Building Caring Healthcare Systems in the Internet of Things

Technology Applications in Tracking 2019-nCoV and Defeating Future Outbreaks: Iraqi Healthcare Industry in IoT Remote

A serious effect on people's life, social communication, and surely on medical staff who were forced to monitor their patients' status remotely relying on the available technologies to avoid potential infections and as a result reducing the workload in hospitals. this research tried to investigate the readiness level of healthcare professionals in both public and private Iraqi hospitals to utilize IoT technology in detecting, tracking, and treating 2019-nCoV pandemic, as well as reducing the direct contact between medical staff and patients with other diseases that can be monitored remotely.A cross-sectional descriptive research via online distributed questionnaire, the sample consisted of 113 physicians and 99 pharmacists from three public and two private hospitals who randomly selected by simple random sampling. The 212 responses were deeply analyzed descriptively using frequencies, percentages, means, and standard deviation.The results confirmed that the IoT technology can facilitate patient follow-up by enabling rapid communication between medical staff and patient relatives. Additionally, remote monitoring techniques can measure and treat 2019-nCoV, reducing direct contact by decreasing the workload in healthcare industries. This paper adds to the current healthcare technology literature in Iraq and middle east region an evidence of the readiness to implement IoT technology as an essential technique. Practically, it is strongly advised that healthcare policymakers should implement IoT technology nationwide especially when it comes to safe their employees' life.Iraqi medical staff are fully ready to adopt IoT technology as they became more digital minded after the 2019-nCoV crises and surely their knowledge and technical skills will be improved spontaneously based on diffusion of innovation perspective.

Ionic shape memory polymer gels as multifunctional sensors

Novel ionic shape memory polymer (SMP) gels were fabricated using SMPs and ionic liquids (ILs) of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMI-TFSI) at different weight ratios (W_IL). The shape memory effect and sensor performance of the ionic SMP gels were investigated by means of thermomechanical and mechanoelectrical analyses. It was found that the ionic SMP gel at W_IL = 25 wt% showed a shape memory effect with the shape fixing ratio (R_f) and shape recovery ratio (R_r) of 72.7% and 72.9%, respectively. Upon bending, the ionic SMP gel sensors with PEDOT:PSS electrodes generated an open circuit voltage of 3.3 mV and a charge of 1.6 nC which linearly increased with increasing bending displacement and velocity, respectively. Furthermore, the wearable shape memory multifunctional sensor array was demonstrated as a self-powered motion sensor for IoT applications.

Telemedicine Technologies Selection for the Posthospital Patient Care Process after Total Hip Arthroplasty

For many years, the importance of using telematic technologies in medicine has been growing, especially in the period of the coronavirus pandemic, when direct contact and supervision of medical personnel over the patient is difficult. The existing possibilities of modern information and communication technologies (ICTs) are not fully used. The aim of the study is to identify the telemedicine technologies that can be used in future implementation projects of the posthospital patient care process after total hip arthroplasty (THA). The literature search is reported according to PRISMA 2020. The search strategy included databases and gray literature. In total, 28 articles (EMBASE, PubMed, PEDro) and 24 records from gray literature (Google Search and Technology presentations) were included in the research. This multi-source study analyzes the possibilities of using different technologies useful in the patient care process. The conducted research resulted in defining visual and wearable types of telemedicine technologies for the original posthospital patient care process after THA. As the needs of stakeholders in the posthospital patient care process after THA differ, the awareness of appropriate technologies selection, information flow, and its management importance are prerequisites for effective posthospital patient care with the use of telemedicine technologies.

Developing Internet of Things-related ISO 10001 Hand Hygiene Privacy Codes in healthcare

Purpose

Augmentation of an ISO 10001 code system for healthcare worker (HW) satisfaction with ISO/IEC 27701 and ISO/IEC 29184 privacy-related subsystems is shown. Four specific codes regarding the privacy of HWs using electronic devices for hand hygiene (HH) monitoring and the related activities are presented.

Design/methodology/approach

HWs’ concerns involving automated hand hygiene monitoring technologies were identified through a literature review and classified. Privacy codes (PCs) that deal with such concerns were developed. ISO/IEC 27701 requirements for privacy information were mapped to the elements of these codes, labelled as “Healthcare Workers’ Hand Hygiene Privacy Codes (HW-HH-PCs)”. Both ISO/IEC 27701 and ISO/IEC 29184 guidelines for Privacy Notices and consent were linked with the activities for preparing the code resources.

Findings

Components of an ISO/IEC 27701 system, the guidance of ISO/IEC 29184 and the definitions provided in ISO/IEC 29100 can assist the preparation of HW-HH-PCs and the required resources. An ISO/IEC 29184 Privacy Notice can be used as input for developing an Informed Consent Form, which can be implemented to suit two of the four developed HW-HH-PCs.

Practical implications

HW-HH-PCs and the supporting resources, which healthcare organizations could implement to potentially increase quality assurance of an automated HH monitoring service, are illustrated.

Originality/value

Integrative augmentation of ISO 10001:2018, ISO/IEC 27701:2019 and ISO/IEC 29184:2020 within an underlying framework from ISO/IEC 20000–1:2018 for information technology service, together with the related examples of privacy-related customer satisfaction codes and the corresponding resources, is introduced.

Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions

This paper provides a comprehensive literature review of various technologies and protocols used for medical Internet of Things (IoT) with a thorough examination of current enabling technologies, use cases, applications, and challenges. Despite recent advances, medical IoT is still not considered a routine practice. Due to regulation, ethical, and technological challenges of biomedical hardware, the growth of medical IoT is inhibited. Medical IoT continues to advance in terms of biomedical hardware, and monitoring figures like vital signs, temperature, electrical signals, oxygen levels, cancer indicators, glucose levels, and other bodily levels. In the upcoming years, medical IoT is expected replace old healthcare systems. In comparison to other survey papers on this topic, our paper provides a thorough summary of the most relevant protocols and technologies specifically for medical IoT as well as the challenges. Our paper also contains several proposed frameworks and use cases of medical IoT in hospital settings as well as a comprehensive overview of previous architectures of IoT regarding the strengths and weaknesses. We hope to enable researchers of multiple disciplines, developers, and biomedical engineers to quickly become knowledgeable on how various technologies cooperate and how current frameworks can be modified for new use cases, thus inspiring more growth in medical IoT.

Towards Responsible Implementation of Monitoring Technologies in Institutional Care

Increasing awareness of errors and harms in institutional care settings, combined with rapid advancements in artificial intelligence, have resulted in a widespread push for implementing monitoring technologies in institutional settings. There has been limited critical reflection in gerontology regarding the ethical, social, and policy implications of using these technologies. We critically review current scholarship regarding use of monitoring technology in institutional care, and identify key gaps in knowledge and important avenues for future research and development.

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance

BACKGROUND

One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity.

OBJECTIVE

The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time.

METHODS

From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model.

RESULTS

The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays.

CONCLUSIONS

This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.

Smart Cities: A Survey on New Developments, Trends, and Opportunities

The continued growth of the population in urban areas has called for smarter cities for the 21st century. While great progress has been made in the last two decades in this regard, remaining challenges faced by city planners have forced them to pursue an alternative version of smart cities. Recent advancements in several technological areas like 5G communications, blockchain, and virtual/augmented reality have facilitated this process. This paper aims at providing a review of the definitions and components of current smart cities. It also discusses new developments, recent trends, and business opportunities.

Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review

BACKGROUND

Over the last century, disruptive incidents in the fields of clinical and biomedical research have yielded a tremendous change in health data management systems. This is due to a number of breakthroughs in the medical field and the need for big data analytics and the Internet of Things (IoT) to be incorporated in a real-time smart health information management system. In addition, the requirements of patient care have evolved over time, allowing for more accurate prognoses and diagnoses. In this paper, we discuss the temporal evolution of health data management systems and capture the requirements that led to the development of a given system over a certain period of time. Consequently, we provide insights into those systems and give suggestions and research directions on how they can be improved for a better health care system.

OBJECTIVE

This study aimed to show that there is a need for a secure and efficient health data management system that will allow physicians and patients to update decentralized medical records and to analyze the medical data for supporting more precise diagnoses, prognoses, and public insights. Limitations of existing health data management systems were analyzed.

METHODS

To study the evolution and requirements of health data management systems over the years, a search was conducted to obtain research articles and information on medical lawsuits, health regulations, and acts. These materials were obtained from the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, Elsevier, MEDLINE, PubMed, Scopus, and Web of Science databases.

RESULTS

Health data management systems have undergone a disruptive transformation over the years from paper to computer, web, cloud, IoT, big data analytics, and finally to blockchain. The requirements of a health data management system revealed from the evolving definitions of medical records and their management are (1) medical record data, (2) real-time data access, (3) patient participation, (4) data sharing, (5) data security, (6) patient identity privacy, and (7) public insights. This paper reviewed health data management systems based on these 7 requirements across studies conducted over the years. To our knowledge, this is the first analysis of the temporal evolution of health data management systems giving insights into the system requirements for better health care.

CONCLUSIONS

There is a need for a comprehensive real-time health data management system that allows physicians, patients, and external users to input their medical and lifestyle data into the system. The incorporation of big data analytics will aid in better prognosis or diagnosis of the diseases and the prediction of diseases. The prediction results will help in the development of an effective prevention plan.

IoT cyber risk: a holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process

Security vulnerabilities of the modern Internet of Things (IoT) systems are unique, mainly due to the complexity and heterogeneity of the technology and data. The risks born out of these IoT systems cannot easily fit into an existing risk framework. There are many cybersecurity risk assessment approaches and frameworks that are under deployment in many governmental and commercial organizations. Extending these existing frameworks to IoT systems alone will not address the new risks that have arisen in the IoT ecosystem. This study has included a review of existing popular cyber risk assessment methodologies and their suitability to IoT systems. National Institute of Standards and Technology, Operationally Critical Threat, Asset, and Vulnerability Evaluation, Threat Assessment & Remediation Analysis, and International Standards Organization are the four main frameworks critically analyzed in this research study. IoT risks are presented and reviewed in terms of the IoT risk category and impacted industries. IoT systems in financial technology and healthcare are dealt with in detail, given their high-risk exposure. Risk vectors for IoT and the Internet of Medical Things (IoMT) are discussed in this study. A unique risk ranking method to rank and quantify IoT risk is introduced in this study. This ranking method initiates a risk assessment approach exclusively for IoT systems by quantifying IoT risk vectors, leading to effective risk mitigation strategies and techniques. A unique computational approach to calculate the cyber risk for IoT systems with IoT-specific impact factors has been designed and explained in the context of IoMT systems.

Looking at Fog Computing for E-Health through the Lens of Deployment Challenges and Applications

Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed.