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In people with bone and soft tissue injuries of the wrist, hand and/or fingers, do feedback-guided exercises performed on a tablet touchscreen hasten return to work, reduce healthcare usage and improve clinical recovery more than a home exercise program prescribed on paper? Randomised, parallel-group trial with concealed allocation, assessor blinding and intention-to-treat analysis. Seventy-four workers with limited functional ability due to bone and soft tissue injuries of the wrist, hand and/or fingers. Participants in the experimental and control groups received the same in-patient physiotherapy and occupational therapy. Participants in the experimental group received a home exercise program using the ReHand tablet application, which guides exercises performed on a tablet touchscreen with feedback, monitoring and progression. Participants in the control group were prescribed an evidence-based home exercise program on paper. The primary outcome was the time taken to return to work. Secondary outcomes included: healthcare usage (number of clinical appointments); and functional ability, pain intensity, and grip and pinch strength 2 and 4 weeks after randomisation. Compared with the control group, the experimental group: returned to work sooner (MD –18 days, 95% CI –33 to –3); required fewer physiotherapy sessions (MD –7.4, 95% CI –13.1 to –1.6), rehabilitation consultations (MD –1.9, 95% CI –3.6 to 0.3) and plastic surgery consultations (MD –3.6, 95% CI –6.3 to –0.9); and had better short-term recovery of functional ability and pinch strength. In people with bone and soft-tissue injuries of the wrist, hand and/or fingers, prescribing a feedback-guided home exercise program using a tablet-based application instead of a conventional program on paper hastened return to work and improved the short-term recovery of functional ability and pinch strength, while reducing the number of required healthcare appointments. ACTRN12619000344190 [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 article inform people with chronic oedema of the leg and cellulitis, is compression therapy is effective than conservative treatment at reducing recurrence of cellulitis, hospital admission and leg volume.

A quiz concerning the Chronic Obstructive Pulmonary Disease is presented.

The article reflects on standard management for children with symptomatic joint hypermobility for improving pain. Topics include The non-pharmacological trials often consist of multi-component interventions; active ingredients are sometimes difficult to isolate and the final intervention may substantially differ from the planned intervention; and recommend explicitly describing the components, including whether it was standardized and the adherence rates to the protocol and to the intervention.

The article presents a synopsis on breathing retraining programs that can improve health-related quality of life in people with asthma. Topics discussed include pharmacological treatment is effective in Asthma; produce such substantial improvement in asthma-related quality of life in people; and breathing retraining did not improve pathophysiological outcomes such as clinical control.

A lack of recent improvement in survival in teenagers and young adults (TYA) with bone tumours may be linked to a lack of trial recruitment and specialist care, poor concordance with therapy, and/or TYA-specific pharmacology. To overcome these challenges, working together to create collaborative care between adult and paediatric experts in bone sarcoma is neccessary, to meet TYA patients' needs.Teenagers and young adults (TYA) cancer contributes substantially to morbidity and mortality in a population with much to offer society. TYA place distinct challenges upon cancer care services, many reporting feeling marginalized and their needs not being met in adult or paediatric cancer services. Bone tumours such as osteosarcoma and Ewing sarcoma, because of their age at presentation and the complexity of their care, are where challenges in managing (TYA) with cancer have often been most readily apparent. Bone sarcomas may be managed by paediatric or medical oncologists, and require fastidious attention to protocol. A lack of recent improvement in survival in TYA with bone tumours may be linked to a lack of specialist care, poor concordance with therapy in some situations and TYA-specific pharmacology. Participation in clinical trials, particularly of young adults, is low, hindering progress. All these requirements may be best met by a concerted effort to create collaborative care between adult and paediatric experts in bone sarcoma, working together to meet TYA patients' needs. [ABSTRACT FROM AUTHOR]

In Egypt access of the entire population to health care services and medications is still a challenging step, with high out of pocket expenditure that reaches 60%. A lot of medications and diagnostic services are still not publicly covered like biological, targeted therapies and many biomarkers tests, and although the government initiated a co-payment program, there is a still a gap in providing eligible patients with full course of therapy. [Extracted from the article]

Background Prosthodontic Rehabilitation of Treated Maxillofacial Trauma Cases by Evaluating Occlusal Force Distribution Using Computerized Occlusal Analysis. Method 30 patients were selected for the study. 15 normal and 15 treated trauma patients were subjected to T Scan analysis and evaluated for the occlusal force distribution. Results The results take into consideration the two parameters. Firstly the largest articulating paper mark (photographed) and secondly the T scan of the same patient. Comparison was made between the largest articulating paper mark and highest force tooth in the quadrant using T Scan. The matches and no matches were then tabulated for statistical analysis assessing the frequency of the matches to the no matches. Conclusion The ultimate advantage of a T Scan III analysis is that it can detect the amount of force as well as location of the highest intensity contacts of a single tooth which is very specific. [ABSTRACT FROM AUTHOR]

Objective: To investigate whether the use of the think-aloud method with propositional analysis could be helpful in the design of a Clinical Decision Support System (CDSS) providing guideline recommendations about long-term follow-up of childhood cancer survivors.Materials and Methods: The think-aloud method was used to gain insight into healthcare professionals' information processing while reviewing a paper-based guideline. A total of 13 healthcare professionals (6 physicians and 7 physician assistants) prepared 2 fictitious patient consults using the paper-based guideline. Propositional analysis was used to analyze verbal protocols of the think-aloud sessions. A prototype CDSS was developed and a usability study was performed, again with the think-aloud method.Results: The analysis revealed that the paper-based guideline did not support healthcare practitioners in finding patient-specific recommendations. An information processing model for retrieving recommendations was developed and used as input for the design of a CDSS prototype user interface. Usability analysis of the prototype CDSS showed that the navigational structure of the system fitted well with healthcare practitioners' daily practices.Conclusions: The think-aloud method combined with propositional analysis of healthcare practitioners' verbal utterances while they processed a paper-based guideline was useful in the design of a usable CDSS providing patient-specific guideline recommendations. [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]

We trust it is clear that JBI addresses a broader range of methods than AI, whereas the other two journals are singularly focused on AI and data science. With the growth in Artificial Intelligence in Medicine (AIM) research and the plethora of informatics journals, there is some confusion where to direct an AIM-related manuscript for peer review and possible publication. [Extracted from the article]

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]

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]

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]

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]

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]

Health is one of the most important aspects of life. However, people still could not get proper health services. It is caused by limitation to the used technology in hospitals and limitations to get to the hospital. Internet-of-Things (IoT) as one of the most trending topics nowadays, already giving so many solutions in many ways, for example, in healthcare. IoT is implemented in many healthcare ways, including detecting disease as a precaution, treating a disease as a healing solution, and monitoring a disease as a healing process itself. As part of IoT, wearable devices have been developed to help people get the right treatment for themselves. In this paper, a systematic literature review of wearable IoT devices for healthcare is conducted. The review is started by searching the references by pre-defined keywords and filtering the references based on the relevance and the published year. In this review, we discuss the utilization of wearable devices in handling the problem in the healthcare aspects, including disease detection, monitoring, and curing. Other than that, this review paper also explained how the architecture of wearable devices applied. Finally, the future challenge of research in this field is defined. [ABSTRACT FROM AUTHOR]

There are various diseases arising in the world with new unexpected areas. Each disease has its own symptoms and cures. Some people are capable to fight against these diseases but most of them are a part of the attacks done by the diseases. This paper introduces the Parkinson's disease patient with the idea to work their body normally with the help of artificial life to their neurons by adding a chip known as Electroencephalography (EEG) into their brains that work in the form of wearable devices. This is required for those people who are suffering from this disease and could not able to communicate with another people just because they cannot able to send their signals properly to the brain because they do not get the signals on time because of dead neurons. This paper is going to introduce architecture to work on the brain by using several chips, which help to send signals and become a mediator/ transmitter who sends to do work. [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 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]

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]

Purpose: The purpose of this study is to identify the quality of health care services' differences between adopted Electronic Medical Record (EMR) and paper-based record hospitals. Moreover, to identify how the quality of electronic medical records affect the quality of health care services.Methods: A cross-sectional, descriptive, and comparative design was utilized between two groups in this study. The study was conducted in two public hospitals in Jordan. One hospital which had adopted the EMR system in their works and another hospital that had paper-based record. A convenience sampling technique was used to select 410 health professionals from the selected hospitals (205 participants from each hospital). SERVQUAL and E-S-QUAL questionnaires were adopted to collect the data about the quality of EMR and quality of health care service. Statistical Package for the Social Sciences (SPSS) Version 21 was deployed to analyze the collected data.Result: The results indicate that the quality of health care services (expectation and perception) in EMRs adopted hospital is higher than the quality of health care services in the hospital using paper-based record. Quality of EMRs and its domains (efficiency, availability, fulfilment, and privacy) are high in both perception and expectation of health care professionals in EMR-adopted hospitals. Moreover, there is a significant relationship between the whole perception of the quality of electronic medical records and the quality of services, and between each domain of quality of EMRs and total perception of quality of services.Conclusion: Adoption of a high quality of EMR has a significant impact on improving the quality of health care services. [ABSTRACT FROM AUTHOR]

Health Data Bank (HDB) as a type of health data service institution has become a promising third-party for mining the value behind the huge amounts of health data and providing valuable health data service to ordinary people. The customers' continuous contribution behavior is the key element of HDB's success and sustainable development. In this paper, we consider creditworthiness value as a measurement of a customer's contribution behavior. The customers' creditworthiness value is computed based on both the Authentic Behavior and Honest Behavior. Then, we propose a scheme Creditworthiness-based Service Differentiation (CBSD), in which the creditworthiness value is used as the guideline for differential service. The proposed scheme provides the right incentives for the customers to share their valid and authentic personal health data. And the simulation results confirm the ability of the proposed scheme to effectively reduce the health data service level provided to the customers with low creditworthiness value. On the other hand, good customers have a high probability of receiving better service. The shortcomings of the proposed scheme and the future work are concluded at the end of the paper. [ABSTRACT FROM AUTHOR]

Abstract Despite the heightened urgency of curbing carbon emissions around the world, the healthcare sector in general, and the pharmaceutical sector in particular have received very little attention from the sustainability community in terms of their contribution to the global carbon footprint. In this paper, we conduct an analysis of the overall contributions and the historical emissions trends of the pharmaceutical sector, as well as an industry-specific comparative analysis of the major pharmaceutical companies in the world. Surprisingly, our analysis reveals that the pharmaceutical industry is significantly more emission-intensive than the automotive industry. We also use a previously published mathematical framework linking national target emissions to the target emission intensity of the pharmaceutical sector to derive the emission intensity of the pharmaceutical sector required for the US to meet its reductions commitments per the now defunct Obama-administration commitments at the 2015 Paris Agreement. We identify the excess emitters among the top-15 Pharmaceutical companies, from those that are leading the pack with their emissions improvement efforts. The results are quite instructive as we find a far greater variability amongst the Top-15 pharmaceuticals than the Top-10 automotive companies, suggesting a very disparate set of environmental practices within the industry. The paper should elicit further in-depth studies of the environmental performance of the pharmaceutical sector and help inform policy makers, business leaders and academicians on how to help curb this unwarranted level of emissions in this important and growing industry sector. Graphical abstract We conduct an analysis of the overall contributions and the historical emissions trends of the pharmaceutical sector, as well as an industry-specific comparative analysis of the major pharmaceutical companies in the world. Surprisingly, our analysis reveals that the pharmaceutical industry is significantly more emission-intensive than the automotive industry. We also use a previously published mathematical framework linking national target emissions to the target emission intensity of the pharmaceutical sector to derive the emission intensity of the pharmaceutical sector required for the US to meet its reductions commitments per the now defunct Obama-administration commitments at the 2015 Paris Agreement. We then use the same framework to identify the excess emitters among the top 15 Pharmaceutical companies, from those that are leading the pack with their emissions improvement efforts. There again, the results are quite instructive as we find a far greater variability amongst the Top 15 pharmaceuticals than the Top 10 automotive companies. Image 1 Highlights • Assessment and comparative analysis of GHG emissions of Top-15 global pharmaceutical companies. • The pharmaceutical industry emission intensity is about 55% higher than the automotive's. • There is a great level of variability in emissions (up to 5x) between peers with comparable revenues. • We calculate the intensities and maximum emissions required to comply with the Paris Agreement. [ABSTRACT FROM AUTHOR]

Background: Constrained optimization methods are already widely used in health care to solve problems that represent traditional applications of operations research methods, such as choosing the optimal location for new facilities or making the most efficient use of operating room capacity.Objectives: In this paper we illustrate the potential utility of these methods for finding optimal solutions to problems in health care delivery and policy. To do so, we selected three award-winning papers in health care delivery or policy development, reflecting a range of optimization algorithms. Two of the three papers are reviewed using the ISPOR Constrained Optimization Good Practice Checklist, adapted from the framework presented in the initial Optimization Task Force Report. The first case study illustrates application of linear programming to determine the optimal mix of screening and vaccination strategies for the prevention of cervical cancer. The second case illustrates application of the Markov Decision Process to find the optimal strategy for treating type 2 diabetes patients for hypercholesterolemia using statins. The third paper (described in Appendix 1) is used as an educational tool. The goal is to describe the characteristics of a radiation therapy optimization problem and then invite the reader to formulate the mathematical model for solving it. This example is particularly interesting because it lends itself to a range of possible models, including linear, nonlinear, and mixed-integer programming formulations. From the case studies presented, we hope the reader will develop an appreciation for the wide range of problem types that can be addressed with constrained optimization methods, as well as the variety of methods available.Conclusions: Constrained optimization methods are informative in providing insights to decision makers about optimal target solutions and the magnitude of the loss of benefit or increased costs associated with the ultimate clinical decision or policy choice. Failing to identify a mathematically superior or optimal solution represents a missed opportunity to improve economic efficiency in the delivery of care and clinical outcomes for patients. The ISPOR Optimization Methods Emerging Good Practices Task Force's first report provided an introduction to constrained optimization methods to solve important clinical and health policy problems. This report also outlined the relationship of constrained optimization methods relative to traditional health economic modeling, graphically illustrated a simple formulation, and identified some of the major variants of constrained optimization models, such as linear programming, dynamic programming, integer programming, and stochastic programming. The second report illustrates the application of constrained optimization methods in health care decision making using three case studies. The studies focus on determining optimal screening and vaccination strategies for cervical cancer, optimal statin start times for diabetes, and an educational case to invite the reader to formulate radiation therapy optimization problems. These illustrate a wide range of problem types that can be addressed with constrained optimization methods. [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]

In many developing countries, populations rely on traditional medicine for primary health care, which have infiltrated commercial markets globally as natural remedies are generally regarded as safe. Traditional and natural remedies are adapted and expanded in commercial products and product ranges to provide alternatives for various diseases and illnesses. These products resemble very little of the traditional use and application and adverse effects are observed in several cases. Some of the herbs and botanical formulations therefore, are not as safe as are commonly contemplated. This paper discusses some plants that are used as food or medicine. These plants are known to contain chemical components that have been identified as genotoxic carcinogens. Often contradictory results are obtained with beneficial and adverse effects reported. The concentration, biotransformation and metabolism of these compounds, as well as the matrix effect, affect the outcome of these results, therefore not providing a clear picture of the risk associated with the use and consumption of these plants. This paper focuses on plants that are accepted as healthy, however contain compounds that are genotoxic and carcinogenic. We further highlight the risks in use of these plants where thorough studies have been conducted in various food and plant products. [ABSTRACT FROM AUTHOR]

Green Public Procurement (GPP) is an increasingly debated “demand side” environmental policy instrument. The aim of this paper is to take stock of the related literature, with the twofold aim of developing a conceptual model of the relevant phases of GPP and to identify the related research gaps in detail. The literature analyzed here comprises English language papers, which are focused on GPP during the time period from 2000 to 2016. By means of a systematic literature review and content analysis, we provide both a quantitative and qualitative viewpoint. As our analysis reveals, GPP discussion has mostly focused so far on the specific impacts of GPP implementation, while the discussion on GPP as compared to other environmental policy tools, in terms of efficiency and innovation, is still lagging behind; this is coupled with a limited geographical coverage. Finally, by disaggregating the 17 years period under scrutiny into four sub-periods allows us to outline the changes in research trends over time. [ABSTRACT FROM AUTHOR]

Background: Patient portals are considered valuable conduits for supporting patients' self-management. However, it is unknown why they often fail to impact on health care processes and outcomes. This may be due to a scarcity of robust studies focusing on the steps that are required to induce improvement: users need to effectively interact with the portal (step 1) in order to receive information (step 2), which might influence their decision-making (step 3). We aimed to explore this potential knowledge gap by investigating to what extent each step has been investigated for patient portals, and explore the methodological approaches used.Methods: We performed a systematic literature review using Coiera's information value chain as a guiding theoretical framework. We searched MEDLINE and Scopus by combining terms related to patient portals and evaluation methodologies. Two reviewers selected relevant papers through duplicate screening, and one extracted data from the included papers.Results: We included 115 articles. The large majority (n = 104) evaluated aspects related to interaction with patient portals (step 1). Usage was most often assessed (n = 61), mainly by analysing system interaction data (n = 50), with most authors considering participants as active users if they logged in at least once. Overall usability (n = 57) was commonly assessed through non-validated questionnaires (n = 44). Step 2 (information received) was investigated in 58 studies, primarily by analysing interaction data to evaluate usage of specific system functionalities (n = 34). Eleven studies explicitly assessed the influence of patient portals on patients' and clinicians' decisions (step 3).Conclusions: Whereas interaction with patient portals has been extensively studied, their influence on users' decision-making remains under-investigated. Methodological approaches to evaluating usage and usability of portals showed room for improvement. To unlock the potential of patient portals, more (robust) research should focus on better understanding the complex process of how portals lead to improved health and care. [ABSTRACT FROM AUTHOR]