Search Results (42)

Machine Learning models, more specifically Artificial Neural Networks, are transforming medical imaging by enabling precise liver segmentation, a crucial task for diagnosing and treating liver diseases. However, these models often face challenges in adapting to diverse clinical data sources as differences in dataset volume, resolution, and origin impact generalization and performance. This study introduces a Private Data Incrementalization, a data-centric approach to enhance the adaptability of Artificial Neural Networks by progressively exposing them to varied clinical data. As the target of this study is not to propose a new image segmentation model, the existing medical imaging segmentation models—including U-Net, ResUNet++, Fully Convolutional Network, and a modified algorithm based on the Conditional Bernoulli Diffusion Model—are used. The study evaluates these four models using a curated private dataset of computed tomography scans from Coimbra University Hospital, supplemented by two public datasets, 3D-IRCADb01 and CHAOS. The Private Data Incrementalization method systematically increases the volume and diversity of training data, simulating real-world conditions where models must handle varied imaging contexts. Pre-processing and post-processing stages, incremental training, and performance evaluations reveal that structured exposure to diverse datasets improves segmentation performance, with ResUNet++ achieving the highest accuracy (0.9972) and Dice Similarity Coefficient (0.9449), and the best Average Symmetric Surface Distance (0.0053 mm), demonstrating the importance of dataset diversity and volume for segmentation models’ robustness and generalization. Private Data Incrementalization thus offers a scalable strategy for building resilient segmentation models, ultimately benefiting clinical workflows, patient care, and healthcare resource management by addressing the variability inherent in clinical imaging data. Full article

Good nutrition promotes a healthy population and mitigates the risk of disease. Integrating nutrition care in the primary healthcare system is considered an essential plan of action to manage poor nutritional status in the population. The role of primary healthcare professionals (HCPs) in the delivery of nutrition care is especially crucial due to a current lack of dietitians and dietary support in the primary care setting in Ireland. This qualitative research explored the current practice, barriers, facilitators, and feasible solutions proposed to optimize the provision of nutrition care by primary HCPs. Twenty semi-structured interviews (pharmacists (n = 14), dietitians (n = 3), a physiotherapist (n = 1), a speech and language therapist (n = 1), and a healthcare assistant (n = 1) were conducted. Six themes were derived from the data: current practice of nutrition care in primary care, perceived role, barriers and facilitators, the importance of a multidisciplinary patient-centred approach, training needs and preferences, and addressing barriers. Participants acknowledged the importance of nutrition care in clinical practice, the principal role of the dietitian as part of the multidisciplinary team, and the essential clinical competencies and nutrition training models that may facilitate the provision of nutrition care in primary practice. A paradigm shift to a multidisciplinary care model that prioritises the integration of nutrition care into primary care practice to ensure optimal dietary counselling is afforded to patients is essential. Full article

Wearable and implantable BioMEMSs (biomedical microelectromechanical systems) have transformed modern healthcare by enabling continuous, personalized, and minimally invasive monitoring, diagnostics, and therapy. Wearable BioMEMSs have advanced rapidly, encompassing a diverse range of biosensors, bioelectronic systems, drug delivery platforms, and motion tracking technologies. These devices enable non-invasive, real-time monitoring of biochemical, electrophysiological, and biomechanical signals, offering personalized and proactive healthcare solutions. In parallel, implantable BioMEMS have significantly enhanced long-term diagnostics, targeted drug delivery, and neurostimulation. From continuous glucose and intraocular pressure monitoring to programmable drug delivery and bioelectric implants for neuromodulation, these devices are improving precision treatment by continuous monitoring and localized therapy. This review explores the materials and technologies driving advancements in wearable and implantable BioMEMSs, focusing on their impact on chronic disease management, cardiology, respiratory care, and glaucoma treatment. We also highlight their integration with artificial intelligence (AI) and the Internet of Things (IoT), paving the way for smarter, data-driven healthcare solutions. Despite their potential, BioMEMSs face challenges such as regulatory complexities, global standardization, and societal determinants. Looking ahead, we explore emerging directions like multifunctional systems, biodegradable power sources, and next-generation point-of-care diagnostics. Collectively, these advancements position BioMEMS as pivotal enablers of future patient-centric healthcare systems. Full article

Artificial Intelligence (AI) methodologies have profoundly influenced healthcare research, particularly in chronic disease management and public health. This paper provides a comprehensive state-of-the-art review of AI’s applications across diabetes, cancer, epidemiology, and mortality prediction. The analysis highlights advancements in machine learning (ML), deep learning (DL), and natural language processing (NLP) that enable robust predictive models and decision support systems, leading to significant clinical and public health outcomes. The study examines predictive modeling, pattern recognition, and decision support applications, addressing their respective challenges and potential in real-world healthcare settings. Emphasis is placed on the emerging role of explainable AI (XAI), multimodal data fusion, and privacy-preserving techniques such as federated learning, which aim to enhance interpretability, robustness, and ethical compliance. This paper underscores the vital role of interdisciplinary collaboration and adaptive AI systems in creating resilient, scalable, and patient-centric healthcare solutions. Full article

The secure and efficient storage and sharing of medical images have become increasingly important due to rising security threats and performance limitations in existing healthcare systems. Centralized systems struggle to provide adequate privacy, rapid access, and reliable storage for sensitive medical images. This paper proposes a decentralized medical image-sharing framework to address these issues by integrating blockchain technology, the InterPlanetary File System (IPFS), and edge computing. Blockchain technology enforces secure patient-centric access control through smart contracts that enable patients to directly manage their data-sharing permissions. The IPFS provides decentralized and scalable storage for medical images and effectively resolves the storage limitations associated with blockchain. Edge computing enhances system responsiveness by significantly reducing latency through local data processing to ensure timely medical image access. Robust security is ensured by using elliptic curve cryptography (ECC) for secure key management and the Advanced Encryption Standard (AES) for encrypting medical images to protect against unauthorized access and data breaches. Additionally, the system includes real-time monitoring to promptly detect and respond to unauthorized access attempts to ensure continuous protection against potential security threats. System results demonstrate that the proposed framework achieves lower latency, higher throughput, and improved security compared to traditional centralized storage solutions, which makes our system suitable for practical deployment in modern healthcare settings. Full article

Integrating deep learning (DL) with the Internet of Medical Things (IoMT) is a paradigm shift in modern healthcare, offering enormous opportunities for patient care, diagnostics, and treatment. Implementing DL with IoMT has the potential to deliver better diagnosis, treatment, and patient management. However, the practical implementation has challenges, including data quality, privacy, interoperability, and limited computational resources. This survey article provides a conceptual IoMT framework for healthcare, synthesizes and identifies the state-of-the-art solutions that tackle the challenges of the current applications of DL, and analyzes existing limitations and potential future developments. Through an analysis of case studies and real-world implementations, this work provides insights into best practices and lessons learned, including the importance of robust data preprocessing, integration with legacy systems, and human-centric design. Finally, we outline future research directions, emphasizing the development of transparent, scalable, and privacy-preserving DL models to realize the full potential of IoMT in healthcare. This survey aims to serve as a foundational reference for researchers and practitioners seeking to navigate the challenges and harness the opportunities in this rapidly evolving field. Full article

The implementation of evidence-based practice (EBP) is crucial for improving patient outcomes and healthcare delivery, yet it faces significant challenges in acute care settings due to organisational barriers, resource limitations, and leadership complexities. This study explores how ward managers (WMs) facilitate knowledge utilisation (KU) and promote EBP adoption in these environments. A longitudinal qualitative case study was conducted over eight months in two acute care hospitals in the East Midlands, England. Data were collected through semi-structured interviews with 23 WMs, nonparticipant observations, and document analysis. Thematic analysis was used to identify key findings. Six themes emerged: navigating leadership challenges, overcoming organisational and resource barriers, sustaining EBP through learning networks, integrating technology, tailoring EBP to patient-centred care, and providing emotional support for staff. Hybrid leadership strategies, combining directive and collaborative approaches, were critical in addressing barriers, fostering engagement, and embedding EBP into workflows. Mentorship and resource management also played pivotal roles. The study highlights the need for tailored leadership strategies, innovative resource utilisation, and sustainable learning networks to overcome systemic challenges and promote EBP. These findings provide actionable insights for fostering evidence-informed care environments in resource-constrained acute care settings. Full article

The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs. Full article

Chemotherapy poses both physical and psychological challenges for patients, prompting many to seek answers independently through online resources. This study investigates German Google search behavior regarding chemotherapy-related terms using Google AdWords data from September 2018 to September 2022 to gain insights into patient concerns and needs. A total of 1461 search terms associated with “chemotherapy” were identified, representing 1,749,312 to 28,958,400 search queries. These terms were categorized into four groups based on frequency and analyzed. Queries related to “adjuvant” and “neoadjuvant” chemotherapy, as well as “immunotherapy”, suggest potential confusion among patients. Breast cancer emerged as the most searched tumor type, with hair loss, its management, and dermatological issues being the most searched side effects. These findings underscore the role of search engines such as Google in facilitating access to healthcare information and provide valuable insights into patient thoughts and needs. Healthcare providers can leverage this information to deliver patient-centric care and optimize treatment outcomes. Full article

The aim of this study was to explore and document the enablers and barriers of chiropractic care colocation in general practice at a large-scale private primary care centre in Australia. This study focused on the perceptions of healthcare professionals regarding this integration. The research setting was a large integrated primary care centre located in an outer metro, low-socioeconomic area in the City of Moreton Bay, Queensland, Australia. Participant inclusion criteria included general medical practitioners, practice nurses, and medical managers who self-reported interactions with the physically collocated and integrated chiropractic practice. Data was collected from 22 participants using face-to-face, qualitative, semi-structured interviews with an average duration of 32 min. The data collected included perceptions of chiropractic treatment, enablers to patient referral pathways, and views of the integrated chiropractic care model. A reflexive thematic analysis was conducted on the data set. All participants reported that this was their first exposure to the colocation of a chiropractor within a general medical practice. Four key enablers of chiropractic care integration were identified: (1) the practitioner [chiropractor], (2) the organisation [general practice], (3) consumer flow, and (4) the environment [shared spaces and tenant ecosystem]. The chiropractic integration enhanced knowledge sharing and interprofessional trust among healthcare providers. The formal reporting of patient outcomes and understanding of the chiropractor’s scope of practice further enabled referrals to the service. Shared administrative and business processes, including patient records, booking systems, and clinical meetings, facilitated relationship development between the chiropractor and referring health providers. Colocation as part of a larger primary care centre created proximity and convenience for health providers in terms of interprofessional communication, and for patients, in terms of access to chiropractic services. Existing governance structures supported communication, professional education, and shared values related to the delivery of patient-centred care. Identified barriers included limited public funding for chiropractic services resulting in reduced access for patients of low-socioeconomic status. Additionally, scepticism or negativity towards the discipline of chiropractic care was identified as an initial barrier to refer patients. In most cases, this view towards the chiropractor was overcome by regular patient reporting of positive treatment outcomes to their GP, the delivery of education sessions by the chiropractor for the health providers, and the development of interprofessional trust between the chiropractor and referring health providers. This study provides preliminary evidence and a conceptual framework of factors influencing the successful integration of chiropractic care within an Australian large primary care centre. The data collected indicated that integration of chiropractic care into a primary care centre serving a low-socioeconomic region can be achieved with a high degree of health provider satisfaction. Full article

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and personal health knowledge graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange while ensuring high levels of accuracy in data representation and health insights. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it, enhancing the precision and accuracy of the care provided. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As a proof of concept, we have demonstrated the versatility of our digital twins by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes. Full article

The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis. Full article

A number of recent studies have shown that wastage and inefficiency are a significant problem in all global healthcare systems. One initiative that could radically improve the operational efficiency of health systems is to make a paradigm shift in data ownership—that is, to transition such systems to a patient-centric model of data management by deploying blockchain technology. Such a development would not only make an economic impact, by radically cutting wastage, but would deliver significant social benefits by improving patient outcomes and satisfaction. However, a blockchain-based solution presents considerable challenges. This research seeks to understand the principal factors, which act as barriers to the acceptance of a blockchain-based patient-centric data management infrastructure, in the healthcare systems of the GCC (Gulf Cooperation Council) countries. The study represents an addition to the current literature by examining the perspectives and views of healthcare professionals and users. This approach is rare within this subject area, and is identified in existing systematic reviews as a research gap: a qualitative investigation of motivations and attitudes among these groups is a critical need. The results of the study identified 12 key barriers to the acceptance of blockchain infrastructures, thereby adding to our understanding of the challenges that need to be overcome in order to benefit from this relatively recent technology. The research is expected to be of use to healthcare authorities in planning a way forward for system improvement, particularly in terms of successfully introducing patient-centric systems. Full article

The relevance of Industry 5.0 confirms the collaborative relationship between humans and machines through an inclusive automation process. The healthcare industry at present is facilitated by the use of these emerging technologies, which promise a more personalized, patient-centric approach, enabling more prompt, cost-effective, and efficacious medical care to the affected. However, managing enormous data volumes, lack of standards, risks to data security, and regulatory obstacles, such as regulatory compliance, are critical issues that must be addressed to ensure that Industry 5.0 can be effectively integrated into the healthcare industry. This research assumes significance in the stated context as it seeks to reveal the gaps between security risks and threats assessments for personalized healthcare services based on Industry 5.0. The study’s investigations cite that the identification of security risks and various threats is an imperative need and must be prioritized so as to ensure optimal security for the healthcare system. Furthermore, the study peruses various security threats and security risk assessments for enhancing and safeguarding the healthcare industry. Moreover, the study also proposes a framework for security risk assessment based on Industry 5.0 (SRVF^HI5.0) for the healthcare security system. A step-wise procedure is applied to validate the proposed framework and provide support for designing feasible security evaluation criteria and tools for future research. Statistical analysis was performed to evaluate the measure of the applicability of multiple criteria, the tool’s reliability, and factor analysis. This offers an adequate basis for accepting the suggested risk assessment methodology based on Healthcare Industry 5.0 for implementation as well as further research and analysis. Full article

With the digitization of healthcare, an immense amount of sensitive medical data are generated and shared between various healthcare stakeholders—however, traditional health data management mechanisms present interoperability, security, and privacy challenges. The centralized nature of current health information systems leads to single points of failure, making the data vulnerable to cyberattacks. Patients also have little control over their medical records, raising privacy concerns. Blockchain technology presents a promising solution to these challenges through its decentralized, transparent, and immutable properties. This research proposes ZeroTrustBlock, a comprehensive blockchain framework for secure and private health information exchange. The decentralized ledger enhances integrity, while permissioned access and smart contracts enable patient-centric control over medical data sharing. A hybrid on-chain and off-chain storage model balances transparency with confidentiality. Integration gateways bridge ZeroTrustBlock protocols with existing systems like EHRs. Implemented on Hyperledger Fabric, ZeroTrustBlock demonstrates substantial security improvements over mainstream databases via cryptographic mechanisms, formal privacy-preserving protocols, and access policies enacting patient consent. Results validate the architecture’s effectiveness in achieving 14,200 TPS average throughput, 480 ms average latency for 100,000 concurrent transactions, and linear scalability up to 20 nodes. However, enhancements around performance, advanced cryptography, and real-world pilots are future work. Overall, ZeroTrustBlock provides a robust application of blockchain capabilities to transform security, privacy, interoperability, and patient agency in health data management. Full article