7%, p=.09), fellow 2 (75.6%, p=.53), and resident 1 (72.3%, p=.42). Conclusions: The best-performing model, GPT-4o, showed no significant difference in staging accuracy versus fellows, but significantly worse performance versus fellowship-trained radiologists. The findings do not support use of LLMs for lung cancer staging in place of expert healthcare professionals. Clinical Impact: The findings indicate the importance of domain expertise for performing complex specialized tasks such as cancer staging.

Biological Reference Intervals for 17α-Hydroxyprogesterone Immunoreactive Trypsinogen, and Biotinidase in Indian Newborns

Neonatal deaths, which usually occur in the first week after delivery, account for nearly 75 percent of all deaths of children under 5 years of age. Prematurity, birth difficulties, infections, and birth defects are responsible for about 40 percent of these deaths. Although mortality rates have declined since 2000, access to quality healthcare remains a major problem for mothers and infants worldwide. In perspective, the present study aimed to establish clear biological reference intervals for 17α-hydroxyprogesterone (17-OHP), immunoreactive trypsinogen (IRT), and biotinidase in Indian neonates. The statistical analysis of data from up to 3200 dried blood spot (DBS) samples of Indian newborns provided valuable information for the new cut-off values in newborn screening (NBS) programs. We applied correlation analysis to fix the relationship for NBS parameters such as 17-OHP, IRT, and biotinidase. This study provided important information about the distribution and comparison of key cut-offs for biomarkers considering body weights and gestational age in the Indian newborn population for the first time, which can help healthcare experts make easier treatment decisions.

An Overview of Explainable AI Studies in the Prediction of Sepsis Onset and Sepsis Mortality.

Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendations, explainability can gain the trust of healthcare experts and assist them in difficult diagnostic tasks. Sepsis is characterized as a serious condition that happens when the immune system of the body has an extreme response to an infection, causing tissue and organ damage and leading to death. Physicians face challenges in diagnosing and treating sepsis due to its complex pathogenesis. This work aims to provide an overview of the recent studies that propose explainable AI models in the prediction of sepsis onset and sepsis mortality using intensive care data. The general findings showed that explainable AI can provide the most significant features guiding the decision-making process of the model. Future research will investigate explainability through argumentation theory using intensive care data focused on sepsis patients.

Knowledge and attitude of university nursing students towards forensic nursing and their influencing factors: a mixed-methods study

BackgroundForensic nursing, which merges healthcare and legal expertise to address trauma and violence, is vital yet often underrepresented in nursing education. Many nursing students lack knowledge in this field, underscoring the need for enhanced education to improve patient care and legal outcomes. This study aimed to assess university nursing students’ knowledge and attitudes towards forensic nursing, as well as identify factors influencing their knowledge. A mixed-methods design was conducted at a Saudi nursing college with a convenience sample of 250 students. The data were collected using a structured questionnaire comprising two parts. The quantitative part included demographic and academic characteristics, knowledge and opinions on forensic nursing, knowledge of forensic evidence, and attitudes towards forensic nursing. The qualitative part consisted of four open-ended questions about factors affecting forensic knowledge. Data analysis involved inferential statistics and content analysis.ResultsThe study revealed that 80.4% of nursing students lacked prior knowledge of forensic nursing, and only 59.76% had some awareness of forensic evidence. A strong positive relationship was found between attitudes towards forensic nursing and knowledge of forensic evidence (r = 0.817, p < 0.001), indicating that positive attitudes could predict 66.8% of the variance in forensic evidence knowledge. Key barriers included lack of support from upper management, insufficient resources, and concerns about faculty qualifications. Students recommended integrating forensic nursing into the curriculum, offering specialized programs, and promoting research and social media awareness campaigns.ConclusionsThese findings underscore significant gaps in knowledge and misconceptions about forensic nursing among participants, emphasizing the critical need for education and awareness in this field. The study highlights the potential implications for integrating forensic nursing concepts into undergraduate curricula, enhancing faculty training, and utilizing diverse teaching modalities. Addressing these gaps will not only improve future nurses’ competency in forensic nursing, but also contribute to better patient care.