Healthcare Industry Research Articles

A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology.

Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.

Application of Artificial Intelligence Models in Nursing Research

In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research.

Redundant Telemetry Link System for Uncrewed Aerial Vehicles

Thanks to their flexibility in research, prototyping, and development, Uncrewed Aerial Vehicles (UAV) are seeing increasing use in agriculture, healthcare, logistics, and other industries. With the increase in the use of UAV in civilian sectors, comes an increase in the possibility of errors in telemetry connections. Issues with the telemetry connection may be catastrophic and cause accidents and crashes and may also adversely affect the performance of the subsystems connected to the telemetry. In this study, a low-cost software-based redundant telemetry link system for UAV is proposed. The proposed system minimises the received packet loss using packet deduplication and is compared with a dynamic link switching method. In the proposed system, both fault tolerance and robustness are introduced into the UAV telemetry link. The analysis is performed using the ArduPilot UAV Simulation environment as a producer and a Golang implementation of the proposed methods.

Automated anthropometric measurements from 3D point clouds of scanned bodies

Anthropometry plays a critical role across numerous sectors, particularly within healthcare and fashion, by facilitating the analysis of the human body structure. The significance of anthropometric data cannot be overstated; it is crucial for assessing nutritional status among children and adults alike, enabling early detection of conditions such as malnutrition, obesity, and being overweight. Furthermore, it is instrumental in creating tailored dietary interventions. This study introduces a novel automated technique for extracting anthropometric measurements from any body part. The proposed method leverages a parametric model to accurately determine the measurement parameters from either an unstructured point cloud or a mesh. We conducted a comprehensive evaluation of our approach by comparing perimetral measurements from over 400 body scans with expert assessments and existing state-of-the-art methods. The results demonstrate that our approach significantly surpasses the current methods for measuring the waist, hip, thigh, chest, and wrist perimeters with exceptional accuracy. These findings indicate the potential of our method to automate anthropometric analysis and offer efficient and accurate measurements for various applications in healthcare and fashion industries.

Developing the Right Skills for Future Success in a Digital World

This article discusses how organizations can develop skills in their employees that cannot be replicated by artificial intelligence (AI) and automation technologies. It explores three categories of skills that are likely to withstand the rise of AI: creative and design skills, social and emotional skills, and adaptability and lifelong learning. Practical guidance is provided for cultivating these skills through targeted training and developing a learning culture. Case studies of companies in the technology, healthcare and manufacturing industries are examined. The role of leadership in prioritizing skills development is also covered. The article argues that by committing resources to systematically building these human skills sets and fostering continuous learning, companies can ensure their workforce is prepared for the impacts of technological change on work. Total commitment from organizational leaders is seen as key to inspiring a culture where people feel empowered to learn and improve.

Blockchain for digital healthcare: case ttudies and adoption challenges

Background The healthcare industry is significantly transforming toward digital and smart healthcare. Blockchain, as an emerging distributed collaborative paradigm, offers a promising solution for ensuring trustworthiness and high availability of services in this evolving healthcare. This paper aims to provide a comprehensive survey of blockchain-based applications in smart healthcare. Methods We first present real-world blockchain use cases in smart healthcare and related fields, outlining the motivations for this study. Next, we review cutting-edge blockchain applications in various domains, including health data sharing, public health management, drug supply chains, insurance claims, and the Internet of Medical Things. A detailed analysis of several blockchain-based healthcare data sharing scenarios is included. Results The findings illustrate the diverse applications of blockchain technology in enhancing healthcare systems, along with a detailed examination of challenges related to technical implementation and adoption. Conclusion We discuss the challenges facing blockchain integration in smart healthcare and propose potential solutions to guide future research in this area.

Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms include hyperandrogenism, polycystic ovary, irregular menstruation cycle, excessive hair growth, etc., which sometimes may lead to more severe complications like infertility, pregnancy complications and other co-morbidities such as diabetes, hypertension, sleep apnea, etc. Early detection and effective management of PCOS are essential to enhance patients' quality of life and reduce the chances of associated health complications. Artificial intelligence (AI) techniques have recently emerged as a popular methodology in the healthcare industry for diagnosing and managing complex diseases such as PCOS. AI utilizes machine learning algorithms to analyze ultrasound images and anthropometric and biochemical test result data to diagnose PCOS quickly and accurately. AI can assist in integrating different data sources, such as patient histories, lab findings, and medical records, to present a clear and complete picture of an individual's health. This information can help the physician make more informed and efficient diagnostic decisions. This review article provides a comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.

Rapid Detection of Microplastics/Nanoplastics Directly Exposed to Blood During Intravenous Injections via Mie Scattering Spectra

Microplastics/nanoplastics (M/NPs) are pervasive in the environment, leading to inevitable human exposure through various pathways and raising significant public and scientific concern. Understanding the sources and levels of M/NPs in human blood is crucial for environmental health studies. This work examined the content, type, shape, and size of M/NPs released directly into the bloodstream from medical devices via saline solution during intravenous (IV) injection. The results of the Mie scattering spectra method show that the M/NPs content from infusion bags was 1.0 ± 0.7μg/L, mainly fibers, polyethylene, and polypropylene, with fragments being predominant. During a IV process, the initial 12mL of saline from infusion tubes contained 8.4 ± 3.6μg/L of M/NPs, primarily polyvinyl chloride and fibers. These results suggest that M/NPs exposure during IV therapy mainly originates from infusion tubing, necessitating high concern for exposure risks. Recommendations include: 1) reducing non-essential IV treatments, 2) discarding the initial 12mL of saline solution flowing through the tubing during essential IV therapy, and 3) expediting the development of legal requirements and detection standards by national authorities and the healthcare industry to mitigate the risk of M/NPs exposure in the bloodstream.

Formal Institution Networks and Coopetition: Insights from the Brazilian Private Healthcare Industry

This study demonstrates that formal institution networks are antecedent to firms adopting coopetition to improve performance. The influence of formal institution agents to foster coopetition within an industry is driven by fragilities in the institutions of emerging economies. We studied the Brazilian healthcare sector, which is characterized by strong regulation and government support. We analyzed how and to what extent coopetition relates to formal institution agents and the performance of private healthcare operators. The study employs a quantitative methodology with a sample of 149 firms from the Brazilian private healthcare system through partial least squares structural equation modelling (PLS-SEM). Our findings highlight that networks facilitated by formal institution agents precede firms’ adoption of coopetition to enhance performance. They also underscore the impact of regulatory policy on firms’ strategies and behaviors in the healthcare sector. Our findings emphasize that formal institution networks alone do not guarantee performance improvement. Healthcare firms must develop coopetitive strategies to create and capture value within these relationships. Our study sheds light on the organizational dynamics within the Brazilian private healthcare sector and provides insights for managers and policymakers. For policymakers, it highlights opportunities and challenges in enhancing healthcare sector competitiveness, emphasizing the importance of considering the institutional environment. For healthcare firm managers, it details opportunities for developing coopetitive strategies. Finally, the study is subject to boundary conditions potentially limiting the generalizability of its findings to a broader global context. Additionally, it is also limited by the cross-sectional design since respondents’ replies are specific to the current scenario in the healthcare sector.

Artificial intelligence in action: Improving breast disease management through surgical robotics and remote monitoring

ObjectivesThis article aims to discuss the transformative impact of artificial intelligence (AI) on the identification and management of breast diseases, with a specific focus on breast cancer. Materials and methodsHealthcare providers have integrated AI technologies such as surgical robots and remote monitoring systems to improve the accuracy and efficiency of breast disease diagnosis and treatment. ResultsSurgical robots equipped with AI algorithms provide real-time guidance to surgeons, analyze imaging data, and ensure precise procedures for better outcomes in breast cancer surgeries. AI-driven remote monitoring systems allow for close monitoring of patient health data, disease progression prediction, and personalized treatment recommendations. ConclusionsThe incorporation of AI technology into healthcare practices for breast diseases has revolutionized patient care delivery, enhancing outcomes, promoting patient engagement, and improving overall quality of care. These advancements offer personalized and tailored healthcare strategies that meet the unique needs of each patient, reshaping the healthcare industry.

IMPLEENTATION OF ENVIRONMENTAL, SOCIAL, AND GOVERNANCE (ESG) PRINCIPLES IN HEALTHCARE COMPANY MANAGEMENT: A COMPREHENSIVE REVIEW

The global healthcare system accounts for 4 % of the world’s carbon dioxide emissions, surpassing both aviation and shipping. Characterized by significant waste production and energy consumption, the industry faces direct challenges from climate change and requires tailored measures due to its unique attributes. Historically, the sector has prioritized social considerations, often at the expense of environmental issues. Enhancing the healthcare industry’s ESG performance is essential for achieving sustainability and Sustainable Development Goals, and it offers potential economic benefits. The primary aim of this research is to comprehensively analyze and understand the implementation of Environmental, Social, and Governance (ESG) principles within the management frameworks of the healthcare industry. The results show, most current research focuses on the value or connection that ESG brings to corporate financial performance or market performance, or pays more attention to the connection between CSR and ESG. In other words, many researchers pay more attention to the S part of ESG and pay less attention to the E and G parts. The research’s conclusions reflect the strategic importance of ESG in the global healthcare industry and underscore the importance of companies considering sustainability and social responsibility when making business strategies and operational decisions. Keywords: ESG, management, healthcare, medicine, pharmaceutical industry, sustainable development.

Digital health in your hands: A narrative review of exploring Ayushman Bharat's digital revolution

Background: “The Ayushman Bharat Digital Mission (ABDM)” is a revolutionary initiative started in India in September 2021 that aims to digitalize the country's healthcare industry. Initiatives like Ayushman Bharat Digital Mission, CoWIN App, Aarogya Setu, e-Sanjeevani, and e-Hospital provide healthcare services and facilities that reach every corner of India. This narrative review focuses on the mission's goals, objectives, innovations, significant features, progress, and worldwide scenery for digital health practices—India's healthcare scene by delivering universal health coverage and improving health outcomes. The assessment finds that ABDM is an important step toward a digitalized healthcare system that requires attention and assistance to overcome obstacles and reach its full potential. Methods: The research utilized various databases including Research Gate, PubMed, Google Scholar, and government portals to conduct a comprehensive literature review. This review article provides a critical analysis and offers essential suggestions, ideas, and recommendations for the effective and rapid implementation of the Ayushman Bharat Program. Furthermore, it includes information about the program's ability to transform India's healthcare sector and achieve universal health coverage. Conclusion: The revolutionary Ayushman Bharat Digital Mission (ABDM) aims to digitalize India's healthcare industry. It has made major achievements in improving healthcare access, increasing system effectiveness, improving care quality, and promoting teamwork amongst stakeholders in the healthcare system. By implementing strong data privacy and security protocols and developing infrastructure for India's diverse population, ABDM can play a key role in achieving national health coverage and improving health outcomes. ABDM may be vital in attaining national health coverage and greatly enhancing health outcomes with persistent work and strategic assistance.

Assessing the Level of Knowledge Regarding Medication Errors among Nurses in Karachi, Pakistan

One of the key elements influencing hospital service quality and decreasing patient safety in healthcare systems is medical error. In the healthcare industry, medication errors are a frequent source of harm and fatalities. Objective: To assess the level of knowledge among nurses regarding medication errors. Methods: This cross-sectional study was conducted in a tertiary care hospital in Karachi among 120 nurses from January 2024 to March 2024. Participants were selected through a convenient sampling technique. The sample size was calculated by open EPI softer by taking into consideration a 95% confidence level and a 5% margin of error. The data were entered and analyzed by Statistical Package Social Sciences software version 26. Results: According to the study results, 14 (12%) of the participants had a low level of knowledge, and 37 (31%), had a moderate level of knowledge. while 69 (57%) had a high level of knowledge regarding medication errors. Conclusion: This study concluded that most of the nurses had a high level of knowledge regarding medication errors.

MULTI-ENTITY STOCK DEPENDENT MODEL WITH CAPACITY AND MANUFACTURE COST RESTRAINT’S

Inventory has an impact on the manufacturing process as well as supply chain operations. The fundamental goal of this research paper is to optimize the cost associated with inventories and to provide flow less continuous production process in time. Normally, demand rate of any entity in inventory control model are treated as predictable and at the same time constant too, and that the cost associated to unit inventory must be independent and non-variable in nature. Nevertheless, in practical circumstances, the unit price and demand rate of an entity may be interconnected. When the asking for an article is enormous, an entity is manufactured in huge quantities and the static charges of manufacturing being diffused over a multiple component. Henceforth, per unit article cost decreases significantly. i.e., per unit article cot and the demand of an article are related under inverse variation. So, better to consider the demand rate of an article as a variable constraint than to fixed one. In this research article, a mathematical model for multiple articles through permitted and restricted shortage and per article cost based on demand accompanied by upper and lower limits viz restricted storage space and manufacturing expenses has been constructed. Overall, investigating the simultaneous effect of storage space and manufacturing expenses in an inventory model provides valuable insights that enable cost optimization, resource allocation, capacity planning, and risk mitigation. It helps companies make informed decisions and improve their overall operational efficiency and profitability. The Multi-Entity Stock Dependent Model with Capacity and Manufacture Cost Restraints can be customized and used in a variety of sectors that include managing inventory across numerous entities and complicated supply chain networks. Here are a few examples of industries that can benefit from such a model: manufacturing industry, the retail and distributor sector, e-commerce companies, pharmaceutical and healthcare industry, automotive industry and food and beverage industry. The article cost is explored at this juncture in a fuzzy atmosphere and solutions of the model being obtained through KKT condition. Finally, a conclusion is offered in the final portion.

Machine Learning-Driven Assessment and Security Enhancement for Electronic Health Record Systems

The digitalized patient-centric system, the Electronic Health Record (EHR), is a platform where comprehensive health information is stored, managed, and accessed electronically. The primary findings of this study aim to secure sensitive patient data and increase overall system resilience by demonstrating that machine learning can evaluate vulnerabilities and improve the security of Electronic Health Record (EHR) systems. This research examines the prospects of incorporating machine learning-driven assessment tools and safety improvements in EHRs to enhance data protection in the healthcare industry. The proposed method utilizes the implementation of machine learning classifiers, specifically the XGBoost and LightGBM models. These classifiers are employed to enhance various aspects of the system, such as data protection and security, within the framework of EHRs. The study emphasizes the efficiency of these machine learning classifiers in ensuring that EHR systems are secure enough to deal with any problem that may occur due to threats posed by external factors or hackers. The findings reveal that the XGBoost model always has outstanding performance, with a near-perfect Receiver Operating Characteristic Curve (ROC) having an AUC equal to 1.00, indicating close to perfect accuracy in distinguishing positive from negative cases. Similarly, LightGBM has a perfect ROC curve as well. Therefore, its performance would be considered flawless. Consequently, future developments could lead to sophisticated machine learning models besides those that have already been developed. Improving data storage through encryption and building safer communication protocols should also be considered to make these systems withstand new security problems. Thus, this study contributes to the existing literature on applying technology to safeguard vulnerable medical records while fostering a safe and efficient healthcare ecosystem.

Prospects of Computational Intelligence in Society: Human-Centric Solutions, Challenges, and Research Areas

The future of society could be shaped by artificial intelligence (AI), which is a part of soft computing that deals with probabilistic reasoning, neural networks, fuzzy logic, and evolutionary algorithms. This paper focuses on issues that are faced by soft computing as it moves towards human-centric solutions, outlines the challenges for soft computing, and provides possible research directions for development. Currently, there is an increasing emphasis on human-centered solutions in various fields, such as the healthcare industry, transportation sector, financial institutions, and education systems in the world today. Using soft computing techniques in this way comes with its own advantages, especially when managing unclearness or ambiguity or unsureness existing within humans' decision-making processes. Soft computing based on using neural networks may help to develop computer systems capable of faking human behavior and preferences as they evolve over time by using fuzzy logic and evolutionary algorithms. This human-centric approach helps improve the user experience while creating more symbiotic ties between technology and society, leading to examples such as personalized medicine treatment plans, among others. Soft computing’s future in society will be, to a large extent, determined by the provision of human-friendly AI while addressing the ethical, legal, and social challenges of its applications. Rights on AI represent humanity and shall be accountable. Collaborating with interdisciplinary scientists and carrying out frontier studies on developing human-friendly AI systems from ethical, legal, and social perspectives and welfare has the potential to have an influence on community and policy-maker decisions, facilitate a suitable climate for soft computing and AI in society, leverage it for empowering individuals, communities, and societies, and to improve the quality of human life while respecting human values and dignity. It also paves the way for society to be more inclusive and equitable.

AI and Cloud Computing in Healthcare: Transforming Patient Care with AWS and Java

A new era in healthcare is being ushered in by the convergence of artificial intelligence (AI) and cloud computing, which will significantly improve patient care through improved diagnosis, individualized treatment planning, and effective patient management. This article explores the potent combination of Java and Amazon Web Services (AWS) and how these technologies may be used to develop cutting-edge AI-driven healthcare solutions that optimize healthcare operations and dramatically enhance patient outcomes. Together with Java's robust and flexible programming environment, healthcare providers can leverage AWS's scalable infrastructure and advanced machine learning capabilities to create and implement sophisticated AI models that analyze massive amounts of medical data at a speed and accuracy never seen before. These developments are making it possible to make more accurate diagnoses, customized treatment plans, and proactive patient monitoring, which can result in better clinical judgment and possibly life-saving measures. But even as we consider the enormous positive effects these technologies have on society, we also have to face the ethical problems they raise, such as issues with data privacy, potential biases in algorithms, and unequal access to cutting-edge medical treatments. The goal of this thorough investigation is to present a fair assessment of the revolutionary potential of artificial intelligence (AI) and cloud computing in the healthcare industry, while also discussing the wider ramifications and obligations associated with incorporating these formidable technologies into our healthcare systems.

EVOLUTION OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA AND ITS PATH FORWARD

Artificial intelligence (AI) has brought a paradigm shift in patient management in the modern healthcare industry. This narrative literature review aims to explore the evolution of AI in the field of anesthesia and discuss its potential path forward. The increased demand for personalized care, smart decision-making tools, precision in practice, and growth in telemedicine, particularly in the realm of perioperative care, has brought AI to the forefront. By examining the existing research and applications of AI in anesthesia, this review seeks to provide insights into the current state of AI technology, its applications, implications for practicing clinicians, and the challenges and limitations that need to be addressed for its successful integration into perioperative care. This review encourages interdisciplinary teamwork between anesthesiologists, technologists, engineers, and technicians to simplify the complexities of AI implementation, address ethical issues, mitigate risks, and ensure safe perioperative care.

Green Supply Chain Practices In Healthcare Industry

Green Supply Chain Practices In Healthcare Industry

A cross-chain-based approach for secure data sharing and interoperability in electronic health records using blockchain technology

In most healthcare facilities, Electronic Health Records (EHRs) have changed paper-based medical records in the healthcare industry. Nevertheless, there are issues with credibility, management, and secure data storage with the existing EHR frameworks. In order to fill this, this work establishes a cross-chain approach for EHR record sharing that is based on the interoperability of EHRs. Different entities, like patients, healthcare providers, Government Agencies (GA), blockchain, and ledger are included in the proposed system. Several processes including initialization, registration process, agreement and verification process, bank fund transfer and certificate generation, health record exchange among two hospitals, and secret key generation, are used to implement the newly developed authentication approach. In addition, the key is obtained from the interior layers of Deep Residual SpinalNet (Deep RSNet), which is utilized to encrypt the EHR records. The Deep RSNet is the integration of Deep Residual Network (DRN) and SpinalNet. The investigation of the developed technique exposes the supremacy of the Deep RSNet model by achieving minimal values of response time, memory usage, and normalized variance of 53.677 sec, 2.682 MB, and 1.346 respectively.