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The Healthcare Nutrition Council (HNC) represents manufacturers of enteral nutrition formulas and oral nutrition supplements, including those categorized as medical foods and parenteral nutrition. HNC member companies, Abbott's Nutrition Division, Nestlé Health Science, and Nutricia North America, a subsidiary of Danone S.A., manufacture a majority of the medical foods consumed in the United States. HNC is proposing a modernized interpretation of the medical food framework to reflect the evolution of nutrition science and health care. The medical food category was first defined in 1988 as part of the Orphan Drug Act. Since then, the scientific community's understanding of nutrition and the role it can play in disease management has progressed. HNC believes that a patient-centric approach is needed to foster research and innovation and to position medical foods as a viable solution in the dietary management of disease. HNC proposes that distinctive nutritional requirements refer to the clinical need for a specific nutritional intake (compared with the intake of healthy populations), which may exist by reason of abnormal physiologic manifestation or physical impairment associated with a disease or condition. The dietary management of these diseases and conditions results in clinically meaningful improvements, including but not limited to nutritional status, health outcomes, or quality of life. HNC believes that abnormal physiologic manifestation or physical impairment would include a limited, impaired, or disturbed capacity to ingest, digest, absorb, metabolize, or excrete ordinary food or certain nutrients or metabolites or other medically determined requirements for nutrients or other food substances of biological value. HNC recommends our position be considered as we build consensus across the industry. We request that the Food and Drug Administration modify and codify the current definition to reflect this. Patients and the health care system will benefit from a strong regulatory interpretation of the medical foods framework. [ABSTRACT FROM AUTHOR]

The article discusses the positive pain reduction outcomes of a personalized care pathway for chronic knee pain post-total knee replacement.

The paper is dedicated to the security measures within the concept of MetaHospital. The idea of MetaHospital is an answer to the modern challenges of time where interaction through digital media is no longer a fantasy. In this paper authors describe the idea of MetaHospital and its modules as well as the ways of personal data protection. The data protection model is given and with a detailed description of each stage. Through the incorporation of rigorous data security protocols, MetaHospital guarantees the safeguarding of patient information, cultivating a trustworthy environment for medical care and research endeavours. Committed to progress and patient well-being, MetaHospital persistently refines its practices, leveraging technology and data-driven perspectives to enhance healthcare delivery and optimize patient outcomes. [ABSTRACT FROM AUTHOR]

Previous reports show that a substantial proportion of (near) medical errors in the operating theatre is attributable to ineffective communication between healthcare professionals. Speaking up about observed medical errors is a safety behaviour which promotes effective communication between health care professionals, consequently optimising patient care by reducing medical error risk. Speaking up by healthcare professionals (e.g., nurses, residents) remains difficult to execute in practice despite increasing awareness of its importance. Therefore, this paper discourses a computational model concerning the mechanisms known from psychological, observational, and medical literature which underlie the speaking up behaviour of a health care professional. It also addresses how a doctor may respond to the communicated message. Through several scenarios we illustrate what pattern of factors causes a healthcare professional to speak up when witnessing a (near) medical error. We moreover demonstrate how introducing an observant agent can facilitate effective communication and help to ensure patient safety through speaking up when a nurse can not. In conclusion, the current paper introduces an adaptive computational model which predicts speaking up behaviour from the perspective of the speaker and receiver, with the addition of a virtual coach to further optimise patient safety when a patient could be in harm's way. [ABSTRACT FROM AUTHOR]

The rapidly developing scene of Healthcare 4.0, has turned into a fundamental concern. This paper dives into the multi-layered domain of medical care network safety, with specific emphasis on the persistent phishing assaults. We examine the motivation for cyberattacks and the challenges involved. In this paper we review the phishing attacks in medical care, talking about preventive measures to handle this danger. By embracing this through methodology, Healthcare organizations can sustain their protections, shield patient information, and maintain the trustworthiness of medical care frameworks, all while encouraging a culture of network safety status in the computerized age. [ABSTRACT FROM AUTHOR]

In this paper, we present a review of several selected talks presented at the CTTACC conference (Cellular Therapies in Trauma and Critical Care) held in Scottsdale, AZ in May 2023. This conference review highlights the potential for cellular therapies to "reset" the dysregulated immune response and restore physiologic functions to normal. Improvements in medical care systems and technology have increasingly saved lives after major traumatic events. However, many of these patients have complicated post-traumatic sequelae, ranging from short-term multi-organ failure to chronic critical illness. Patients with chronic critical illness have been found to have dysregulated immune responses. These abnormal and harmful immune responses persist for years after the initial insult and can potentially be mitigated by treatment with cellular therapies. The sessions emphasized the need for more research and clinical trials with cellular therapies for the treatment of a multitude of chronic illnesses: post-trauma, radiation injury, COVID-19, burns, traumatic brain injury (TBI) and other chronic infections. [ABSTRACT FROM AUTHOR]

Digital remote care has emerged as a transformative approach in the delivery of healthcare services. This paper presents an empirical case study that investigates the utilization of digital remote care for postoperative rehabilitation in a relatively large-scale implementation in China. To gather the empirical data, we employed a mixed-methods approach including observations, document analysis, semi-structured interviews, and a participatory workshop. The findings obtained from these diverse sources were documented and analyzed through journey mapping, which provides a holistic view of the process of delivering digital remote care services. Drawing from the journey map, we gain insights into the specific problems faced by health professionals and use this knowledge to identify areas for improvement. Simultaneously, we recognize opportunities to enhance the overall digital remote care experience and facilitate a successful large-scale implementation. [ABSTRACT FROM AUTHOR]

The European health care system is moving toward personalised, distributed, and home-based services. This is made possible via new and improved connected medical devices (CMDs), and will benefit health care providers in terms of reduced cost and improved service. Patients will see improved quality of life in terms of reduced travel time and reduced stress via treatment at home or where they want it. However, for these benefits to be fully realised, the cybersecurity of CMDs needs to be ensured. This paper presents a brief survey of challenges to building security into CMDs, and introduces NEMECYS, an EU-funded project which will help practitioners to (a) comply with Medical Device (MD) regulations; (b) be able to apply proportionate MD cybersecurity, and (c) build in cybersecurity by design for both MDs and the connected scenarios they operate in. [ABSTRACT FROM AUTHOR]

The SmartEUS project aims to design, develop, and test a highly innovative model for the provision of Emergency Urgency Services (EUS) based on the concept of clinical pathway (i.e., the entire emergency care chain). The project is innovative both from an organizational and technological point of view. It is based on an e-Health approach, capable of exploiting new digital technologies in a coordinated way to support the evolution of emergency health systems towards dynamic and safer processes. The paper presents the SmartEUS platform which can integrate the information from all the key players in medical emergency response (emergency operation center, emergency medical service, hospital emergency department) improving their coordination. The most relevant innovations of the proposed solution are data integration and interoperability, advanced communication and collaboration, knowledge-based resource allocation, telemedicine integration to support decision making in dispatching and resource assignment. In fact, a Decision Support System is integrated into SmartEUS cloud platform to suggest to the emergency operation center which emergency medical service and hospital emergency department. In fact, information on the patient status can include results from ambulance's on board telediagnosis equipment, alerting the hospital emergency department even before the arrive of the patient, thus guaranteeing the appropriateness of care according to patient needs (e.g., patient pre-triage index, potential pre-hospital diagnosis) and reducing emergency department overcrowding and resources saturation. [ABSTRACT FROM AUTHOR]

Timely diagnosis is crucial for the successful treatment of a serious medical condition like brain hemorrhage. Deep learning algorithms have shown great promise in applications for medical image analysis, like the identification of brain hemorrhages. The goal of this study is to assess how well various deep learning algorithms can detect brain hemorrhages. Using a suitable dataset, the study evaluates the computational efficiency, accuracy, sensitivity, and specificity of the selected algorithms. The results demonstrate the potential of deep learning models to assist physicians in identifying this potentially fatal condition and demonstrate how well they can identify brain hemorrhages. The study's findings improve automated brain hemorrhage detection technology, improving patient outcomes and the efficiency of healthcare delivery. EfficientNetB3 typically achieves higher accuracy due to its increased model complexity. Despite its increased complexity, EfficientNetB3 is still more parameter-efficient and computationally efficient than many alternative architectures. EfficientNetB3's strong performance and feature extraction capabilities make it a good choice for transfer learning tasks. Out of all the models implemented in this paper, the proposed model with EfficientNetB3 gave the best accuracy for training as well as validation i.e. 99.95% and 93.29% respectively followed by EfficientNetB2, ResNet, SEResNext and ResNext. [ABSTRACT FROM AUTHOR]

For over a decade, mobile applications are frequently used to make people's daily lives more accessible in all areas, such as commerce, tourism, education, medical care, and others. For implementing these applications, several frameworks exist that can accomplish this task, such as Google's Flutter, Microsoft's Xamarin, Facebook's React Native, Apple's Swift UI, Ionic, Django..etc. However, choosing the best framework to develop an application is not a simple enough decision for developers. This paper presents a detailed analysis of the four most widely used frameworks for implementing mobile applications: React Native, Swift UI, Flutter, and Xamarin. Furthermore, this study also aims to assist developers in choosing the most suitable framework by proposing an application that can recommend the best framework according to the needs entered by answering the questions, which are generally related to the relevant criteria resulting from the analysis of these frameworks. [ABSTRACT FROM AUTHOR]

Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [ABSTRACT FROM AUTHOR]

This paper presents a global approach for implementing Digital Predictive Maintenance (DPM), based on the principles of the digital industry. The proposed approach is applicable at both academic and professional scales, across all sectors and all sizes of organisations. This approach consists of four main phases: the identification of the system or work environment, the identification of the specific targets that justify the adoption of digital maintenance, the choice of appropriate digital technologies for predictive maintenance and the implementation of maintenance X.0 at all levels (i.e., strategic, tactical and/or operational). To validate this approach, the healthcare sector was chosen as a field of application. The uninterrupted operation of facilities and equipment in this sector is paramount. The adoption of maintenance X.0, which has already demonstrated significant benefits in other sectors, would be of great value in the healthcare sector. By applying our approach to the Pneumatic Tube System (PTS) at the Ibn Sina University Hospital Center (actually) in Rabat, Morocco, we have succeeded in introducing a structured and streamlined implementation of DPM in the healthcare sector. [ABSTRACT FROM AUTHOR]

The paper presents issues related to methods used in breast histopathological images tumor changes detection. The problem is connected with sustainable health issues which focus on the improvement of health and better delivery of healthcare, rather than late intervention in disease, with resulting benefits to patients and to the environment on which human health depends, thus serving to provide high quality healthcare. The main purpose of the paper is to develop a model based on deep artificial neural networks for the cancer detection in histopathological breast images. The implementation of the proposed model fits in with the concept of sustainable health through the support of the work of doctors in their decisions, diagnosis and in the reduction of the human workload and time, which can be referred to improve the health services. Data set contains 277524 samples from 163 breast histopathological images taken with the WSI scanner. The model is based on a convolutional neural network in the ResNet-18 architecture, which consists of residual blocks. During the final validation on the test set, the network achieved an accuracy of 93.6% and a 87.3% sensitivity in the detection of cancer tissues. The overall performance of the model is characterized by an F1-score of 0.887. The obtained results indicate the possibility of using the system in clinical conditions. [ABSTRACT FROM AUTHOR]

In the complex landscape of healthcare supply chains, challenges extend well beyond internal operational concerns, necessitating innovative solutions that align with the evolving technological landscape. The metaverse has emerged as a compelling solution to address these challenges by offering the potential for significant enhancements in efficiency and traceability. This paper focuses on both the theoretical underpinnings and practical applications of integrating the metaverse into healthcare supply chain management. Employing the Cyber-Physical-Social System framework, the study comprehensively explores the pivotal roles played by emerging information technologies in constructing the metaverse and overseeing various stages of the healthcare supply chain. The practical application is exemplified through the introduction of the TraceLink example, illustrating the successful healthcare supply chain digitalization. Furthermore, the paper introduces a barrier framework based on the Technology-Organization-Environment framework, addressing obstacles such as obsolete management technologies, data security, organizational structure, and societal acceptance. The examination and comparison of individual barriers using the Bayesian best-worst method further provide a nuanced understanding of the challenges. This comprehensive exploration not only equips research scholars with profound insights for addressing these challenges but also lays the foundation for future endeavors. The envisioned improvements in healthcare service delivery hold substantial benefits for patients and the industry, making this research a pioneering exploration of the metaverse's potential applications within the realm of the healthcare supply chain. [ABSTRACT FROM AUTHOR]

Powered by the rapid progress of analytics techniques and the increasing availability of healthcare data, artificial intelligence (AI) is bringing a paradigm shift to healthcare applications. AI techniques offer considerable advantages for the evaluation and assimilation of large amounts of complex healthcare data. However, to effectively use AI tools in healthcare, key issues need to be considered and several limitations must be addressed, such as privacy-preserving and authentication of the healthcare data for analysis in training and inference procedures. Although various techniques ranging from cryptographic tools to obfuscation mechanisms have been proposed to provide privacy guarantees for data in AI-based services, none of them is applicable to online AI-driven healthcare applications. For they require a heavy computational cost on protecting privacy without offering authentication services for third parties. In this paper, we present RASS, an efficient privacy-preserving and authentication scheme for securing analyzed data in an AI-driven healthcare system. The security proofs of our construction indicate that its unforgeability and multi-show unlinkability can defend against the tempering and collusion attacks respectively. Finally, we conduct sufficient efficiency analysis, and the results show that RASS achieves the above security demands without introducing complex computation and communication costs. • Privacy-preserving and authentication are crucial for AI based healthcare systems. • RASS supports multiple redactions without invaliding the authenticity. • The security analysis show RASS defends arbitrary-replace and linking attacks. • RASS performs well in securing privacy and authenticity of healthcare data. [ABSTRACT FROM AUTHOR]

In Canada, public insurance for physician and hospital services, without cost-sharing, is provided to all residents. Outpatient prescription drug coverage, however, is provided through a patchwork system of public and private plans, often with substantial cost-sharing, which leaves many underinsured or uninsured. We conducted a systematic review to examine the association of drug insurance and cost-sharing with drug use, health services use, and health in Canada. We searched 4 electronic databases, 2 grey literature databases, 5 specialty journals, and 2 working paper repositories. At least 2 reviewers independently screened articles for inclusion, extracted characteristics, and assessed risk of bias. The expansion of drug insurance was associated with increases in drug use, individuals who reported drug insurance generally reported higher drug use, and increases in and higher levels of drug cost-sharing were associated with lower drug use. Although a number of studies found statistically significant associations between drug insurance or cost-sharing and health services use, the magnitudes of these associations were generally fairly small. Among 5 studies that examined the association of drug insurance and cost-sharing with health outcomes, 1 found a statistically significant and clinically meaningful association. We did not find that socioeconomic status or sex were effect modifiers; there was some evidence that health modified the association between drug insurance and cost-sharing and drug use. Increased cost-sharing is likely to reduce drug use. Universal pharmacare without cost-sharing may reduce inequities because it would likely increase drug use among lower-income populations relative to higher-income populations. • There is a gap in the literature pertaining to the effects of drug insurance and cost-sharing in a Canadian context. • We conducted a systematic review of Canadian studies and found that there was consistent evidence that the expansion of drug insurance was associated with meaningful increases in drug use, that individuals who reported drug insurance coverage generally reported higher drug use relative to those who reported no coverage, and that increases in and higher levels of drug cost-sharing were associated with lower use. • Universal pharmacare without cost-sharing may reduce inequities in access to essential drugs. [ABSTRACT FROM AUTHOR]

Healthcare offers a rich palette of potential applications of RFID technology. Healthcare provides a rich palette of possible applications of RFID technology. Besides traditional uses such as tracking medical equipment and devices or access control, healthcare can benefit even more significantly from RFID technology. However, using the RFID technology in healthcare raises various problems of scalability, timely identification of tags, security, privacy, and efficient implementation in practice. That is because such systems contain many tags, operate with private personal data, and must respond promptly in concrete, practical situations to avoid malfunctions (errors in the decision process, traffic congestion, and so on). This paper discusses the fundamental requirements of RFID systems raised by healthcare and the limitations of existing schemes. Then, we propose a new RFID scheme that achieves mutual authentication, strong privacy, and constant-time identification in the HPVP model. The scheme employs a secure symmetric-key encryption scheme, making it very efficient in implementation and physically unclonable functions (PUFs) to protect the secret key against adversaries with corruption capabilities. [ABSTRACT FROM AUTHOR]

Remote patient monitoring is a healthcare delivery model that uses technology to collect and transmit patient data from a remote location to healthcare providers for analysis and treatment. Remote patient monitoring systems rely on a network infrastructure to gather and transmit data from patients to healthcare providers through a network. While these systems become more prevalent, they may also become targets for cyberattacks. This paper deals with the development of a domain ontology to facilitate partial automation to improve the security posture of IoT networks used in remote patient monitoring. For this purpose, it captures the semantics of the concepts and properties of the main security aspects of IoT medical devices. This is complemented by a comprehensive ruleset, evaluated by using SPARQL queries, and automated reasoning over the aggregated knowledge. [ABSTRACT FROM AUTHOR]

• Comprehensive optimization model is proposed for healthcare network design incorporates a robust goal programming approach. • Uncertainties of distribution of healthcare demand and facility capacity are captured. • Healthcare products have a limited shelf-life. • An option contract is applied for supply coordination with pre-determined supports before and after disruption occurrence. • Risk mitigation strategies are regarded to enhance the efficiency and effectiveness of design decisions under disruption. In this paper, a robust goal programming model for a healthcare network design problem under disruption is developed. The objective of the proposed multi-period, multi-echelon model is to simultaneously optimize the total deviation from the considered three goals, including minimizing network cost, maximizing network coverage, and maximizing network reliability. Distribution uncertainty of healthcare demand and facility capacity is captured. The perishability of healthcare products is regarded with limited shelf-life. Using an option contract ensures appropriate coordination with suppliers during pre and post-disaster periods. Via handling the tractable robust budget-based counterpart model, suitable strategies are proposed to present the designed network's efficiency and effectiveness. Risk mitigation strategies deal with disrupted demand and supply in the healthcare network with specific impacts on coverage, cost, and reliability of the services. The proposed cooperate coverage mechanism provides appropriate service coverage under disruption propagation. Finally, the option contract has achieved cost-efficient coordination under demand uncertainty. Furthermore, practical managerial insights are demonstrated by investigating a real-life case study and conducting extensive sensitivity analyses. [ABSTRACT FROM AUTHOR]

Mobile companions are a subset of telemedicine and they have gained increasing attention to enhance healthcare services and communication between patients and care providers. These digital tools allow for the delivery of care services through a variety of channels, increasing access to care and convenience for patients. By gathering and analyzing data from patients, mobile companions also provide care providers with valuable insights into patient health, helping to improve care. Additionally, they can be integrated with electronic medical records, making it easier for healthcare providers to access patient information and track progress over time. The use of mobile companions in healthcare services has many advantages and it brings new opportunities to care providers. However, it also presents many challenges to care providers that aim to adopt it. This paper presents a study where is addresses the opportunities and challenges of multichannel mobile companions in healthcare services provided to patients. As a result of this study, a SWOT analysis is presented and discussed regarding the mobile companion in healthcare services. [ABSTRACT FROM AUTHOR]

The ever-growing usage and popularity of Internet of Things devices, coupled with Big Data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, there is a need to reduce the gap on time from when information is created to when actions and insights can be offered. However, a challenge in implementing a large-scale data processing architecture is deciding which tools are appropriate, and how to apply them in the best way possible. For example, streaming systems are now mature enough that hospitals worldwide can use their extremely large datasets, along with data producers, to predict and influence future events. Thus, the main objective of this systematic review is to identify the state-of-the-art in data platforms on healthcare that allow the creation of metrics and actions in real-time. The PRISMA guideline for reporting systematic reviews was implemented to deliver a transparent and consistent report, validating the technological advances in a critical sector. Multiple pertinent articles and papers were retrieved from the SCOPUS abstract and citation database on May 13, 2022, using several relevant keywords to identify potentially relevant documents published from January 2020 onward. These documents must have already been published in English and been already published, and accessible through the B-ON consortium that allows Portuguese students to legally download from most publishers. Over seven studies have been selected for deeper discussion based on their relevance and impact for this review, showcasing their main objectives, data sources, and tools used, as well as their approaches for interoperability and support of machine learning algorithms for decision support. In closing, the collected articles have shown that while Big Data is currently in use at health institutions of all sizes, the ability of processing large amounts of data from sensors and events, and notifying stakeholders as quickly as possible is still in its infancy. [ABSTRACT FROM AUTHOR]

Coronary heart disease patients must reach a hospital within 90 minutes from the start of myocardial infarction to avoid the risk of death. Current hospital locations are optimized for the overall health care system and may not be the best possible for these patients. For this reason, we have constructed a web tool for optimizing the facility locations to study how well they serve for the coronary heart disease patients. In this paper, we explain how the system has been constructed, how it can be used, and show the results using patient data in Finland. Results show that the number of patients at risk could theoretically be reduced to 1% with optimized locations but at the cost of increasing the average travel time. [ABSTRACT FROM AUTHOR]

With the rapid development of edge computing technologies, smart healthcare significantly improves people's lives by collecting and analyzing health data in real time. However, security and privacy issues impede the wide deployment of smart healthcare systems (SHS). Most of existing solutions still have drawbacks with respect to computation efficiency and users' privacy. In this paper, a blockchain-enabled attribute-based access control scheme with hidden policies is proposed for SHS. The scheme introduces multiple authorities to avoid single point failure. Especially, the mode of online-offline encryption relieves users' online computation burden by transferring computation tasks to the idle time of users, and policy hiding protects users' sensitive information. Furthermore, fair payments are realized based on blockchain and smart contracts to support the outsourcing of decryption between users and mobile edge computing servers. Finally, the proposed scheme is proven secure in the random oracle model, and experimental results show that it is computationally efficient and hence can be used in the edge computing environment. [ABSTRACT FROM AUTHOR]

The human vestibular system is adversely affected by the aging process. Recent evidence indicates that vestibular information and cognitive functions are related, suggesting that age-related vestibular loss may contribute to cognitive impairment. In this study, we aimed to investigate the effects of repetitive, home-based galvanic vestibular stimulation (GVS) on cognitive functions in healthy older adults. Twenty-one participants (age = 64.66 ± 2.97 years, 12 females) were randomly allocated to either a home-based GVS or an active control group. The GVS intervention lasted 20 min per session, five times a week, for two weeks (10 sessions). Cognitive functions were assessed before and after the intervention using the Stroop Test, Trail Making Test A&B, and Dual-Task (digit recall and paper-pencil tracking test). Our findings revealed a significant group-by-time interaction effect for the tracking accuracy (F (1,18) = 7.713, p = 0.012, η p 2 = 0.30), with only the home-based GVS group showing significant improvement (t = −2.544, p = 0.029). The proposed home-based GVS protocol offers a promising non-pharmacological avenue for enhancing visuospatial ability in healthy older adults. Further research is needed to investigate the effects of different GVS protocols on various cognitive functions, particularly in older individuals with different health conditions. • Home-based GVS is feasible for older adults. • Repetitive GVS improves visuospatial ability in healthy older adults. • GVS is a promising non-pharmacological tool for improving cognition. [ABSTRACT FROM AUTHOR]

If Europe's health systems make a conscious decision to increase their utilization of technology and techniques that can enhance prevention and expedite early-stage diagnosis, they can effectively address the growing challenges of disease. By embracing these advancements, these health systems can significantly improve their response to emerging health issues.However, at present the effective integration and exploitation of these opportunities remains hesitant and suboptimal, and health and health services underperform accordingly, with patients suffering from the continuing variations in diagnosis and access to innovation. This paper presents a comprehensive study that examines the current state of various influential disciplines and factors in European countries. It specifically focuses on the adoption of Next Generation Screening technologies and the development stage of Public Health Genomics. The assessment of these areas is presented in the context of a rapidly changing policy environment, which provides an opportunity for a fundamental reconsideration of how and where new tools can be integrated into healthcare systems and routine practices. Top of Form [Display omitted] • NGS Adoption: European nations differ in NGS tech adoption, with disparities between leaders and laggards. • Policy Impact: Strong policies enhance NGS tech integration in healthcare. • Collaboration: Unified data standards, legal frameworks, and training are essential for Europe-wide NGS integration. • Benefits: NGS integration improves healthcare services, research, diagnostics, and treatments. • Conditions: Progress requires better workforce capacity, data sharing, collaboration, and policy leadership. [ABSTRACT FROM AUTHOR]

The widespread adoption of cloud technology in the healthcare industry has achieved effective outcomes in sharing health records and sensitive data. In recent years, many organizations have prioritized E-Health as their primary goal for advancement in health services. As a result, Attribute Based Encryption (ABE) has emerged as a reliable model for health information exchange across cloud settings. Eventually, it leads to provide acceptable solutions for challenging scenarios like fine-grained access control. Despite ABE significance and its breadth of applications, no systematic and comprehensive survey exists in the literature that covers every variation of ABE in healthcare, highlighting its past and present status. This paper presents a systematic and comprehensive study of ABE works concerning E-Health as the authors rigorously investigate healthcare-focused ABE frameworks and examine them based on various descriptive criteria, along with categorizing them systematically in 10 distinct domains and sub-domains, ultimately offering observations and potential recommendations. The descriptive research design, significant findings along with the suggested future works will help future research in ABE to secure the existing E-Health data sharing more effectively. The present study will also facilitate researchers and practitioners to comprehend the past trends and current state of ABE architectures in secure health data-sharing scenarios, as well as the prospect of ABE deployment in most recent technological evolutions. [ABSTRACT FROM AUTHOR]

Secure and efficient analytic over sensitive data becomes nowadays a big challenge for some enterprises (medical health care, insurance, etc). While these companies are collecting a large amount of sensitive data, cloud system is offering important opportunities for outsourcing of storage and computation. Thus, preserving the data privacy while maintaining the efficiency in computation at the cloud is considered an attractive research topic for computer scientists in academia as well as in industry. Homomorphic Encryption (HE) came as a new cryptographic topic that allows computation over encrypted data at the cloud side. Paillier is a famous HE scheme, that allows secure addition and average calculation over encrypted data. It presents the efficiency in implementation while preserving the security. In this paper, a new HE scheme competent for Paillier crypto-system is proposed. The presented scheme is called Secure Analytic using Vector based Homomorphic Operation (SAVHO). A deep security analysis has shown that the new scheme is resistant against several types of attacks. In addition, performance analysis has shown that SAVHO is a practical scheme by comparison with Pailler crypto-system. Different implementations are achieved under Python using SageMath Library. [ABSTRACT FROM AUTHOR]

The healthcare sector is constantly facing challenges in ensuring security due to the sophisticated attacks by the threat actor for acquiring sensitive patient data. An attack on the system can pose any potential risk to the business continuity. The increased use of information technology in the modern healthcare system makes medical devices and systems more vulnerable to exploitation and possible cyber-security attacks. This paper proposes a flexible and decentralized cyber-security model based on the integration of multi-agent systems and swarm intelligence for tackling the propagation of attacks inside interconnected healthcare organizations and ensuring the whole healthcare ecosystem's security and resilience. The proposed model is based on the collaboration between the agents with different functions and cognitive capabilities, named primary and supervisor agents. Primary agents are lightweight BDI (Belief-Desire-Intention) agents implementing a minimum set of capabilities for monitoring a specific area of the healthcare system; supervisor agents incorporate an extended version of the BDI reasoning to provide advanced capabilities for securing the overall healthcare system by enabling collective intelligence and overall cyber-security awareness. The preliminary experimental results show that the model is robust and responsive for securing the ecosystem. [ABSTRACT FROM AUTHOR]

• Organization effect on safety and risk management. • Safety and Risk management in process industries and healthcare. • Human decision-making in safety critical operations. • Design of decision-support tools for effective human decision-making. • Process systems engineering applications in healthcare. Cyber-physical-human systems are ubiquitous entities with varied scales of function. Apart from the systems-level complexity, it also poses a variety of decision-making problems to designers and practitioners. This decision-making is done by humans-in-the-loop, at every level of cyber-physical-human systems, with the help of decision-support tools. For any safety and risk critical cyber-physical-human systems organization, certain key features are useful to assess and compare the organizational functioning. Two such examples are process and healthcare industries, with safety and risk as major objectives. The paper uses the lens of humans-in-the-loop decision-making and decision-support tools to survey how safety and risk are handled in process and healthcare industries. Further, a discussion of current practices in the two domains and how future applications will involve more interdisciplinary aspects is provided. The paper concludes by discussing the ethical and data related concerns of a humans-in-the-loop and algorithm based shared decision-making future. [ABSTRACT FROM AUTHOR]

As more and more important smart healthcare becomes in people's daily life, its security issues draw increasing attention. It is stringent to design trusty communication protocol guaranteeing the data security and users privacy. Recently, Wang et al. proposed a certificateless authenticated key agreement (CL-AKA) protocol such that providing a resolution to transmit unforged data over open channel in smart healthcare. Claiming that their protocol could resist attacks from two types of adversaries, Wang et al. also compared their protocol with several related works. However, in this paper, we analyze their protocol and point out that the protocol lacks forward security. Moreover, aiming at remedying such defects, this paper proposes an improved CL-AKA protocol. Not only does the improved CL-AKA protocol satisfy the security requirements but also behaves efficient compared with other related works. • Potential attacks on Wang et al.'s CL-AKA scheme, WHX protocol, are presented. The session key may be recovered easily with the compromised static private keys. The defect is also analyzed by Scyther tool. • An improved CL-AKA protocol is proposed and analyzed by theorems, BAN logic and Scyther tool, showing the protocol's better security. • Performance evaluation and comparison are presented showing that the protocol can achieve a relatively good balance between security and efficiency. [ABSTRACT FROM AUTHOR]

The cognitive load experienced by humans is an important factor affecting their performance. Cognitive overload or underload may result in suboptimal human performance and may compromise safety in emerging human-in-the-loop systems. In driving, cognitive overload, due to various secondary tasks, such as texting, results in driver distraction. On the other hand, cognitive underload may result in fatigue. In automated manufacturing systems, a distracted operator may be prone to muscle injuries. Similar outcomes are possible in many other fields of human performance such as aviation, healthcare, and learning environments. The challenge with such human-centred applications is that the cognitive load is not directly measurable. Only the change in cognitive load is measured indirectly through various physiological, behavioural, performance-based and subjective means. A method to objectively assess the performance of such diverse measures of cognitive load is lacking in the literature. In this paper, a performance metric for the comparison of different measures to determine the cognitive workload is proposed in terms of the signal-to-noise ratio. Using this performance metric, several measures of cognitive load, that fall under the four broad groups were compared on the same scale for their ability to measure changes in cognitive load. Using the proposed metrics, the cognitive load measures were compared based on data collected from 28 participants while they underwent n -back tasks of varying difficulty. The results show that the proposed performance evaluation method can be useful to individually assess different measures of cognitive load. [ABSTRACT FROM AUTHOR]

The evolution of knowledge and information has changed the practices of health services over the years. The use of Information systems (IS), with the underlying Information Technology (IT), plays a vital role in managing medical information and associated resources, always to provide a better quality of health care. IS/IT is increasingly developing globally, with new features and tasks greatly expanded, leaving paper records and voluminous files behind. The ubiquitous information and computing resources increasingly allow the sharing of all types of data between various entities. New IS/IT will have to be thought to correspond to the new types of monitoring and analysis of biomedical data. This work intends to explore the IS/IT in health and its evolution over the last few years. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused huge and disruptive technological changes in the healthcare sector, transforming the way businesses and societies function. To respond to the global health crisis, there have been numerous innovation projects in the healthcare sector, including the fast design and manufacturing of personal protective equipment (PPE) and medical devices, and testing, treatment, and vaccine technologies. Many of these innovative activities happen beyond organizational boundaries with collaboration and open innovation. In this paper, we review the current literature on open innovation strategy during the pandemic and adopt the co-evolution view of business ecosystems to address the context of change. Based on a detailed exploration of the COVID-19-related technologies in the UK and global healthcare sectors, we identify the key emerging themes of open innovation in crisis. Further discussions are conducted in relation to each theme. Our results and analysis can help provide policy recommendations for the healthcare sector, businesses, and society to recover from the crisis. [ABSTRACT FROM AUTHOR]

The primary purpose of the paper is to identify the level of organisation's readiness for change in order to improve change in health organizations. The current research is focused to establish an evaluation model for organizational readiness. Fuzzy methods offer a streamlined and analytical environment, which enables to design, analyse or test models in a reasonable shorter time compared to other approaches. Development of conceptual framework through defining enablers, criteria and attributes based on literature review and expert opinion. Four enablers are defined for the development of a conceptual model along with 12 criteria and 38 attributes. Fuzzy logic approach is performed by two levels of analysis. In order to determine the organizational readiness level of the hospital, the Fuzzy Organizational Readiness Index (FORI) and Fuzzy Performance Importance Index (FPII) were calculated. The FORI can be estimated as the "Averaged Ready" (3.48, 5.21, 6.95) and FPII is calculated to identify eighteen weaker attributes that necessities management attention to prepare a hospital for the process of change. The suggested structure strengthens the ability of administrators to effectively incorporate improvements in their organizations. [ABSTRACT FROM AUTHOR]

Although Healthcare Analytics (HA) plays a critical role to uncover the link between data and discover novel and actionable information, the level that each analytics influences clinical decision-making is different. This paper aims to highlight the role of each HA in respect to Precision Medicine (PM). Where, PM is defined as a new approach in medical decision-making that takes into account individual differences in genetic, environmental, and lifestyle indicators. Besides, Simon's model in decision-making assesses the performance of clinical decision-making followed by HA. In addition, SOAP framework is discussed as an example under the influence of HA for adopting PM. [ABSTRACT FROM AUTHOR]

Data visualization is key in the Big Data context, enabling different cognitive perspectives over large datasets. These visual perspectives can prompt relevant advantages with regard to healthcare records, because they may contribute to a faster, more understandable, and adequate way to capture patients' health history and overall condition, thus improving healthcare quality. Timelines are well-known visual artifacts that help healthcare professionals to visualize patients' electronic health records (EHR) over a time period. As data stored in EHR tend to quickly grow with each interaction between patient and healthcare system both number and size, traditional linear timelines are can increasingly become more difficult to manage visually, as they often span over different screens. Considering that a holistic analysis is desirable to provide proper and quality health services, data visualization should enable a seamless understanding of patients' health history and overall condition. When dealing with critical episodes - such as an emergency - where time is an important factor, this is even more decisive. Furthermore, traditional timelines do not support multidimensional data representation. This paper presents a new visual model of time-dependent EHR based on radial models. It is capable of simultaneously displaying several data categories that characterize patients' medical history, enabling medical professionals to be aware of different data categories over time in a single display, without the need to scroll between screens. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) in medicine is beginning to alter current procedures in prevention, diagnosis, treatment, amelioration, cure of disease and other physical and mental impairments. In addition to raising concerns about public trust and ethics, advancements in this new emerging technology have also led to a lot of debate around its integration into healthcare. The objective of this work is to introduce researchers to AI and its medical applications, along with their potential pitfalls, in a comprehensive manner. This paper provides a review of current studies that have investigated how to apply AI methodologies to create a smart predictive maintenance model for the industries of the future. We begin with a brief introduction to AI and a decade's worth of its advancements across a variety of industries, including smart grids, train transportation, etc., and most recently, healthcare. In this paper, we explore the various applications of AI across various medical specialties, including radiology, dermatology, haematology, ophthalmology, etc. along with the comparative study by employing several key criteria. Finally, it highlights the challenges for large-scale integration of AI in medical systems along with a summary of the ethical, legal, trust, and future implications of AI in healthcare. [ABSTRACT FROM AUTHOR]

• A comprehensive systematic review on healthcare recommender systems is provided. • The main ideas, methods, tools, advantages, disadvantages, evaluation parameters, and evaluation methods are discussed and summarized. • A scientific taxonomy of healthcare recommender systems is presented. • A detailed list of challenges, open issues, and future research directions is outlined. The maintenance of health through prevention, diagnosis, and treatment of illnesses, injuries, and other mental and physical impairments in people is called healthcare that is performed by professionals in health fields. All activities performed in providing primary, secondary, and tertiary care, as well as in public health, are components of healthcare systems that are required to analyze massive amounts of patients' data to gain insights and aid in disease prediction. The healthcare recommender system (HRS) is a growing and robust platform for healthcare services. The purpose of this paper is to identify , taxonomically classify , and compare current HRS researches in a systematic way. This study presents a systematic literature review (SLR) method for HRSs regarding 41 papers published between 2010 and 2021; the selected articles fall into five classes: collaborative-based, content-based, knowledge-based, context-based, and hybrid. Seventeen essential factors are identified to examine the selected HRSs, and each of the papers were studied to find advantages, disadvantages, evaluation types, and tools. In addition, we address a discussion of challenges, future directions, and open issues. This paper demonstrates that HRSs are still in their early stages of maturity, but they are developing. It identifies the need for a scalable, confidential, and reliable framework to aid in the improvement of both patients' and health professionals' trust in HRSs. This research reveals a lack of standardization of medical codes across various healthcare systems. In addition, this review identifies the need for novel patterns to address cold start problems. [ABSTRACT FROM AUTHOR]

• This paper proposes the application of deep learning algorithms for diagnosing 14 major ophthalmological defects such as Hollenhorst Emboli, Arteriosclerotic Retinopathy etc. • In this study, multiple performance evaluation techniques such as Precision, Recall, F-1 Score, etc. are used to compare deep learning algorithms. • In this study, the performance compared with the existing literature achieved higher accuracy due to the unique model and its configuration, hyperparameter tuning and pre-processing techniques for the 14 classes of retinal defects. Retina is the heart of an eye which generates electrical impulses due to light sensitivity. The vessel formation in human eye is an essential key for diagnosing ophthalmological conditions. This paper aims to diagnose ophthalmological conditions through deep learning models and provide advancements in early detection of ophthalmological conditions for proper treatment to protect patient's vision, and for health care giver worldwide. STARE dataset is used for this study which consists over 385 retinal images of 14 ophthalmological defects such as BRAO, CRAO, etc. This dataset is further pre-processed over the techniques such as augmentation, normalization, etc for obtaining the best refined features for training deep learning algorithms. This paper broadly implements 5 deep learning algorithms i.e., EfficientNet, 3-Layers CNN, InceptionV2, ResNet-50, VGG-16. These models are trained number of times over tuned hyperparameters such as batch size etc and evaluated over 4 performance metrics over weighted averaged and macro averaged of precision, recall, F1-score, and accuracy to acquire the best performing model. EfficientNet performed the best with 98.43% accuracy, macro averaged f-1 score, recall, precision as 98.37%, 99.16%, 97.91% and weighted averaged f-1 score, recall, precision, as 98.50%, 98.43%, 98.82% over batch size 64. In this study, the performance compared with the existing literature achieved higher accuracy due to the unique model and its configuration, hyperparameter tuning and pre-processing techniques for the 14 classes of retinal defects. The future work includes classifying more ophthalmological conditions, adding more parameters from blood, etc. [ABSTRACT FROM AUTHOR]

Personal health records (PHR) offer significant benefit for patients, such as reducing medical cost and improving the quality of medical care. Majority of the current schemes lack provisions for tracking and revoking malicious doctors. The explicit access policies are prone to leaking patient private information. What is more, owning to the uneven distribution of medical supplies, shocking computational overhead during decryption is a burden that cannot be ignored for busy medical workers. This paper proposed a patient-centric medical service matching scheme that supports policy hiding, attribute matching, fine-grained access control, and user dynamic management. The scheme uses ciphertext policy-based attribute encryption (CP-ABE) to achieve fine-grained access control and supports policy hiding. It utilizes white-box tracking technology and binary tree structure to achieve malicious doctor tracking. Revocation information is ciphertext to achieve dynamic management of doctors. From the experimental results, it can be concluded that our protocol achieves both patient-centric security and performance advantages. • A secure patient-centric medical service matching scheme with policy hiding. • Achieve fine-grained access control and withdraw untrustworthy doctors. • Low computational and communication overhead. [ABSTRACT FROM AUTHOR]

Supplier selection is critical to the healthcare industry, especially in emergencies. Accurate and timely supply of healthcare resources can reduce economic cost and ensure the safety of people's lives. However, the healthcare supplier market is enormous and complex; making accurate evaluations of different suppliers and helping managers choose the most suitable one when considering many factors is becoming an important task. Many multicriteria decision-making (MCDM) methods have been applied to supplier selection in existing literature. However, the research on healthcare supplier selection still needs to be completed. In this paper, to deal with healthcare supplier selection problems when there is no sufficient time and information to decide with uncertainty, the stochastic multicriteria acceptability analysis (SMAA) method and multi-attributive border approximation area comparison (MABAC) method are first combined by taking both the advantages of SMAA and MABAC methods when the assessments are expressed by belief distributions (BDs). In the SMAA-MABAC method, the global ignorance in BDs and the uncertain criteria weights are considered by judging the feasible space to derive the final rank of healthcare suppliers with the most possible. The more straightforward calculation process of SMAA-MABAC makes it faster to make an accurate choice among various suppliers. Moreover, it is flexible to generate different recommended suggestions when diverse preference information is added in SMAA-MABAC. Finally, a case study of healthcare supplier selection is examined for managers based on distributed reviews, and targeted selection advice is given concerning different demands. Besides, the sensitive analysis is provided to better understand the influence of the criteria weights on the proposed model. The results of the comparative analysis also show the effectiveness and rationality of the proposed method. [ABSTRACT FROM AUTHOR]

• Peoples who're specially affected with heart sickness and it's far one of the man-kill illnesses in the international level. • Most of researchers to awareness at the prediction, clustering, rule generation, decision tree and machine learning algorithm for figuring out and predicting the danger of the sufferers primarily based on the medical information. • The researcher can pick out the time and reminiscence wanted for the execution. • There are many different types of machine learning algorithms are categorized into three important classifications namely unsupervised learning, supervised learning and reinforcement learning. • This research paper includes the six elements to evaluate the overall performance of K-Means, Navie-Bayes and enhanced PSNB-IMR Algorithm with various parameters. Peoples who're specially affected with heart sickness and it's far one of the man-kill illnesses in the international level. Most of researchers to awareness at the prediction, clustering, rule generation, decision tree and machine learning algorithm for figuring out and predicting the danger of the sufferers primarily based on the medical information. The overall performances of the crucial functions are based on the machine-learning concept. By studying the algorithm, the researcher can pick out the time and reminiscence wanted for the execution. As such, there are many different types of machine learning algorithms are categorized into three important classifications namely unsupervised learning, supervised learning and reinforcement learning. Unsupervised learning consists of all varieties of clustering algorithms at the same time as supervised learning algorithm consists of all of the category strategies. But the author is considered the two algorithms are Supervised and Unsupervised learning algorithm to examine the overall performance. This research paper includes the six elements to evaluate the overall performance of K-Means, Navie-Bayes and enhanced PSNB-IMR Algorithm with various parameters. [ABSTRACT FROM AUTHOR]

The use of smart wearables provides an opportunity to improve construction safety and productivity. Because the healthcare industry has been at the forefront of applying such technologies, this paper investigates available ubiquitous wearables, their metrics, and types of measurements; how existing healthcare systems detect hazards and unhealthy behaviors; and the potential for cross-fertilization between healthcare and construction domains. A comprehensive review of 173 papers is used to examine existing developments in the use of smart wearables in the construction and healthcare industries. The literature survey identified applicable healthcare metrics, measurements, and ranges that can be readily utilized in the construction industry. This information can facilitate further studies related to improving work ergonomics, health and safety, and worker stress analysis. Future research can also help develop efficient construction schedules that dynamically monitor workers' fatigue, and accordingly devise corrective actions that minimize the impact on workers' safety and work productivity. [Display omitted] • This paper reviews existing work in construction and health care fields towards using wearables to enhance health and safety. • Over 170 papers from construction and healthcare related journals were reviewed. • Existing applications for wearables towards health and safety are discussed. • Healthcare metrics related to fatigue and work intensity that could be translated to the construction industry are featured. • Technical and social challenges towards the adoption of wearables and IoT systems are identified. [ABSTRACT FROM AUTHOR]

There is an increasing interest in the use of stated preference methods to understand individuals' preferences for health and healthcare. There is also a growing interest in understanding heterogeneity in individuals' preferences. Consequently, stated preference studies frequently consider models that capture either or both observed and unobserved preference heterogeneity. A popular preliminary investigation into heterogeneity involves split-sample analysis to compare subgroups' preferences e.g., comparing patients with clinicians, or older patients with younger. In fixed-effects models, the constant variables (the individuals' characteristics) remain stable across choice sets and therefore only enter the choice model when interacted with various attributes and/or levels. However, subgroups of respondents may differ on multiple variables that may not easily be implemented with interaction terms because of complexity and a lack of power thus only one, or a few, variables are typically taken into account in each subgroup model. This paper presents an overview of methods for matching and balancing samples to weight individuals with different characteristics in subgroup analysis and an example of how unweighted comparisons may produce erroneous conclusions regarding the degree of heterogeneity in preferences. We illustrate the issue with synthetic and empirical datasets to explore methods for matching subgroups before and within simple choice models. Our results show that entropy balancing and propensity score matching could be more appropriate than analyses using unmatched preference data when heterogeneity is driven by multiple factors. The paper concludes with a discussion of when matching and weighting may and may not be useful in healthcare decision-making. • Subgroup analysis to investigate preference heterogeneity is commonplace. • Direct subgroup comparisons can be biased if other characteristics also influence preferences. • Matching or weighting techniques can be used to ensure balance of multiple characteristics across subgroups. • Whether these techniques are required depends on the research question of interest. [ABSTRACT FROM AUTHOR]

Medical waste management (MWM) is a challenging issue for medical facility managers owing to its potential to prevent environmental and health risks. Many treatment technologies (TTs) have been used for MWM, and selecting appropriate TTs is a complex multi-criteria decision-making (MCDM) problem. In this paper, we propose a novel integrated MCDM method based on the best-worst method (BWM) and the VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method under the Fermatean fuzzy environment to evaluate, rank, and select treatment technologies for medical waste management. First, novel distance measure and entropy measure for Fermatean fuzzy sets are developed, and their properties are examined. Second, different treatment technologies are evaluated by experts using Fermatean fuzzy sets. Third, the experts' weights are determined using a novel method based on the entropy measure, and the criteria weights are calculated by a novel hybrid criteria weight calculation method. Finally, the ranking of different treatment technologies is determined by the proposed method. To validate the proposed method, a real case study in Jinan, China is presented, where the proposed method is applied to determine the optimal health care waste (HCW) treatment technologies from five TTs with eight criteria. The results are compared to other MCDM methods, which shows the effectiveness and reliability of the proposed method. Moreover, sensitivity analysis is also carried out to show the robustness of the proposed method. From the results, it can be found that the proposed method could provide reliable and robust results for obtaining the optimal health care waste treatment technology. • Proposes a novel decision-making method under Fermatean fuzzy set called FF-BWM-VIKOR. • The FF-BWM-VIKOR method is proposed to select health care waste treatment technology. • Novel distance measure and entropy measure for Fermatean fuzzy sets are developed. • A hybrid criteria weight calculation method is proposed to determine weights of criteria. • A real case of health care waste treatment technology selection is presented. [ABSTRACT FROM AUTHOR]

The efficient distribution and timely vaccine delivery to the masses during any pandemic situation require reliable and efficient vaccine cold chain management (VCCM). Therefore, the present study is an effort to explore the factors of VCCM to develop a sustainable and resilient healthcare delivery system in any health outbreak. The factors of VCCM are scrutinized using Fuzzy-Delphi method and then prioritized adopting Fuzzy-Analytical Hierarchy Process (F-AHP) approach while considering the stakeholders' inputs. Finally, factors are categorized into clusters by implementing a machine learning tool ' k -means clustering' algorithm. The results of the study are further validated by conducting extended robustness tests. The findings of the study highlight the factors, namely, cold chain infrastructure, vaccine storage guidelines and availability of skilled professionals as the most important for VCCM. The ′ k -means clustering' algorithm analysis classifies all the 16 factors into three clusters: continuous process improvement (including eight factors), stakeholders' coordination (five factors) and maximum resource utility (three factors). The study can be significantly useful to VCCM decision-makers to implement the prioritized factors by ensuring the advanced cold chain infrastructure and issuing special storage and distribution guidelines to fight against health outbreaks like COVID-19. This is one of the few papers to discuss the factors of VCCM for developing a sustainable and resilient healthcare delivery system in the context of pandemics and stakeholders can address the factors in different clusters at a time. [Display omitted] • Vaccine cold chain management (VCCM) to develop a resilient healthcare delivery system during health outbreaks. • Prioritization of factors of VCCM using Fuzzy-Analytic Hierarchy Process approach. • Categorization of factors of VCCM by implementing a machine learning tool of K − m e a n s clustering algorithm. • Storage and distribution guidelines to fight against the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The smart healthcare system plays a vital role in modern healthcare, facilitating the exchange of Electronic Patient Records (EPR), and improving medical care. Nevertheless, safeguarding the inherent security and confidentiality of EPR data persists as a formidable challenge. This issue demanding rigorous attention and innovative solutions. Digital watermarking safeguards the genuineness and integrity of digital images, and is widely employed. In medical imaging, it is vital to guarantee the confidentiality and security of patient data. In this paper, we propose an Encryption-Then-Embedding-Based data hiding scheme for medical images that combines cryptography and watermarking techniques. The proposed technique first encrypts the patient information using Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) before embedding. The intensity-based image segmentation method is then used to select the Region of Non-interest (RONI) for embedding the encrypted patient information and the watermark using a Fused Transform-Based Method (FTBM). The experiments conducted in this study utilize a large dataset of medical data, The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD). The findings of this study demonstrate the efficacy of the proposed technique in safeguarding patient data while simultaneously preserving the quality of medical images. In comparison to existing techniques, the proposed approach demonstrates superior performance in terms of security, authenticity, and integrity. [ABSTRACT FROM AUTHOR]