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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]

• 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]

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]

Inspired by the observation that a healthcare system usually involves various intelligent technologies from different disciplines, especially metaheuristics and data mining, this paper provides a brief survey of metaheuristics for healthcare system and a roadmap for researchers working on metaheuristics and healthcare to develop a more efficient and effective healthcare system. This paper begins with a discussion of changes for healthcare, followed by a brief review of the features of “up-to-date technologies for healthcare.”. Then, a learnable big data analytics framework for healthcare system is presented which provides a high performance solution to the forthcoming challenges of big data. Finally, changes, potentials, open issues, and future trends of metaheuristics for healthcare are addressed. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework that takes advantage of the computing capabilities provided by the Internet of Thing (IoT) paradigm in order to support collaborative applications. It looks at the requirements needed to run a wide range of computing tasks on a set of devices in the user environment with limited computing resources. This approach contributes to building the social dimension of the IoT by enabling the addition of computing resources accessible to the user without harming the other activities for which the IoT devices are intended. The framework mainly includes a model of the computing load, a scheduling mechanism and a handover procedure for transferring tasks between available devices. The experiments show the feasibility of the approach and compare different implementation alternatives. [ABSTRACT FROM AUTHOR]

There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians' resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model's generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model's clinical impact; (6) internal validation of the model's performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems. [ABSTRACT FROM AUTHOR]

This study is the first that theorises and empirically tests, at the organisational level, the role that ‘Green’ Human Resource Management (HRM) practices play in facilitating employees' collective engagement in environment protection by taking voluntary actions that ‘go the extra mile’. In this regard, we take an original perspective on Environmental Management (EM) as an organisational change that requires strong support from employees. This allows us to investigate the mediating role of collective commitment to change – and specifically to EM – with regard to the relationship between ‘Green’ HRM practices and collective voluntary behaviours towards the environment. Our results show that ‘Green’ HRM practices are conducive to voluntary behaviours towards the environment at the collective level. Moreover, employees' willingness to support their organisation in its EM endeavour partially mediates this relationship. Additionally, by conceptualising three different types of ‘Green’ HRM practices, our results disentangle their relative importance and their differing impact on collective behaviours towards the environment. This paper thus provides scholars of EM and managers with original evidence-based guidelines on how to leverage ‘Green’ HRM to enhance employees' collective attitudes and behaviours towards the environment. [ABSTRACT FROM AUTHOR]

Blood supply chains play a key role in the healthcare systems. Any improvement in the management of these chains will have a direct impact on the supply of blood as a life-saving product. This paper presents a mixed integer linear programming model to make strategic as well as tactical decisions in a blood collection system over a multi-period planning horizon. A robust possibilistic programming approach is applied to cope with the inherent epistemic uncertainty of the model’s parameters. Several numerical examples are solved to demonstrate the robustness of solutions and to provide managerial insights. Finally, the applicability of the proposed model is demonstrated using a real case study in Iran. [ABSTRACT FROM AUTHOR]

The design and analysis of process models is a critical factor for organizational improvement across various industries. Thanks to its potential to enable common understanding and foster automation, process modeling is increasingly adopted in the healthcare sector. However, the complexity of the healthcare domain makes process modeling a challenging task, potentially explaining the modest uptake of process modeling standards like the Business Process Model and Notation (BPMN). In this paper, we identify common challenges of process modeling in healthcare, elicited from healthcare process modeling initiatives and supported by the literature. For each challenge, we present some BPMN best practices in the form of ready-to-use process fragments that guide the standard modeling of complex healthcare aspects. Also, we report the results of a first evaluation of the use and perceived usefulness of best practices conducted with junior experts in medicine and IT. We observed that the domain-specific process fragments help to capture healthcare aspects in detail and are perceived as a source of learning, turning out to be especially useful for modelers with a basic understanding of BPMN and the healthcare domain. [Display omitted] • A structured elicitation of relevant modeling needs and challenges in healthcare processes. • The definition of a set of one or more BPMN best practices for each identified challenge in the form of ready-to-use process fragments applicable in existing BPMN modeling and execution tools. • An indicative evaluation of the influence and perception of the BPMN best practices through a modeling experiment with participants having a mixed background in medicine or IT without and with the help of the best practices. [ABSTRACT FROM AUTHOR]

Concrete privacy endeavours to give Confidentiality, Integrity, and Availability (CIA) measures to secure traffic streams in sensitive healthcare applications are a necessity. When talking about the access of sensitive healthcare data or the confidentiality of highly esteemed data, the first thing that comes to mind is that it should be secured. Sensitive healthcare data needs to be protected to restrict illegal access, exposure, and/or manipulation. As there is no such protection by which we can make our systems fully secure, the most acceptable methodology is to perform layered modelling of safety measures. This paper intends to provide a mathematical description of the Modular Encryption Standard (MES) and the augmentation of condition-centric risk monitoring of confidential information to provide a layered model for securing healthcare data confidentiality. Decision-making regarding the risk monitoring strategy of MES is augmented using a machine learning approach based on a Fuzzy Inference System amalgamated with Neural Networks. Result analysis shows that MES has less than a 0.005 error-rate and a 97% precision-rate, which elucidates its desideratum towards increasing security risks. [Display omitted] • Propose an approach for securing healthcare data confidentiality. • Provide mathematical modelling for the entire working of Modular Encryption Standard. • Enhance the decision-making strategy of Modular Encryption Standard using fuzzy logic. [ABSTRACT FROM AUTHOR]

In healthcare, inter-organizational sharing and collaborative use of big data become increasingly important. The cloud-computing paradigm is expected to provide an environment perfectly matching the needs of collaborating healthcare workers. However, there are still many security and privacy challenges impeding the wide adoption of cloud computing in this domain. In this paper, we present a novel architecture and its implementation for inter-organizational data sharing, which provides a high level of security and privacy for patient data in semi-trusted cloud computing environments. This architecture features attribute-based encryption for selective access authorization and cryptographic secret sharing in order to disperse data across multiple clouds, reducing the adversarial capabilities of curious cloud providers. An implementation and evaluation by several experiments demonstrate the practical feasibility and good performance of our approach. [ABSTRACT FROM AUTHOR]

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing. Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy. [ABSTRACT FROM AUTHOR]

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice. [ABSTRACT FROM AUTHOR]

The prevalence of musculoskeletal (MSK) symptoms in radiographers is high, similar to other healthcare occupations that involve high levels of physical exertion (e.g. patient handling; grasping and moving equipment). Reports of interventions to reduce MSK discomfort in radiographers are limited. A participatory approach was used to investigate daily challenges, needs, and opportunities for developing interventions to address exposures to many of the risk factors that contribute to MSK symptoms in radiographers. In this paper, we present the expressed needs of experienced radiographers (including assistance with patient handling, security, supportive design of equipment and work spaces), along with their evaluations of several intervention concepts intended to address some of those needs. We also report results from tests of three prototype interventions stemming from this participatory process that demonstrate the potential for new engineering control concepts to reduce the physical effort associated with some of the most common tasks radiographers perform. [ABSTRACT FROM AUTHOR]