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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]

Artificial intelligence (AI) is emerging as a promising healthcare technology. Especially in critical, data-driven, and complex environments such as acute care, the use of AI can significantly improve treatment processes and support clinical staff. To date, AI applications in healthcare are scarce. Research remains fragmented across individual applications. To address this gap, we conduct a systematic literature review. In this review, we map the status quo of AI research in acute care and use service-dominant logic (SDL) from service science as a framework to integrate our analysis. Using a multilayered lens, we (1) identify intended beneficiaries of AI, (2) identify relevant activities supported by AI, and (3) determine those steps of the patient journey that have been in the spotlight for AI research. Our findings suggest that researchers have focused on hospital staff members as intended beneficiaries during the first three steps of the patient journey: admission, diagnosis, and treatment. The patient's perspective, however, remains underexplored. Moreover, 96 % of the research papers we reviewed focus on AI development and proof-of-concept studies, while only 4 % employ and test AI applications in the field. We identify three priorities for future AI research in acute care and provide suitable research methods. • First approach to combine systematic literature review with key determinants of SDL • SDL as an appropriate means to assess existent AI research in acute care • Four analysis determinants (micro level, meso level, activities, patient journey) • 96 % of the research papers focus on AI development and proof-of-concept. • 4 % of the research papers employ field study testing. [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]

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]

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]

Integrating Internet of Things (IoT) technologies in the healthcare industry represents a transformative shift with tangible benefits. This paper provides a detailed examination of IoT adoption in healthcare, focusing on specific sensor types and communication methods. It underscores successful real-world applications, including remote patient monitoring, individualized treatment strategies, and streamlined healthcare delivery. Furthermore, it delves into the intricate challenges to realizing the full potential of IoT in healthcare. This includes addressing data security concerns, ensuring seamless interoperability, and optimizing the use of IoT-generated data. The paper seeks to inspire practitioners and researchers by highlighting the practical implications of IoT in healthcare, emphasizing the ways IoT can enhance patient care, resource allocation, and overall healthcare efficiency. • More than 103 references from top journals are analyzed. • A systematic theoretical analysis of IoT applications in healthcare is provided. • General works on IoT applications in healthcare are well summarized. • Future challenges of IoT applications in healthcare are analyzed from several standpoints. [ABSTRACT FROM AUTHOR]

The acceptance of new technologies by elderly individuals has been long debated in literature, showing barriers in their adoption. However, recent technology features such as gamification have been promising a great deal of impact on their intention to use new technology-based solutions. Particularly in the healthcare sector, gamification has been suggested to increase elderly individuals' intention to use digital therapeutics. However, in the attempt to incentivize the adoption of these new technological innovations, the importance of socialization has been advanced as a further reinforcement mechanism by improving the social interactions of elderly patients thanks to the connections with young or expert people. In this paper, we aim to investigate whether the use of gamification affects elderly patients' intention to use new digital therapeutics (DTx) trying to shed lights on the importance of socialization as a winning strategy to boost their adoption. Through an explorative research design, we conducted a study among a selected group of 168 elderly participants. Our findings reveal that these patients' perceived ease of use gamification-based digital therapeutics positively affects their intention to use, via the perceived usefulness of such new technological features. More importantly, we find that this effect increases in magnitude as elderly patients' socialization tendency seeking increases, revealing the importance of socialization-oriented strategies in stimulating elderly's positive reactions to technological tools. This results in a higher willingness to adopt new digital healthcare therapeutics. Implications for both theory and practices emerge from this research. • Digital therapeutics (DTx) may improve elderly active aging. • Gamification-based systems enrich DTx opportunity. • Patients' perceived ease of use gamification-based digital therapeutics positively affects their intention to use. • Patients' socialization tendency increases their higher willingness to adopt DTx. [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]

• 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 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]

In this paper we explore the impact of the emerging COVID-19 pandemic on the governance of healthcare in the Netherlands. In doing so, we re-examine the idea that a crisis necessarily leads to processes of transition and change by focusing on crisis as a specific language of organizing collective action instead. Framing a situation as a crisis of a particular kind allows for specific problem definitions, concurrent solutions and the inclusion and exclusion of stakeholders. Using this perspective, we examine the dynamics and institutional tensions involved in governing healthcare during the pandemic. We make use of multi-sited ethnographic research into the Dutch healthcare crisis organization as it responded to the COVID-19 pandemic, focusing on decision-making at the regional level. We tracked our participants through successive waves of the pandemic between March 2020 and August 2021 and identified three dominant framings of the pandemic-as-crisis: a crisis of scarcity, a crisis of postponed care and a crisis of acute care coordination. In this paper, we discuss the implications of these framings in terms of the institutional tensions that arose in governing healthcare during the pandemic: between centralized, top-down crisis management and local, bottom-up work; between informal and formal work; and between existing institutional logics. • The COVID-19 pandemic is framed as a crisis of different kinds in healthcare. • Framing impacts decision-making and inclusion of stakeholders. • Ethnographic research provides insights into the consequences of these frames. [ABSTRACT FROM AUTHOR]

Discovering workflow patterns in event-logs is important for many organizations to understand and optimize organizational processes. Although numerous algorithms have been proposed in the literature to discover patterns in sequences of symbols, most of them are inadequate to discover patterns in rich event-log data. In this paper, motivated by the analysis of patient pathways in the health domain, a rich type of event logs, called activity-cost event logs, is considered where each event is associated with a cost. The paper formalizes the problem of mining interesting low-cost patterns in these logs by combining novel concepts of penalties (activity costs) and consistency of patterns, with traditional measures of confidence, length, and time. Furthermore, to extract these patterns efficiently from event logs, an algorithm named TWINCLE (Time-WINdow, Cost and LEngth constrained sequential rule mining) is proposed. Experiments carried out on benchmark datasets and real-life healthcare event logs show that proposed algorithm is efficient and can discover interesting patterns. [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]

The COVID-19 outbreak posed several challenges to healthcare professionals in treating patients: limiting the spread of infection; develop appropriate short-term strategies; and formulating long-term plans. Each of the affected countries had specific capacities before the outbreak began. This potential may have helped reduce the spread of the disease and should help reduce its impact. This paper uses the VMCM method to determine the capacity of health services in European countries. It allowed for a ranking of countries in terms of this potential. This allows the capacity of the health service to be related to the number of infected and deceased people. The purpose of this article is to examine the relationship of infections and mortality on COVID-19 to health care capacity. [ABSTRACT FROM AUTHOR]

Healthcare is a vital human need and always had an important role in our lives, especially in the last year when the pandemic context raised a call for innovative and safe delivery methods. Together with the enlargement in the use of wearable sensors and smartphones, remote healthcare monitoring applications have unfolded at a fast pace, helping with the prevention of spreading diseases as well as to strengthen, help, and assist the health of a patient when a doctor is not physically available. This paper proposes a remote patient monitoring system within an application whose purpose is to bring patients closer to vital medical care. Based on voice interaction and wearable sensors, our application congregates the health data through the Google Fit app from the patient's phone, and then it conducts analysis and sends automatic data alerts for detected anomalies, providing an appealing interface and capabilities to make it engaging to all age groups. Moreover, every patient is under the supervision of a doctor who is constantly monitoring the patient's profile, ensuring assistance through presumptive diagnosis, treatments, or pieces of advice for boosting the quality of lifestyle. [ABSTRACT FROM AUTHOR]

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage. [ABSTRACT FROM AUTHOR]

Complex security environments are characterized by violence (including, but not limited to “armed conflict” in the legal sense), poverty, environmental disasters and poor governance. Violence directly affecting health service delivery in complex security environments includes attacks on individuals (e.g. doctors, nurses, administrators, security guards, ambulance drivers and translators), obstructions (e.g. ambulances being stopped at checkpoints), discrimination (e.g. staff being pressured to treat one patient instead of another), attacks on and misappropriation of health facilities and property (e.g. vandalism, theft and ambulance theft by armed groups), and the criminalization of health workers. This paper examines the challenges associated with researching the context, scope and nature of violence directly affecting health service delivery in these environments. With a focus on data collection, it considers how these challenges affect researchers’ ability to analyze the drivers of violence and impact of violence. This paper presents key findings from two research workshops organized in 2014 and 2015 which convened researchers and practitioners in the fields of health and humanitarian aid delivery and policy, and draws upon an analysis of organizational efforts to address violence affecting healthcare delivery and eleven in-depth interviews with representatives of organizations working in complex security environments. Despite the urgency and impact of violence affecting healthcare delivery, there is an overall lack of research that is of health-specific, publically accessible and comparable, as well as a lack of gender-disaggregated data, data on perpetrator motives and an assessment of the ‘knock-on’ effects of violence. These gaps limit analysis and, by extension, the ability of organizations operating in complex security environments to effectively manage the security of their staff and facilities and to deliver health services. Increased research collaboration among aid organizations, researchers and multilateral organizations, such as the WHO, is needed to address these challenges. [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]

Adequate health care facilities are not available for people of low resource settings in India. It is even worse when it comes to the monitoring of the health of pregnant women in villages. In this paper we discuss about the integrated healthcare system which has both vital parameter monitoring and ultrasound scanning, particularly for pregnant women in rural areas. We present in depth about the significance of vital parameter measurements in pregnant women and the system we designed for this purpose and the experimental setup. The measured parameters are stored in SD card by the healthcare workers who carry the portable system to villages. This SD card is taken to the nearest primary health care center for diagnosis of any pre-natal disorders by medical experts. The same is reported back to the pregnant women in case of any treatment is required. The measured parameter values are also provided in this paper. Such a system would help in global effort for reduction in child and mother mortality rates in low resource settings, identification of prenatal disorders and treatment at right time. [ABSTRACT FROM AUTHOR]

Kevin Dooley (1997), defined Complex Adaptive System (CAS) as a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, such that those patterns then influence behavior of the agents. A healthcare system is considered as a Complex Adaptive System of system (SoS) with agents composed of strategies, people, process, and technology. Healthcare systems are fragmented with independent systems and information. The enterprise architecture (EA) aims to address these fragmentations by creating boundaries around the business strategy and key performance attributes that drive integration across multiple systems of processes, people, and technology. This paper uses a SoS Explorer to select an optimal architecture that provide the necessary capabilities to meet key performance attributes (KPAs) in a dynamic, complex healthcare business environment. The SoS Explorer produced an optimal meta-architecture where all but two systems (disease and facility processes) participated with many of the systems having at least four interfaces. The healthcare meta-architecture produced in this study is not a solution to address the challenges of the healthcare enterprise architecture but provides insight on the areas – systems, capabilities, characteristics, and interfaces – to pay attention to where agility is an important attribute and not to be severely compromised. [ABSTRACT FROM AUTHOR]

The paper aims to demonstrate how the study of everyday clinical work can contribute novel insights into a common and stubborn patient safety problem—the vulnerabilities of handover across care boundaries in emergency care. Based on a dialectical interpretation of the empirical evidence gathered in five National Health Service organisations, the paper argues that performance variability is an essential component in the delivery of safe care, as practitioners translate tensions they encounter in their everyday work into safe practices through dynamic trade-offs based on their experience and the requirements of the specific situation. The findings may shed new light on the vulnerabilities of the handover process, and they might help explain why improvements to handover have remained largely elusive and what type of future recommendations may be appropriate for improving patient safety. [ABSTRACT FROM AUTHOR]

There has been growing interest in the concept of safety cases for medical devices and health information technology, but questions remain about how safety cases can be developed and used meaningfully in the safety management of healthcare services and processes. The paper presents two examples of the development and use of safety cases at a service level in healthcare. These first practical experiences at the service level suggest that safety cases might be a useful tool to support service improvement and communication of safety in healthcare. The paper argues that safety cases might be helpful in supporting healthcare organisations with the adoption of proactive and rigorous safety management practices. However, it is also important to consider the different level of maturity of safety management and regulatory oversight in healthcare. Adaptations to the purpose and use of safety cases might be required, complemented by the provision of education to both practitioners and regulators. [ABSTRACT FROM AUTHOR]

This paper presents an automatic multi-view food classification of a food intake assessment system on a smart phone. Food intake assessment plays important roles in obesity management, which has shown significant impacts on public healthcare. Conventional dietary record-based food intake assessment methods are not popularly applied due to their inconvenience and high reliance on human interactions. This paper presents a smart phone application, named DietCam, to recognize food intakes automatically. The major difficulties in food recognition from images come from uncertainties of food appearances and deformable nature of food especially when they are on a complex background environment. The proposed DietCam system utilizes a multi-view recognition method that separates every food by estimating the best perspective and recognizing them using a probabilistic method. The implemented DietCam system on an iPhone 4 platform showed improved performance compared with baseline methods for food recognition, with an average accuracy of 84% for the selective regular shape foods. [ABSTRACT FROM AUTHOR]

• A review about the use and evolution of IoT solutions in smart health care is presented. • The work discusses the role of AI in the internet of medical things context. • Particular attention is given to solutions and approaches exploiting fog and edge computing for running AI algorithms. In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects huge number of devices using the internet to collect data. The IoT has a variety of application domains, including health care. In this scenario, the patients can use wearable or implanted medical sensors for measuring medical parameters anywhere and anytime. The medical sensors are typically equipped with batteries and have limited energy. In order to prolong the network lifetime, a powerful routing protocols are required. In this paper, we propose PEERP: An Priority-based Energy-Efficient Routing Protocol for Reliable Data Transmission in Healthcare using the IoT. We classify health information into two categories assigned corresponding priorities: Emergency Situation and Vital Health Data. The Emergency Situation is the highest priority data and should be successfully delivered as soon as required. The Vital Health data is the data requested by doctors for continuous monitoring of the patients. The direct communication is used for critical data while Multi-hop communication is used for vital health data delivery. The simulation results show that the routing protocol can improve the energy utilization of the sensor nodes and prolong the network lifetime while guaranteeing the delivery ratio and reliability. [ABSTRACT FROM AUTHOR]

An endemic challenge facing healthcare systems around the world is how to spread innovation more widely and sustainably. A common response to this challenge involves conducting pilot implementation studies to generate evidence of the innovation's benefits. However, despite the key role that such studies play in the local adoption of innovation, their contribution to the wider spread and sustainability of innovation is relatively under-researched and under-theorized. In this paper we examine this contribution through an empirical examination of the experiences of an innovation intermediary organization in the English NHS (National Health Service). We find that their work in mobilizing pilot-based evidence involves three main strands; configuring to context; transitioning evidence; and managing the transition. Through this analysis we contribute to theory by showing how the agency afforded by intermediary roles can support the effective transitioning of pilot-based evidence across different phases in the innovation journey, and across different occupational groups, and can thus help to create a positive feedback loop from localized early implementers of an innovation to later more widespread adoption and sustainability. Based on these findings, we develop insights on the reasons for the unnecessary repetition of pilots – so-called 'pilotitis'- and offer policy recommendations on how to enhance the role of pilots in the wider spread and sustainability of innovation. • Innovation intermediaries mobilize evidence from pilot studies to spread innovation. • Pilot-based evidence can link early adoption to wider spread and sustainability. • Mobilizing involves co-creating multiple forms of evidence with different groups. • Intermediary roles allow transitioning of pilot-based evidence into wider adoption. • Pilotitis involves a failure to transition evidence along the innovation journey. [ABSTRACT FROM AUTHOR]

Highlights • A validated ad hoc taxonomy of 21 Influencing Factors for HRA in surgery. • IFs influence estimated by eliciting surgeons' perception. • Rude talk and disrespectful behaviours impact surgical performance the most. • Some IFs show higher influence in mini-invasive surgery compered to open surgery. • • No statistical correlation between the influence of IFs and surgeon's proficiency. Abstract Several authors encouraged the application of Human Reliability Analysis (HRA) to patient care, recognising the benefits it brought to high-risk industries in terms of human errors reduction, thanks to its anticipative approach. However, the literature on HRA in healthcare is still scanty, and shows significant gaps at methodological level. Only a very limited number of HRA studies implemented Influencing Factors (IFs) analysis in the quantification of the Human Error Probability, despite it represents a peculiar component and a well-established step in several HRA techniques. Furthermore, the need for a deep adaptation and translation of existing HRA techniques to the healthcare domain is emphasised by several authors. This paper aims at designing a taxonomy of Influencing Factors for HRA studies in surgery, and assessing their influence on surgeons' technical performance by means of structured elicitation of experts' judgement. An ad hoc taxonomy of 21 IFs has been validated through focus group and individual interviews, in Denmark and Italy; 215 questionnaires from expert surgeons in open and mini-invasive surgeries were then used for the quantification of the impact of each IF on surgeons' performance. An investigation on the possible influence of different surgical techniques revealed that only two out of the twenty-one IFs show different probability density functions (pdf) when considered in open or mini-invasive surgeries. The study offers an original and relevant contribution to the development of domain-specific knowledge and, as such, to fostering the diffusion of HRA studies in healthcare, and in surgery in particular. [ABSTRACT FROM AUTHOR]

Various mobile phone applications (hereafter apps) challenge instituted ways of working in healthcare. This study explores the institutional breaches arising from the use of apps in doctor-patient interactions. This paper argues that institutional breaches, however small, are important occasions for observing the contextual intersections between healthcare, regulation and technology in a hospital setting. Based on healthcare professionals' normative judgements, the paper offers an empirically grounded understanding of institutional legitimacy-claiming; safeguarding responses deployed by the instituted regime, and the case-building responses deployed by the instituting persuaders. Institutional breach persistence arises from the moral dimension of legitimacy and is grounded in asymmetrical dynamics between two virtuous healthcare narratives. The paper concludes with a discussion of the contextual intersections between healthcare, regulation and technology, paying particular attention to institutional breaches as experimentation, the contestation of normativity and patterns of technology indulgency in healthcare work. • New healthcare technology use is inherently disruptive. • Institutional legitimacy-claiming involves safeguarding responses. • Institutional legitimacy-claiming involves the case-building responses. • Institutional breach persistence arises from the moral dimension of legitimacy. [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]

The framework of design for the circular economy is increasingly used in industry to improve product sustainability and decrease costs, and in academia various models have been developed to guide circular design. However, in the medical sector, although it generates a large amount of waste, application of circular design principles is difficult because of the clinical challenges of safety and sterility that reuse of products or materials entail. This paper categorizes and analyses existing instances of circular economy in the medical sector, using a literature review and examination of existing industry examples. This is used to identify challenges and unmet opportunities for circular design in the medical sector. The key factors affecting circular medical design are found to be device criticality in terms of sterilization requirements, device value and the organizational support structure around the device. A design heuristic and suggested strategies for circular design of medical products are proposed based on these findings. [ABSTRACT FROM AUTHOR]

In the European Union, it is clear that as a result of the combination of several factors the current healthcare system is unable to handle the increasing number of patients. Primarily most of the facilities are not prepared for the effects of the increase in longevity or rapid shocks. Furthermore, in our rapidly aging society, the healthcare professional-patient ratio is constantly deteriorating. This creates a huge gap among predicted, expected, and received healthcare services. Therefore, the importance and necessity of a highly personalized treatment process as services are unquestionable. In a way, this is similar to hospitality services, since the ultimate goal is to gain people's satisfaction. So the primary question is how do we jump over the gap of a shortage of medical professionals and provide personalized high-quality care that could support hospitality in healthcare? As an answer, our paper presents interconnected solutions to the reader: eHealth solutions and a unique university-level health-related course where new rehabilitation experts are trained to support the essence of hospitality in hospitals, and also at homecare. • Consumer and patient behavior and expectations change due to digitalization. • Huge gap among predicted, expected, and received healthcare services. • eHealth and new specialists needed for customer-oriented healthcare hospitality. [ABSTRACT FROM AUTHOR]

Framing of causal interdependencies and uncertainties is important due to factors bringing change to healthcare, such as increasing access to healthcare information via the Internet and increasing self-care. In this paper, it is explained how critical realism can provide a basis for framing causal interdependencies and causal uncertainties within healthcare. The need for critical realist framing of causal interdependencies and uncertainties in healthcare is explained from the perspectives of human to human communication and human to AI communication. An example is provided of critical realist framing for causal interdependencies. Also, an example is provided of critical realist framing for causal uncertainties. Implications are discussed for human to human communication and human to AI communication. Critical realism can provide framing that is easy to grasp and recall. Importantly, critical realist framing can encompass both causal specificity and holistic overview when integrating diverse healthcare options. Moreover, critical realist framing can encompass uncertainties across the “why”, “how”, and “what” of healthcare. These characteristics of critical realist framing are important as end-users become increasingly aware of healthcare diversity and uncertainty. [ABSTRACT FROM AUTHOR]

The enormous exploitation of connected objects for patient monitoring becomes a source of a huge quantity of health data. This data can be badly expressed, understood, and exploited by other systems and devices. Additionally, the huge amount of connected objects embeds with resource-constrained devices (sensors, actuators, RFIDS, etc.) are also challenging. Consequently, semantic representation of both medical connected objects and their data becomes important. Furthermore, the semantic reasoning of this data requires the definition of diverse rules in order to provide adequate and efficient results. This paper suggests two main contributions. The first one focuses on the semantic representation of both the medical connected objects and their data by proposing a HealthIoT ontology. The second contribution is proposed to provide practical backup to the use of this ontology. In fact, an IoT Medicare system is proposed to evaluate the HealthIoT ontology. [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]

This paper reports research on improving decisions about hospital discharges – decisions that are now made by physicians based on mainly subjective evaluations of patients’ discharge status. We report an experiment on uptake of our clinical decision support system (CDSS) which presents physicians with evidence-based discharge criteria that can be effectively applied at the point of care where the discharge decision is made. One experimental treatment we report prompts physician attentiveness to the CDSS by replacing the default option of universal “opt in” to patient discharge with the alternative default option of “opt out” from the CDSS recommendations to discharge or not to discharge the patient on each day of hospital stay. We also report results from experimental treatments that implement the CDSS under varying conditions of time pressure on the subjects. The experiment was conducted using resident physicians and fourth-year medical students at a university medical school as subjects. [ABSTRACT FROM AUTHOR]

Nowadays, healthcare systems are forced to perform highly from dissimilar perspectives. Solutions that combine diverse managerial approaches could be helpful to face this challenge. To this extent, this paper aims at studying how to implement Health Lean Management (HLM) projects with benefits of Clinical Risk Management (CRM). Considering the innovativeness of the topic, a multiple case study has been conducted selecting HLM projects with patient safety improvements (L&S projects) from operative units of different hospitals. Data were collected by executing semi-structured interviews. In-depth interviews, observations and analysis of relevant documentation were also performed to triangulate the information about hospital context, organizational and managerial aspects. Based on these data, a cross-case analysis was performed. Categorizing the cases, three clusters with distinguishing profiles emerged. Significant propositions were grasped highlighting the organizational and managerial characteristics of L&S projects. In particular, cases regarding front-office processes show distinct features and projects with intentional direct impacts on patient safety report peculiar characteristics, highlighting the need of CRM involvement to achieve multiple objectives. Besides interesting common evidences, indications distinguished by clusters are delineated. Patient safety improvements are intentionally obtained by managing wastes and errors, while they are unexpectedly reached by identifying and solving organizational issues. This research contributes to define guidelines for implementing L&S projects, which are still missing in literature, but which constitute a support for hospital managers who operate in a complex and turbulent context. [ABSTRACT FROM AUTHOR]

As the healthcare industry evolved, it became an integral participant in providing services. In recent years, remote observation services have grown increasingly important for patients. In cases where patients are not physically present in clinics or hospitals, healthcare providers produce observations systems that diagnose and prescribe treatment. In/on patients' bodies, sensors are installed. Data can be exchanged wirelessly between healthcare systems. As a result of the implementation of blockchain-based characteristics (e.g. distributed ledger, decentralized storage, and authentication), healthcare systems can be enhanced. Accordingly, smart contracts are applied for the achievement of more authentication and sharing of records, and requirements for participants automatically. As the number of users or other components increases, blockchains and smart contracts should be able to provide quality service. Technically, smart contracts using blockchains have several challenges that are important, such as scalability, enhanced security, and optimal performance. There is still a question about the scalability of blockchain as there is a significant amount of computational power needed to scale up with a high number of participants in healthcare systems. In this paper, Hierarchical State Machines (HSMs) and ZoKrates are used to lower the consumption gas and the scalability of blockchain with smart contracts. [ABSTRACT FROM AUTHOR]

• Qualitative study of stakeholder perceptions of healthcare AI. • Diversity of views can support responsible innovation. • There is a need for a systems approach to AI safety. • The impact on patient – clinician relationship needs consideration. • Ethical, legal and societal aspects need to be addressed. There is an increasing number of healthcare AI applications in development or already in use. However, the safety impact of using AI in healthcare is largely unknown. In this paper we explore how different stakeholders (patients, hospital staff, technology developers, regulators) think about safety and safety assurance of healthcare AI. 26 interviews were undertaken with patients, hospital staff, technology developers and regulators to explore their perceptions on the safety and the safety assurance of AI in healthcare using the example of an AI-based infusion pump in the intensive care unit. Data were analysed using thematic analysis. Participant perceptions related to: the potential impact of healthcare AI, requirements for human-AI interaction, safety assurance practices and regulatory frameworks for AI and the gaps that exist, and how incidents involving AI should be managed. The description of a diversity of views can support responsible innovation and adoption of such technologies in healthcare. Safety and assurance of healthcare AI need to be based on a systems approach that expands the current technology-centric focus. Lessons can be learned from the experiences with highly automated systems across safety-critical industries, but issues such as the impact of AI on the relationship between patients and their clinicians require greater consideration. Existing standards and best practices for the design and assurance of systems should be followed, but there is a need for greater awareness of these among technology developers. In addition, wider ethical, legal, and societal implications of the use of AI in healthcare need to be addressed. [ABSTRACT FROM AUTHOR]

Background and objective In the field of biomedical, it becomes necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and telemedicine system. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits. Method This paper, presents an algorithm based on singular value decomposition (SVD), and embedded zero tree wavelet (EZW) techniques for ECG signal compression which deals with the huge data of ambulatory system. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2-D) ECG data array using SVD, and then EZW is initiated for final compression. Initially, 2-D array construction has key issue for the proposed technique in pre-processing. Here, three different beat segmentation approaches have been exploited for 2-D array construction using segmented beat alignment with exploitation of beat correlation. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is very efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments. Results The evaluation results illustrate that the proposed algorithm has achieved the compression ratio of 24.25:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference (PRD) as 1.89% for ECG signal Rec. 100 and consumes only 162 bps data instead of 3960 bps uncompressed data. Conclusion The proposed method is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction. Simulated results are clearly illustrate the proposed method can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare 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]

Electronic Medical Records (EMR) holds the medical data of the patients in an electronic form. It appears as a massive Big Data that needs to be stored in elastic clouds which are generally handled by a third party. The cloud service provider acts as a third party here and have the access to all the EMRs. Also, most of the times, the patient information needs to be shared with other research analysts and medical professionals for research or expert opinion. This raises serious concerns regarding the privacy of the patient's data. In this paper, an approach based on reducing the re-identification risk is proposed to preserve the privacy of the EMRs. The proposed solution is based on k -Anonymity, l -Diversity, t -Closeness & δ-Presence and is implemented through ARX Anonymization tool. We have implemented the solution on randomly generated medical dataset based on extension of publicly available EMRs. The result shows that the re-identification risks are reduced to 2.33% from 100%. [ABSTRACT FROM AUTHOR]

Today, the over $2 trillion US healthcare system is ravaged by fraud, waste, and abuse, with an estimated one-third of all these costs frivolously spent in such ways. Sun Tzu wrote, “Every battle is won or lost before it's ever fought.” To combat healthcare fraud, we must understand it and the forms it takes. In this paper, we systematically evaluate published literature using Webster and Watson's concept matrix technique. From the applicable published literature, we provide a categorization and description of the documented types of fraud in healthcare. [ABSTRACT FROM AUTHOR]

We live today in a world wide open, where privacy is hardly maintained, with the big data getting involved, it's only getting worse, since data is henceforth everywhere and about everyone, we can’t really tell who knows what about who, where the data is actually relying, and what uses are made out of it. Privacy though is a very important aspect that people always require in many sectors, and it's not less important in the healthcare industry, where information only gets even more personal and sensitive that any privacy breach becomes an issue of life or death. In this paper, we have focused on the benefits of big data in the healthcare sector, as well as some of the main challenges often encountered. We have then concentrated on the Moroccan context, shown the problems it has and its specificities, in order to suggest recommendations to encourage the decision makers to take the path of Big Data. And finally, we have enlightened the privacy issues in healthcare and the current methods used to ensure the patient's privacy. [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]

This paper investigates the mechanisms behind the health effects of retirement. Using a Regression Discontinuity Design to exploit financial incentives in the German pension system for identification, I find that retirement improves subjective health status and mental health, while also reducing outpatient care utilization. I explore a wide range of health behaviors, time use, and effect heterogeneity as potential mechanisms. Relief from work-related stress and strain, increased sleep duration as well as more frequent physical exercise seem to be key mechanisms through which retirement affects health. [ABSTRACT FROM AUTHOR]

This paper presents a new multi-objective mathematical model to address a Healthcare Inventory Routing Problem (HIRP) for medicinal drug distribution to healthcare facilities. The first part of objective function minimizes total inventory and transportation costs, while satisfaction is maximized by minimizing forecast error which caused by product shortage and the amount of expired drugs; Greenhouse Gas (GHG) emissions are also minimized. A demand forecast approach has been integrated into the mathematical model to decrease drug shortage risk. A hybridized possibilistic method is applied to cope with uncertainty and an interactive fuzzy approach is considered to solve an auxiliary crisp multi-objective model and find optimized solutions. [ABSTRACT FROM AUTHOR]

This paper gives an insight of how we can uncover additional value from the data generated by healthcare and government. Large amount of heterogeneous data is generated by these agencies. But without proper data analytics methods these data became useless. Big Data Analytics using Hadoop plays an effective role in performing meaningful real-time analysis on the huge volume of data and able to predict the emergency situations before it happens. It describes about the big data use cases in healthcare and government. [ABSTRACT FROM AUTHOR]

This research aims to describe pattern recognition models for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. The early anticipation of anomalies can improve the rate of disease prevention. Here we present different learning techniques for predicting abnormalities and behavioural trends in various user contexts. In this paper we described a Hidden Markov Model based approach for detecting abnormalities in daily activities, a process of identifying irregularity in routine behaviours from statistical histories and an exponential smoothing technique to predict future changes in various vital signs. The outcomes of these different models are then fused using a fuzzy rule-based model for making the final guess and sending an accurate context-aware alert to the health-care service providers. We demonstrated the proposed techniques by evaluating some case studies for different patient scenarios in ambient assisted living. [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]