Era Of Health Care Research Articles

BERT-Based Medical Chatbot: Enhancing Healthcare Communication through Natural Language Understanding

The advent of modern technologies like Artificial Intelligence(AI), Internet of Things(IoT) and Deep Learning(DL) has ushered in a transformative era in healthcare, offering innovative solutions towards personalized healthcare by enhancing the quality of various medical services. Our proposed methodology involves the development of a BERT-based medical chatbot, leveraging cutting-edge deep learning technology to significantly enhance healthcare communication and accessibility. The traditional challenges faced by medical chatbots, such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to offer personalized feedback, are addressed through the utilization of Bidirectional Encoder Representations from Transformers (BERT). The performance metrics of our chatbot underscore its effectiveness. With an accuracy of 98%, the chatbot ensures a high level of precision in handling medical queries. The precision score of 97% attests to the accuracy and reliability of its responses. The AUC-ROC score of 97% indicates the chatbot's exceptional ability to predict specific diseases based on user queries and symptoms, showcasing its robust predictive power. Furthermore, a recall of 96% demonstrates the chatbot's capability to avoid missing cases in medical diagnoses, ensuring comprehensive coverage of potential conditions. The F1 score of 98% showcases the chatbot's proficiency in delivering accurate and personalized healthcare information, striking a harmonious balance between precision and recall. Our BERT-based medical chatbot not only addresses the limitations of traditional approaches but also achieves a remarkable performance with high accuracy, precision, predictive power, and comprehensive coverage, making it a valuable tool for advancing the quality of healthcare services.

Defining the Dimensions of Diversity to Promote Inclusion in the Digital Era of Health Care: A Lexicon.

The pandemic provided a stark reminder of the inequities faced by populations historically marginalized by the health care system and accelerated the adoption of digital health technologies to drive innovation. Digital health technologies' purported promises to reduce inefficiencies and costs, improve access and health outcomes, and empower patients add a new level of urgency to health equity. As conventional medicine shifts toward digital medicine, we have the opportunity to intentionally develop and deploy digital health technologies with an inclusion focus. The first step is ensuring that the multiple dimensions of diversity are captured. We propose a lexicon that encompasses elements critical for implementing an inclusive approach to advancing health care quality and health services research in the digital era.

A New Era of Healthcare: The Convergence of Artificial Intelligence and Pharmaceuticals

A New Era of Healthcare: The Convergence of Artificial Intelligence and Pharmaceuticals

Patient Autonomy in Medical Education: Navigating Ethical Challenges in the Age of Artificial Intelligence.

The increasing integration of Artificial Intelligence (AI) in the medical domain signifies a transformative era in healthcare, with promises of improved diagnostics, treatment, and patient outcomes. However, this rapid technological progress brings a concomitant surge in ethical challenges permeating medical education. This paper explores the crucial role of medical educators in adapting to these changes, ensuring that ethical education remains a central and adaptable component of medical curricula. Medical educators must evolve alongside AI's advancements, becoming stewards of ethical consciousness in an era where algorithms and data-driven decision-making play pivotal roles in patient care. The traditional paradigm of medical education, rooted in foundational ethical principles, must adapt to incorporate the complex ethical considerations introduced by AI. This pedagogical approach fosters dynamic engagement, cultivating a profound ethical awareness among students. It empowers them to critically assess the ethical implications of AI applications in healthcare, including issues related to data privacy, informed consent, algorithmic biases, and technology-mediated patient care. Moreover, the interdisciplinary nature of AI's ethical challenges necessitates collaboration with fields such as computer science, data ethics, law, and social sciences to provide a holistic understanding of the ethical landscape.

Biotechnology and advanced medical technologies

This study considers a complex synergy of biotechnologies and advanced medical technologies. The growing convergence of their positions is clearly shown in shaping the transformational era in healthcare. The study exposes developments ranging from precision gene editing to organ bioprinting and the rise of personalized medicine through extensive literature reviews, case study analyses, and expert interviews. Notwithstanding that these advancements promise unprecedented therapeutic and diagnostic capabilities, they also present challenges. Technological barriers in association with profound ethical problems, e.g., the implications of gene editing on future generations and issues of equity in healthcare, emphasize the complexity of this union. The article emphasizes the potential of this integration in the development of proactive health paradigms, stressing the significance of judicious utilization, continued dialogue, and ethical stewardship. The results show the following: although the fusion of biotechnologies and medical technologies holds great promise, it requires a prudent approach to research and application.

Pharmacointelligence: Revolutionizing Pharmacy Practice in the Intelligence-driven Healthcare Era

Pharmacointelligence: Revolutionizing Pharmacy Practice in the Intelligence-driven Healthcare Era

Towards the Future of Public Health: Roadmapping Trends and Scenarios in the Post-COVID Healthcare Era.

The COVID-19 pandemic, a transformative event in modern society, has disrupted routine, work, behavior, and human relationships. Organizations, amidst the chaos, have innovatively adapted to the evolving situation. However, many countries were unprepared for the magnitude of the challenge, revealing the fragility of health responses due to inadequate leadership, insufficient resources, and poor information system integration. Structural changes in health systems are imperative, particularly in leadership, governance, human resources, financing, information systems, technology, and health service provision. This research utilizes the Technological Roadmapping method to analyze the health sector, focusing on public health, drawing on articles from SCOPUS and PubMed databases, and creating a roadmap extending to 2050. The research presents three long-term scenarios based on the literature-derived roadmap and explores various alternatives, including integrated care, telemedicine, Big Data utilization, nanotechnology, and Big Tech's AI services. The results underscore the anticipation of post-pandemic public health with high expectations, emphasizing the importance of integrating health history access, encouraging self-care, and leveraging technology for streamlined treatment. Practical implications include insights for decision makers and stakeholders to inform strategic planning and adapt to evolving industry demands, recognizing the significance of preventive services and the humanizing potential of technology.

The effect of speech pathology on automatic speaker verification: a large-scale study

Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data. While public datasets appear to offer solutions, they come with inherent risks of potential unintended exposure of patient health information via re-identification attacks. Using a comprehensive real-world pathological speech corpus, with over n=3800 test subjects spanning various age groups and speech disorders, we employed a deep-learning-driven automatic speaker verification (ASV) approach. This resulted in a notable mean equal error rate (EER) of 0.89 pm 0.06 %, outstripping traditional benchmarks. Our comprehensive assessments demonstrate that pathological speech overall faces heightened privacy breach risks compared to healthy speech. Specifically, adults with dysphonia are at heightened re-identification risks, whereas conditions like dysarthria yield results comparable to those of healthy speakers. Crucially, speech intelligibility does not influence the ASV system’s performance metrics. In pediatric cases, particularly those with cleft lip and palate, the recording environment plays a decisive role in re-identification. Merging data across pathological types led to a marked EER decrease, suggesting the potential benefits of pathological diversity in ASV, accompanied by a logarithmic boost in ASV effectiveness. In essence, this research sheds light on the dynamics between pathological speech and speaker verification, emphasizing its crucial role in safeguarding patient confidentiality in our increasingly digitized healthcare era.

Tailoring Tomorrow's Healthcare for Young Lives: Precision Medicine in Pediatrics and Advanced Neonatal Care

Precision medicine has emerged as a groundbreaking approach to healthcare, tailoring medical treatment to the individual characteristics of each patient. While its applications have been widely discussed in the context of adult medicine, its role in pediatrics and advanced neonatal care is equally promising. It is ushering in a new era in healthcare, with particular promise in the fields of pediatrics and advanced neonatal care. This article explores the transformative potential of precision medicine in optimizing treatment strategies for pediatric patients and infants in neonatal intensive care units (NICUs). By focusing on genetic, environmental, and lifestyle factors, precision medicine offers targeted therapies, early interventions, and improved outcomes. This paper reviews recent advancements, ethical considerations, and challenges associated with implementing precision medicine in pediatric and neonatal healthcare. The integration of genomic data, advanced diagnostic tools, and personalized treatment plans exemplifies the future of healthcare, promising enhanced quality of life for young patients. This article explores the current landscape of precision medicine in pediatrics, with a specific focus on neonatal care, highlighting its potential benefits, challenges, and prospects.

Revolutionizing Healthcare: Disease Prediction Through Machine Learning Algorithms

A crucial field of medical research is disease prediction, which has the potential to improve early diagnosis and therapy that can have a major impact on the course of treatment. By dramatically raising the standard of patient care and the effectiveness of the healthcare system as a whole, disease prediction plays a critical role in contemporary healthcare. Early detection of illnesses or medical issues, even before symptoms appear, is a key component of this proactive approach to healthcare management. This enables prompt interventions, better treatment outcomes, and better resource allocation. In this study, we use four different machine learning techniques to predict diseases using large datasets. Our main goal is to evaluate the effectiveness of different algorithms and determine which one performs best at accurately predicting the condition. To guarantee data quality and significance, the study makes considerable use of feature selection, engineering, and data preparation. Across various illness datasets, four machine learning algorithms, K-Nearest Neighbors, XG Boost, Ada Boost with SVM and Logistic Regression, are thoroughly examined. Accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve (AUC-ROC) are just a few of the performance criteria used to rate these algorithms. The comparative study not only identifies the algorithm with the best predicted accuracy, but it also offers insightful information about the benefits and drawbacks of each strategy. This study has significant healthcare impacts. We provide medical professionals with an effective tool for early detection and intervention by determining the algorithm that performs best at disease prediction. Improved disease prediction accuracy can result in earlier and more efficient treatment, which may save lives and lower healthcare costs. Additionally, this research opens the door for the application of sophisticated machine learning methods to clinical practice, ushering in a new era in healthcare where data-driven predictions support clinical judgment. In conclusion, by utilizing the potential of machine learning algorithms for more precise and timely disease prediction, our research supports the continual evolution of healthcare.

Biocompatibility characterization of cryogenically treated FDM printed thermoplastic polyurethane

The utilization of Additive Manufacturing (AM) has ushered in a transformative era in healthcare, presenting unprecedented possibilities for medical applications. This trend has escalated the demand for 3D printing of medical-grade materials. Cryogenic treatment, a technique known to influence the structure and morphology of biocompatible thermoplastics like ultra-high molecular weight polyethylene (UHMWPE) and thermoplastic polyurethane (TPU), holds promising potential for revolutionizing biomedical materials. The present study conducted a comprehensive biocompatibility assessment on untreated and deep cryogenically treated 3D printed TPU. Cryogenic treatment induced a roughly 2% decrease in contact angle, enhancing TPU's hydrophilicity. The Deep Cryogenic Treatment (DCT) did not exhibit any deteriorating impact on the 3D-printed TPU specimens. This study shows that the transformative potential of cryogenically treated TPU in biomedical contexts is profound and paves the way for potential breakthroughs in applying DCT on additively manufactured biomedical materials in healthcare.

Current state of the art and future directions for implantable sensors in medical technology: Clinical needs and engineering challenges.

Implantable sensors have revolutionized the way we monitor biophysical and biochemical parameters by enabling real-time closed-loop intervention or therapy. These technologies align with the new era of healthcare known as healthcare 5.0, which encompasses smart disease control and detection, virtual care, intelligent health management, smart monitoring, and decision-making. This review explores the diverse biomedical applications of implantable temperature, mechanical, electrophysiological, optical, and electrochemical sensors. We delve into the engineering principles that serve as the foundation for their development. We also address the challenges faced by researchers and designers in bridging the gap between implantable sensor research and their clinical adoption by emphasizing the importance of careful consideration of clinical requirements and engineering challenges. We highlight the need for future research to explore issues such as long-term performance, biocompatibility, and power sources, as well as the potential for implantable sensors to transform healthcare across multiple disciplines. It is evident that implantable sensors have immense potential in the field of medical technology. However, the gap between research and clinical adoption remains wide, and there are still major obstacles to overcome before they can become a widely adopted part of medical practice.

Evaluating Responsivity and Resilience in Trust in Healthcare Systems: A Scoping Literature Review

The integration of automation into clinical workflows has increased since 2004. As the next healthcare era (i.e., Health Care 4.0) approaches, it is important to understand how human-automation interaction has evolved. The objective of this research was to perform a scoping literature review on trust in automation in healthcare systems focused on: (1) Understanding the extent to which responsivity and resiliency are studied in trust in automation and (2) Understanding the extent to which automation is studied from a systems thinking perspective. This review evaluated published works between 2004 and early 2022 to determine gaps in the current literature related to evaluating trust in automation in healthcare. Over 5,000 articles were identified during the literature review process using three databases. Twenty-four articles matched the inclusion criteria of being empirical research conducted within hospital environments. The findings of this scoping literature review identify future research opportunities for investigating trust in automation.

Insomnia Treatment in the Digital Healthcare Era: Present and Future

Insomnia is a prevalent sleep disorder characterized by difficulties in falling asleep, frequent awakenings, or waking up too early. These disturbances adversely affect patient’s health. Although numerous insomnia treatment guidelines recommend cognitive behavioral therapy for insomnia (CBT-I) as the primary treatment approach, its clinical effectiveness has been hampered because of challenges such as insurance concerns, limited healthcare accessibility, inadequate compensation for providers, and a shortage of specialists. To address these limitations, CBT-I should be implemented as a digital therapeutic modality. Internationally, products such as Somryst, Sleepio, CBT-I Coach, Night Owl-Sleep Coach, and Minddistrict have been utilized. In South Korea, emerging digital therapeutic devices such as Somzz and PilLow Rx are also being introduced. In this review, we explored the current status of digital therapeutic modalities for insomnia in both international and domestic markets, aiming to provide insights into the future prospects in the realm of insomnia treatment in the modern digital healthcare era.

Chinese Health Insurance in the Digital Era: Bibliometric Study.

China has entered the era of digital health care after years of reforms in the health care system. The use of digital technologies in healthcare services is rapidly increasing, indicating the onset of a new period. The reform of health insurance has also entered a new phase. This study aims to investigate the evolution of health care insurance within the context of telemedicine and Internet Plus Healthcare (IPHC) during the digital health care era by using scientometric methods to analyze publication patterns, influential keywords, and research hot spots. It seeks to understand how health care insurance has adapted to the growing integration of IPHC and telemedicine in health care services and the implications for policy and practice. A total of 411 high-quality studies were curated from the China National Knowledge Infrastructure (CNKI) database in the Chinese language, scientometric analysis was conducted, and VOSviewer software was used to conduct a visualized analysis of keywords and hot spots in the literature. The number of articles in this field has increased notably from 2000 to 2022 and has increased annually based on a curve of y=0.332exp (0.4002x) with R2=0.6788. In total, 62 institutions and 811 authors have published research articles in the Chinese language in this field. This study included 290 keywords and formulated a total of 5 hot-topic clusters of "telemedicine," "IPHC," "internet hospital," "health insurance payments," and "health insurance system." Studies on the application of digital technologies in health care insurance has evolved from foundational studies to a broader scope. The emergence of internet hospitals has showcased the potential for integrating IPHC services into insurance payment systems. However, this development also highlights the necessity for enhanced interregional coordination mechanisms. The reform of health insurance payment is contingent upon ongoing advancements in digital technology and increased investment in electronic medical records and primary health care services. Future efforts should focus on integrating technology with administrative systems, advancing mobile health care solutions, and ensuring interoperability among various payment systems to improve efficiency and standardize health care services.

Ethical Implications Of Artificial Intelligence In Healthcare Decision-Making A Crossroads Of Social Values, Computer Algorithms, And Medical Practice

The progression of Artificial Intelligence (AI) is poised to revolutionize healthcare, augmenting human expertise with computational capabilities to transform clinical decision-making. This study delves into the intricate dynamics among societal values, computational algorithms, and medical practice, shedding light on the juncture where AI functions as a collaborative tool, elevating diagnostic precision and treatment selection while upholding the human dimension in patient care. The evolution of AI technology highlights its capacity to process extensive medical data, unveiling nuances imperceptible to human observation. Nevertheless, the assimilation of AI within clinical contexts prompts ethical considerations concerning the continuity of patient-centric care. The essence of patient-provider relationships—characterized by empathy and emotional resonance—intersects with AI's computational efficacy. This inquiry navigates this intersection by exploring models of collaboration that harmonize AI with human judgment, enriching clinical accuracy while honoring the complexities of human experience. In response to the dynamic healthcare landscape, various dimensions come into focus, spanning the augmentation of diagnostic precision to the intricate intricacies of treatment selection. These facets underscore the symbiotic rapport between AI algorithms and human expertise. Nonetheless, challenges emerge in the integration process—ensuring AI's transparency, preserving patient autonomy, and addressing potential erosion of trust. This work underscores the essential equilibrium between AI's capabilities and the essence of compassionate human care. By traversing diagnostic accuracy, treatment selection, and the preservation of human attributes, this study embarks on a holistic exploration. It navigates the metamorphosis of medical practice, steered by AI-augmented healthcare. This investigation illuminates the trajectory toward an ethically grounded, patient-centric integration of AI, heralding a transformative era in healthcare while honoring the timeless principles of empathetic care.

Blockchain for the Management of Internet of Things Devices in the Medical Industry

With the recent emergence of the smart healthcare era, and patients relying more on personalized health monitoring based on Internet of Medical Things (IoMT) devices; patients’ lives are becoming highly threatened in case they fall victim to counterfeit devices. Thus, verifying whether these body sensors utilized are authentic and reliable in an unimpeachable, credible, and auditable manner without any centralized management is of crucial importance. Furthermore, manipulating data and hijacking in an IoMT context are also of tremendous criticality. Motivated by the aforementioned challenges, in this article, a smart contract-based scalable authentication scheme dedicated for IoMT devices is proposed. The scheme mitigates the deficiencies of the traditional established systems that are extensively built on centralized approaches, vulnerable to distributed denial of service attacks, by leveraging blockchain’s decentralization and security properties. The scheme ensures confidentiality, anonymity, and privacy as it is built on a consortium blockchain and integrity by offering secure firmware updates and protects patients from counterfeit devices by leveraging the physical unclonable function. The authentication approach was implemented on Ethereum and evaluated with regard to its computation and communication costs to prove its feasibility and effectiveness as well as its security by presenting a formal analysis using ProVerif.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine.

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

The Antecedent of Clinician Engagement in Business Excellence

Problem statement: Clinician engagement has been a very important area of work in this era of healthcare. The healthcare organization realizes the importance of the clinician in successful reform & continuous quality improvement in the area of patient care, employee welfare & hence contributing to the business[1]. The feedback from healthcare leadership mentioned clinician engagement to be a challenge in the area of a robust business model[2],[5]. Objective: The objective of the study is to find the antecedent for clinician engagement & also fit in the business excellence model mentioned by the European Foundation for quality management (EFQM). Methodology: Articles in this area of work were reviewed & developed the framework for the studies. The step used to conduct the review were referred from Walker and Avant’s model of review. A huge qualitative data were coded to quantitative to measure & study the outcome from review & survey processed through ADANCO software. Findings: This study has defined clinician engagement in the healthcare organization. The study also concluded that engagement activities are performed highest in the pillar of Process (39 percent) followed by People (32 percent) and partnership & resources (16percent). Whereas the pillar of strategy & leadership has less engagement seven percent & six percent respectively. The survey was designed, and reliability & validity were checked with help of ADANCO software. The outcome of the survey mentioned the importance of the leadership role in engaging the clinicians, 70 percent of responders felt the need for the leadership’s role to be critical for engaging the clinicians. Feedback from the middle management highlighted the governance structure as a significant contributor to engaging clinicians. Conclusion: Leadership awareness & appropriate governance structure are the key antecedents for clinician engagement across all the pillars of EFQM. Keywords: Clinician, Engagement, EFQM, Physician, Healthcare, Systematic Review

Secure Biomedical Document Protection Framework to Ensure Privacy Through Blockchain.

In the recent health care era, biomedical documents play a crucial role, and they contain much evidence-based documentation associated with many stakeholders data. Protecting those confidential research documents is more difficult and effective, and a significant process in the medical-based research domain. Those bio-documentation related to health care and other relevant community-valued data are suggested by medical professionals and processed. Many traditional security mechanisms such as akteonline and Health Insurance Portability and Accountability Act (HIPAA) are used to protect the biomedical documents as they consider the problem of non-repudiation and data integrity related to the retrieval and storage of documents. Thus, there is a need for a comprehensive framework that improves protection in terms of cost and response time related to biomedical documents. In this research work, blockchain-based biomedical document protection framework (BBDPF) is proposed, which includes blockchain-based biomedical data protection (BBDP) and blockchain-based biomedical data retrieval (BBDR) algorithms. BBDP and BBDR algorithms provide consistency on the data to prevent data modification and interception of confidential data with proper data validation. Both the algorithms have strong cryptographic mechanisms to withstand post-quantum security risks, ensuring the integrity of biomedical document retrieval and non-deny of data retrieval transactions. In the performance analysis, Ethereum blockchain infrastructure is deployed BBDPF and smart contracts using Solidity language. In the performance analysis, request time and searching time are determined based on the number of request to ensure data integrity, non-repudiation, and smart contracts for the proposed hybrid model as it gets increased gradually. A modified prototype is built with a web-based interface to prove the concept and evaluate the proposed framework. The experimental results revealed that the proposed framework renders data integrity, non-repudiation, and support for smart contracts with Query Notary Service, MedRec, MedShare, and Medlock.