Field Of Health Care Research Articles

Legal regulation of rehabilitation assistance to military personnel during martial law

The scientific article delves into the legal regulation of rehabilitation assistance to military personnel during martial law. The research, aimed at establishing the legal basis for providing rehabilitation assistance to military personnel during martial law, is underpinned by a robust methodological basis. The research methods employed are not only general scientific but also special legal, including theoretical-legal, formal-dogmatic, and comparative-legal methods. This thorough approach instills confidence in the validity of the research. The results of the research consist in determining the legal basis for providing rehabilitation assistance to military personnel. It has been established that the legal regulation of providing rehabilitation assistance to military personnel is carried out by such Laws of Ukraine as "Basics of Ukrainian legislation on health care," "On rehabilitation in the field of health care," "On social and legal protection of military personnel and their family members." The specifics of providing rehabilitation assistance to military personnel during martial law are determined. It has been established that the Ministry of Defense of Ukraine’s structure includes a network of centers for medical rehabilitation and sanatorium-resort treatment of military personnel. It has been found that rehabilitation assistance for military personnel can occur in rehabilitation institutions and healthcare institutions with rehabilitation departments, subordinated to the Ministry of Health of Ukraine or other ministries. The legal grounds for sending military personnel to provide rehabilitation in foreign healthcare institutions have been analyzed. It is indicated that the decision to send military personnel to a rehabilitation institution for rehabilitation assistance is made based on the conclusion of the military medical commission. The importance of the existing state target program for providing rehabilitation assistance to defenders of Ukraine is emphasized. A conclusion was drawn on the necessity of enshrining a separate section in the Law of Ukraine, "On Rehabilitation in the Field of Health Care," dedicated to the specifics of the rehabilitation of military, injured in hostilities.

IoT-Driven Approaches for Early Detection and Monitoring of Heart Disease: Current Trends and Future Directions

Cardiovascular disease (CVD) is a serious disease having a widespread effect on individuals across the world. Early and accurate detection of cardiac disease is crucial in healthcare, especially in the domain of cardiology. Currently, a non-invasive ultrasound imaging method is used that evaluates the structure, performance, and blood, allowing for the precise identification of a number of cardiac ailments, such as valve problems, heart failure, and congenital anomalies. These traditional techniques have some limitations, including high cost, the need for medical expertise and equipment, and the fact that they often create incorrect results due to human involvement. Furthermore, the traditional method takes more time to predict heart disease. Electrocardiogram (ECG) signals play a critical role in reducing death rates caused by CVDs, and they provide details regarding the heart patient’s health to a medical expert by employing an automated heart failure detection system. Recent developments in deep learning-based health care systems, such as ECG signal analysis, include CNN, LSTM, and other neural networks. In this research, we provide a hybrid deep learning based approach for the timely and accurate diagnosis of cardiovascular disease. The proposed system uses a hybrid of convolutional neural networks (CNN) and long short-term memory (LSTM) and utilizes the MIT-BIH ECG signal dataset for heart disease diagnosis. This study uses two different approaches with MIT-BIH arrhythmia imbalanced and balanced datasets. The first approach uses CNN and CNN-LSTM with an imbalanced dataset, and the second approach uses CNN and CNN-LSTM with a balanced dataset. The performance of both approaches was analyzed. The experimental outcomes show that the overall performance of both CNN, CNN-LSTM was excellent on a balanced dataset compared to imbalanced dataset. The proposed system achieved a better result than the previous suggested methods. Additionally, it is easy to adopt the suggested technique in the field of healthcare in order to identify heart disease.

Application of Nanotechnology to Enhance the Effectiveness and Stability of Essential Oils

The global demand for essential oils is expanding as a result of their numerous applications and increasing consumer exposure in the international market. Essential oils (EOs) are mixtures of volatile compounds including, but not limited to, phenolics, esters, ketones, terpenes, alcohols, and amides. The majority of EOs have excellent antioxidant, antibacterial, and functional activities. These natural products have conquered space in the food, drug, and cosmetic commercial arena. Unfortunately, like many lipophilic and aromatic active substances, EO presents some drawbacks such as low water solubility, strong organoleptic characteristics, low stability, and limited administration routes, which confines their Industrial use. EOs can be nanoencapsulated to increase their persistence, and bioavailability in the biological process by employing the optimum encapsulation polymers. Additionally, these particles are capable of controlling the release of the EOs promoting a prolonged preservative effect in cosmetics and foods, as well as a potential advantage for pharmaceutical dosage forms. The current research focuses on the essential features of EOs as well as their encapsulation methods. Furthermore, the potential applications of applying nanoencapsulated essential oils in the healthcare field were addressed.

Harmony of Healing: Exploring Ayurveda's Timeless Wisdom in Pharmacy Practices: A Review

Ayurveda, an ancient system of medicine originating from the Indian subcontinent, has gained significant attention and recognition worldwide. Ayurveda emphasizes a holistic approach to health and disease management, focusing on the balance between mind, body and spirit. Traditional Ayurvedic practices incorporate various therapeutic interventions, including herbal medicines, dietary modifications, yoga, meditation and lifestyle modifications. With growing interest in natural and alternative therapies, Ayurveda has become a subject of extensive scientific investigation and integration into modern pharmaceutical practices. This review is to provide an updated and comprehensive understanding of Ayurveda¢s role in pharmacy and its potential contributions to the field of healthcare.

Hospital and White Coat Fears in Children of Parents Working in the Healthcare Field: A Qualitative Study of Parental Experiences

Objectives: This qualitative study investigates the hospital and white coat fears experienced by children of healthcare professionals, focusing on parental insights and experiences. The study explores the prevalence and nature of these fears, the psychological impact on children, and the strategies employed by parents to mitigate their children's anxieties. Materials and Methods: Thirty parents working in various healthcare roles were interviewed using a semi-structured format. Thematic content analysis was used to analyze the data and identify recurring themes and sub-themes related to the children's fears and coping mechanisms. Results: The analysis revealed several key themes, including fear of medical procedures, separation anxiety, fear of medical equipment, the impact of parental roles, and coping mechanisms. Findings indicate that children's exposure to healthcare environments through their parents' professions heightens their awareness and fear of medical situations. Effective parental involvement and clear communication were identified as crucial factors in alleviating these fears. Conclusion: The study emphasizes the need for targeted interventions and supportive environments in healthcare settings to address these anxieties. Practical implications include recommendations for healthcare providers to create child-friendly environments and policies that support parental presence and psychological preparation for children. This comprehensive understanding of children's hospital-related fears can inform better practices and policies in healthcare settings to improve the psychological well-being of children of healthcare workers

Decision Support Systems for Disease Detection and Diagnosis

The last few years have been characterized by a large amount of research activity in the field of healthcare for both the improvement of diagnostic treatments and the development of simple, efficient, and multi-tasking applications [...]

Ultralow Power, Cleft Size-Adjustable and pH-Sensitive Hyaluronic Acid (HA) Biodevices for Acid-Sensing Ion Channels Emulation.

The burgeoning implantable biodevices have unlocked new frontiers in healthcare, promising personalized monitoring strategies tailored to specific needs. Herein, hyaluronic acid (HA) is harnessed to create fully biocompatible, acidity-sensitivity and cleft-adjustable neuromorphic devices. These HA-biodevices exhibit remarkable sensitivity to pH variations, effectively mimicking biological acid-sensing ion channels (ASICs) through protonation reactions between electronegative atoms and hydrogen ions, even at ultralow driving voltage (5mV). They can monitor joint cartilage acidity by tracking changes in proton concentration and successfully diagnose the onset of arthritis. Furthermore, by adjusting the synaptic device's cleft distance, which determines responsiveness, power efficiency and plasticity, HA-based neuromorphic devices can be tailored to meet the unique demands of various implantation sites, providing both high-sensitivity and low-heat dissipation, thus broadening their application scopes. Moreover, the HA-biodevices maintain stable performance across various bending degrees, up to a curvature radius of 7.5mm, with flexibility and deformation resilience enabling installation on joints of varying curvatures. The combination of all-biocompatibility, high sensitivity, low heat dissipation, ultralow low power (2 pW), and extraordinary deformation tolerance paves the way for the development of versatile, multipurpose medical monitoring devices with immense potential in the field of healthcare.

Combining A Hybrid Genetic Algorithm with A Fuzzy Logic Classifier Enhances Heart Disease Diagnosis

Because cardio illness has a significant impact on death rates worldwide, diagnosing heart disease is an essential field of healthcare. The study proposes a unique method, the HDD-GA-FL model, to enhance Heart Disease Diagnosis (HDD) that combines a hybrid genetic algorithm (GA) along with a fuzzy logic classifier (FL). The suggested hybrid system aims to overcome the difficulties brought on by the intricacy and ambiguity involved in diagnosing cardiac disease. Fuzzy logic classifiers are used to analyze ambiguous medical data, while genetic algorithms are used for choosing features and optimization. Combining these two methods provides a strong foundation for precise and effective diagnosis. Experiments on an extensive dataset with different clinical factors and cases of heart disease are conducted to assess the efficacy of the hybrid strategy. Compared to conventional diagnostic techniques, there have been significant improvements in diagnostic reliability and accuracy. When navigating the complex feature space involved in diagnosing heart illness, the combination of GA and FLC performs better than alone. It can capture minute associations and trends in the data. The suggested hybrid system has great potential for real-world application in clinical settings, providing doctors with an invaluable instrument for accurately diagnosing and detecting cardiac problems early on. This method advances the latest developments in cardiac healthcare by utilizing the complementary strengths of fuzzy logic classifiers and genetic algorithms, eventually improving patient outcomes and lowering healthcare costs.

Real-time pre-eclampsia prediction model based on IoT and machine learning

Pre-eclampsia (PET) is a hypertensive disease that occurs during pregnancy or in the postpartum period. It complicates 2% to 8% of all pregnancies and is one of the causes of more than 50,000 maternal deaths and over 500,000 fetal deaths worldwide annually. Adverse birth outcomes due to pregnancy complications have been associated with three delays: delay in recognizing the complication, delay in reaching an appropriate facility, and delay in receiving adequate care when the facility is reached. Thus prevention, timely detection, and care of pregnancy complications can prevent maternal deaths and morbidity. The Internet of Things (IoT) and machine learning (ML) technologies have become the new revolution of research in the field of healthcare. These technologies can be utilized to interconnect various sensors, monitor the health status of a patient, and predict the occurrence of an ailment. This study has designed and prototyped a pre-eclampsia monitoring model based on IoT and machine learning for remotely monitoring the health status of an expectant woman and her unborn child, to enhance early diagnosis of pre-eclampsia and improve birth outcomes. The study involved researching the on most appropriate biosensors and then designing and prototyping the pre-eclampsia watch. To build the pre-eclampsia prediction model the best ML algorithm was empirically analysed. A Naïve Bayes pre-eclampsia prediction model was found to perform better in identifying pregnant women who are at risk of pre-eclampsia after evaluation of various pre-eclampsia models built using decision trees, Naïve Bayes, K Nearest Neighbor (KNN), logistic regression, support vector machines (SVM) and Artificial neural networks (ANN). Lastly, the predictive model was integrated with the pre-eclampsia model to assist in early diagnosis of pre-eclampsia. The prototype generates alerts when the expectant woman is at risk of Pre-eclampsia. The pre-eclampsia watch model can securely capture and transmit expectant women's vital to the cloud for processing and provide timely alerts when the woman is at risk. Further research on the performance and efficacy of the model in a real environment will be done by experimenting with it in a purposively selected sample.

Artificial Intelligence System for Automated Breast Cancer Detection in Pathology in Burkina Faso: Methodology Overview.

The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represents a significant advancement in the field of healthcare. Faced with the public health issue posed by breast cancer, this study focuses on the use of AI to improve early and accurate detection of this disease from histopathological images. For the implementation of the system, we utilized a customized architecture tailored to our context where image quality is low, based on the convolutional neural networks algorithm from the Keras library of TensorFlow. Subsequently, we developed a platform to facilitate its use. This article aims to present the methodology that was used and the results obtained.

Interprofessional Simulation for Nursing and Paramedicine Students: Community Anaphylaxis Management Following Immunization.

Interprofessional simulations are becoming an important aspect of learning for nursing students. Still, the execution of these types of simulations can prove challenging. Educational institutions often struggle to access faculties from different health care fields for interprofessional simulations. There is limited literature on operationalizing and implementing interprofessional simulations related to scenarios in the community, which makes creating these simulations challenging. Nursing and paramedicine educators from a university and a community college collaborated on a simulation centered on the management of immunization anaphylaxis in the community. Feedback from facilitators and students was positive. Both disciplines agreed that collaborative learning significantly enhanced role clarification, team functioning, conflict resolution, collaboration, and communication skills with team members. Challenges included coordinating schedules, space, and training of facilitators. Collaborating with other educational institutions to establish an interprofessional simulation can be complex, but the benefits significantly outweighed the challenges.

Large-scale production of cefazolin in a microreactor with a low impurity content and high yield

Cefazolin is crucial in the field of healthcare because of its efficacy against bacterial diseases, but trace amounts of polymer impurities produced during synthesis may cause severe allergic reactions. In this work, an efficient system based on a membrane dispersion microreactor was designed for scalable cefazolin production with a high yield and low impurity content. At a production scale of 200 kg, the yield could reach 90 %, and the polymer impurity content could reach <0.008 %. Density functional theory calculations were used to investigate the formation mechanisms of the polymer impurities. The results revealed that the activation energy of cefazolin ring-opening and substitution reactions decreased by 22.4 kJ/mol and 6.4 kJ/mol respectively under alkaline conditions, resulting in increased polymer content. Computational fluid dynamics simulation indicates that the organic base in the substrate can be rapidly dispersed in the membrane dispersion microreactor, thus avoiding excessive local base concentration. In conclusion, the development of a membrane dispersion microreactor system offers a scalable and efficient method for cefazolin production with significantly lower polymer impurity content, thereby addressing a critical challenge in antibiotic synthesis and ensuring safer and more reliable healthcare applications.

A highly responsive and sensitive flexible strain sensor with conductive cross-linked network by laser direct writing

As a critical component of wearable electronic devices, flexible strain sensors have made significant contributions in the fields of smart healthcare, motion monitoring and communication, garnering escalating research interests. However, the pursuit of ideal materials synergizing high sensitivity with facile and cost-effective manufacturing methodologies remains a persistent challenge, significantly hindering the full realization of the potential of flexible strain sensors. Herein, an innovative hybrid conductive network architecture constituted by multi-walled carbon nanotubes and laser-induced graphene (MWCNTs/LIG) has been explored via a facile, cost-effective, controllable, and environmentally friendly laser direct writing (LDW) technique. Subsequently, the resultant MWCNTs/LIG composite with a cross-linked microstructure has been harnessed for the fabrication of a high-performance strain sensor. Notably, the proposed sensor exhibits remarkable sensitivity with a gauge factor (GF) reaching 192, rapid response and recovery times of 27/20 ms, and favorable durability exceeding 2300 cycles. Furthermore, the developed sensor demonstrates the capability to accurately capture full-range motions in real-time, as well as the ability to transmit information as a portable communication device. This promising discovery offers a new insight for the flourishing development of high-performance flexible strain sensors in the realm of wearable sensing.

Activities of public administration entities in the field of standardization, licensing and accreditation of healthcare in Ukraine

According to the provisions of the Basic Law of our state, life and health, honor and dignity, inviolability and security of a person are the highest social value. In order to ensure the guaranteed right to health care, the state, in the form of authorized bodies, takes the necessary organizational and legal measures and methods aimed at preserving human health. The human right to health covers a wide range of health-related phenomena and practices. Standardization, licensing and accreditation as means of ensuring the human right to health care directly belong to this spectrum of social relations. Taking into account the fact that the health care of citizens is based on a number of principles enshrined in the Constitution and laws of Ukraine, the article emphasizes that standardization, licensing and accreditation, which are the guarantee of life and health care, occupy a specific weight in health care citizens. Through the prism of legislative regulation and the analysis of subordinate regulatory legal acts, the author of the article examines the impact of regulatory legal acts on the regulation of relations in the field of standardization, licensing, and accreditation. The author focuses on the activities of the Cabinet of Ministers of Ukraine, the Ministry of Health of Ukraine, the State Service of Ukraine for Medicinal Products and Drug Control, as well as other public administration entities that play a leading role in licensing, standardization and accreditation of business entities, as well as other participants in relations in the field of health care. In general, the article draws attention to the important role of public law entities in regulating relations in the field of standardization, licensing, and accreditation, as well as draws attention to the legal mechanism of compulsory licensing. It is concluded that the modern system of public administration in the researched sphere of public relations is carried out primarily by authorized state bodies. It is emphasized that the subject of further research should be the issue of determining the system of public and private law entities involved in the process of regulating relations in the sphere of health care of Ukraine, the disclosure of the legal mechanism of compulsory licensing in order to ensure the health of the population, the defense of the state, environmental safety and other public interests, as well as the activities of public entities during the martial law regime.

Explainable multi-omics deep clustering model reveals an important role of DNA methylation in pancreatic ductal adenocarcinoma

Patients with pancreatic ductal adenocarcinoma (PDAC) have the lowest survival rate among all cancer patients in Europe. Since western societies have the highest incidence of pancreatic cancer, it has been projected that PDAC will soon become the second leading cause of cancer-related deaths. The main challenge of PDAC treatment is that patients with similar somatic genotypes exhibit a wide range of disease phenotypes. Artificial Intelligence (AI) is currently transforming the field of healthcare and represents a promising technology for integrating various datasets and optimizing evidence-based decision making. However, the interpretability of most AI models is limited and it is challenging to understand how and why a decision is made. In this study, we developed a deep clustering model for PDAC patient stratification using integrated methylation and gene expression data. We placed a specific emphasis on model explainability, with the aim to understand the hidden patterns learned by the model. The results showed two subgroups of PDAC patients with different prognoses and biological factors. The multi-omics profile analysis revealed the important role of DNA methylation. We also showed how the model was able to learn underlying patterns using both single modalities and their combinations. We hope that this study will help to promote more explainable AI in real-world clinical applications, where the knowledge of decision factors is crucial. The code of this project is publicly available in GitHub (https://github.com/albertolzs/edc_mo_pdac).

Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud

Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set.

Unlocking the potential of advanced large language models in medication review and reconciliation: A proof-of-concept investigation

BackgroundMedication review and reconciliation is essential for optimizing drug therapy and minimizing medication errors. Large language models (LLMs) have been recently shown to possess a lot of potential applications in healthcare field due to their abilities of deductive, abductive, and logical reasoning. The present study assessed the abilities of LLMs in medication review and medication reconciliation processes. MethodsFour LLMs were prompted with appropriate queries related to dosing regimen errors, drug-drug interactions, therapeutic drug monitoring, and genomics-based decision-making process. The veracity of the LLM outputs were verified from validated sources using pre-validated criteria (accuracy, relevancy, risk management, hallucination mitigation, and citations and guidelines). The impacts of the erroneous responses on the patients' safety were categorized either as major or minor. ResultsIn the assessment of four LLMs regarding dosing regimen errors, drug-drug interactions, and suggestions for dosing regimen adjustments based on therapeutic drug monitoring and genomics-based individualization of drug therapy, responses were generally consistent across prompts with no clear pattern in response quality among the LLMs. For identification of dosage regimen errors, ChatGPT performed well overall, except for the query related to simvastatin. In terms of potential drug-drug interactions, all LLMs recognized interactions with warfarin but missed the interaction between metoprolol and verapamil. Regarding dosage modifications based on therapeutic drug monitoring, Claude-Instant provided appropriate suggestions for two scenarios and nearly appropriate suggestions for the other two. Similarly, for genomics-based decision-making, Claude-Instant offered satisfactory responses for four scenarios, followed by Gemini for three. Notably, Gemini stood out by providing references to guidelines or citations even without prompting, demonstrating a commitment to accuracy and reliability in its responses. Minor impacts were noted in identifying appropriate dosing regimens and therapeutic drug monitoring, while major impacts were found in identifying drug interactions and making pharmacogenomic-based therapeutic decisions. ConclusionAdvanced LLMs hold significant promise in revolutionizing the medication review and reconciliation process in healthcare. Diverse impacts on patient safety were observed. Integrating and validating LLMs within electronic health records and prescription systems is essential to harness their full potential and enhance patient safety and care quality.

Economic Dimension Of Strategic Investments In Health: Friendship Hospitals

While health services were provided for epidemic diseases in the early periods, today they have gained a more comprehensive structure with the developing technology. The common goal in all development processes is to provide an effective health service. In this process, factors such as quality, equipment, number and quality of personnel have become determining factors in effective health service delivery. However, in addition to the existing factors, the level of development of countries also plays an important role in the provision of health services and the expenditures allocated to health services can change the level of development of the service. As a matter of fact, the good level of health services in developed countries depends on the expenditures allocated to health services. However, in some developed countries, the opposite situation may be observed and a low level of health service provision may be realized. In this respect, inter-country agreements and strategic health investments play an important role. Friendship Hospitals, which are built as a result of Turkey's agreements with other countries and can be considered as a strategic investment, can be evaluated in this context. Friendship hospitals built by Turkey in different countries have various consequences for the health system. It is thought that these results will have an impact not only on the health system of the country where the hospital is built, but also on Turkey's health system. The aim of this study is to examine and evaluate friendship hospitals, one of Turkey's strategic investments in the field of healthcare, from various perspectives

DEVELOPMENT AND IMPLEMENTATION OF A NEW PATIENT APPOINTMENT SCHEDULING SYSTEM AT ADUSTECH WUDIL: AN EVALUATION OF USER SATISFACTION AND SYSTEM IMPACT

Globally, the healthcare sector is the pivot and integral part of human lives. Any error committed in the clinic services might lead to defects or the termination of life. In the rapidly evolving field of healthcare, efficient and accessible appointment scheduling plays a crucial role in ensuring timely and quality care for patients. This article is centered around creating a Patient Appointment Scheduling System, with a focus on handling registration and management, as well as managing bookings or making appointments and scheduling sessions at Aliko Dangote University of Science and Technology, Wudil (ADUSTECH Wudil). Identification of the drawbacks of the existing traditional system leads to the design of a computerized system, which is more user-friendly and GUI-oriented. HTML, PHP, CSS, JavaScript, and MySQL were used for the development. Note that performance was improved to 92% from using the traditional system. Similarly, the productivity of participants increased to 90%. Furthermore, the waiting time for patients to be attended was enhanced to 90% in the proposed system.

Attitudes and Perceptions of Dentists and Dental Residents Practicing in the Navi Mumbai Region Toward the Use of Artificial Intelligence in Dentistry: A Descriptive Survey.

Introduction Artificial intelligence (AI) has been gaining considerable attention in recent years within the healthcare field. It has established a presence in various aspects of health sciences, including accurate diagnosis and precise, streamlined treatment. This study aimed to assess the attitudes of dental residents and dentists in the Navi Mumbai region toward the use of AI in dentistry. Methods An online questionnaire-based survey was conducted, inviting 130 dental residents and dentists from the Navi Mumbai region. The collected data were compiled on a worksheet and subjected to descriptive statistical tests, which were expressed in numbers and frequencies. Results A total of 100 responses were received. Sixty-eight percent of individuals agreed that AI helps enhance diagnosis and treatment planning in the dental field. Sixty-five percent of the respondents stated that they are most likely to incorporate AI tools into their practice within the next five years. Conclusion From the present study, it can be inferred that AI is a promising and essential subsidiary tool in dentistry as well as in healthcare as a whole. However, major concerns such as extensive, in-depth training, data security, and cybercrime must be addressed before the full-scale incorporation of AI in the health sciences.