Developing and evaluating the Digitally Enhanced Liberation from VEntilation (DELVE) system: A study of feasibility and user acceptance

Introduction: In common with many aspects of critical illness recovery, there is no universally accepted formula for "weaning," the term used to describe the process of liberating patients from mechanical ventilation.1-3 Understanding a patient's progress during a prolonged wean can be difficult and requires integration of various datasets. Therefore, it is good practice to ensure that weaning prescriptions are clear, easy to follow and adhered to and that weaning-associated data and meta data are recorded accurately and are easy to interpret. The prototype Digitally Enhanced Liberation from VEntilation (DELVE) system has been designed to be used in combination with the Puritan Bennett(TM) 980 (PB980) ventilator (Covidien, USA). DELVE is an open-loop system which provides an interactive weaning chart, combining the weaning prescription entered by the clinical staff, with actual settings recorded from the ventilator in order to display compliance with the prescription (Figure 1). DELVE also collects measured data from the ventilator which could be used to display respiratory performance, both real-time and historical. Figure 1. DELVE set up with the PB980 ventilator (in the simulation suite). Objective(s): This feasibility study was designed to inform development of the first DELVE prototype and a future clinical trial to determine clinical effectiveness and usefulness. The study objectives were to determine whether DELVE could: 1. Present a digital weaning chart that staff could use effectively and would be superior to the current paper version. 2. Record and display the patients' ventilatory performance, both real time and historical, during liberation from mechanical ventilation. Method(s): This was a mixed-methods, prospective feasibility study of a complex intervention.4 Ventilated patients with a tracheostomy, commencing the weaning process, were recruited from an adult intensive care unit. DELVE was used alongside the current paper-based system for weaning planning and data collection. Patients remained in the study until they no longer required the support of the PB980 ventilator. Result(s): Twenty patients were enrolled for between 25 and 270 hours each. There were no safety incidents or data breaches. DELVE was successfully operated by staff, who were able to connect DELVE to the ventilator, prescribe weaning plans and analyse adherence. The digital weaning chart user interface was intuitive and easy to navigate. It was clearer, more complete and easier to interpret when compared to the paper weaning charts (Figure 2). DELVE reliably collected data every ten seconds and safely stored over six million items of measured data and 25000 events, such as alarm triggers and setting changes, in a form that could allow analysis and pictorial or graphical presentation. Conclusion(s): This study supported the feasibility of this and future versions of DELVE to present both a digital weaning chart and to facilitate visual and numerical data presentation. Future iterations of the system could include a user-friendly dashboard representing patient progress during the weaning process. Assimilation of large volumes of data could be used to enhance understanding and inform decision making around the prolonged wean.

Demonstration of Remote Control of Critical Care Ventilators and the Integrated Clinical Environment

Introduction: The COVID-19 pandemic posed numerous challenges to patient care, including extensive PPE use, patient care in isolation rooms, inadequate numbers of intensivists particularly in rural communities, use of unfamiliar ventilators that would be partially remedied by the ability to remotely control lung ventilation. The goals of the project were to study the intended use, risk management, usability, cybersecurity for remote control of ventilators and demonstrate the use of a single interface for several different ventilators. Method(s): Clinical scenarios were developed including remote control of the ventilator from an antechamber of an isolation room, nursing station within the same ICU, and remote control from across the country. A risk analysis and was performed and a risk management plan established using the AAMI Consensus Report--Emergency Use Guidance for Remote Control of Medical Devices. A cybersecurity plan is in progress. Testing was done at the MDPNP laboratory. We worked with Nihon Kohden OrangeMed NKV-550, Santa Ana, CA, and Thornhill Medical MOVES SLC, Toronto, Canada. Both companies modified their devices to allow remote control by and application operating on DocBox's Apiary platform. Apiary is a commercially available ICE solution, DocBox Inc, Waltham, MA. An expert panel was created to provide guidance on the design of a single common, simple to use graphical user interface (GUI) for both ventilators. Manufacturers' ventilation modes were mapped to ISO 19223 vocabulary, data was logged using ISO/IEEE 11073-10101 terminology using AAMI 2700-2-1, Medical Devices and Medical Systems - Essential safety and performance requirements for equipment comprising the patient-centric integrated clinical environment (ICE): Part 2-1: Requirements for forensic data logging. Result(s): We demonstrated that both ventilators can be controlled and monitored using common user interface within an institution and across the country. Pressure and flow waveforms were available for the NKV-550 ventilator, and usual ventilator measurements were displayed in near-real time. The interface allowed changing FiO2, ventilation mode, respiratory rate, tidal volume, inspiratory pressure, and alarm settings. At times, increased network latency negatively affected the transmission of waveforms. Conclusion(s): We were able to demonstrate remote control of 2 ventilators with a common user interface. Further work needs to be done on cybersecurity, effects of network perturbations, safety of ventilator remote control, usability implications of having a common UI for different devices needs to be investigated.

The impact of smartphone use on learning effectiveness: A case study of primary school students.

This study investigated the effects of smartphone use on the perceived academic performance of elementary school students. Following the derivation of four hypotheses from the literature, descriptive analysis, t testing, one-way analysis of variance (ANOVA), Pearson correlation analysis, and one-way multivariate ANOVA (MANOVA) were performed to characterize the relationship between smartphone behavior and academic performance with regard to learning effectiveness. All coefficients were positive and significant, supporting all four hypotheses. We also used structural equation modeling (SEM) to determine whether smartphone behavior is a mediator of academic performance. The MANOVA results revealed that the students in the high smartphone use group academically outperformed those in the low smartphone use group. The results indicate that smartphone use constitutes a potential inequality in learning opportunities among elementary school students. Finally, in a discussion of whether smartphone behavior is a mediator of academic performance, it is proved that smartphone behavior is the mediating variable impacting academic performance. Fewer smartphone access opportunities may adversely affect learning effectiveness and academic performance. Elementary school teachers must be aware of this issue, especially during the ongoing COVID-19 pandemic. The findings serve as a reference for policymakers and educators on how smartphone use in learning activities affects academic performance.

Design and Development of Toroidal Core Winding Machine with Digital Interface

The small size and inherent superior electrical characteristics of a toroid has made it the first choice for many Original Equipment Manufacturers (OEMs). However, the lack of knowledge regarding the toroidal coil winding equipment is still hampering the growth of toroid as the first choice for transformers, inductors and other electrical applications. Additionally, due to Covid-19 pandemic and lockdown situation, small scale companies are lacking skilled manpower for the high precision task of toroidal core winding and taping. Although the machine is readily available in the market, the cost is still very high. Toroidal core winding machine is an equipment used for the purpose of winding toroidal cores which is used in various electrical machines such as current transformers, power transformers, isolation transformers, inductors and chokes, auto transformers, etc. This project aims to develop a low-cost toroidal winding machine with a user-friendly digital interface for selection of winding parameters as per the user input. The winding machine developed in this project is efficient and reliable with high-speed performance and negligible error. © 2022 IEEE.

Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.© 2022 Copyright held by the owner/author(s).

From face-to-face to online: Teachers' perceived experiences in online distance teaching during the Covid-19 pandemic.

The urgent shift to online distance teaching and learning during the Covid-19 pandemic presented teachers with unique pedagogical, technological, and psychological challenges. The aim of this study was to map the main positive and negative experiences of teachers during this transition, as well as to examine intra- and interpersonal factors that affected teachers' ability to cope effectively with the challenges of online distance teaching. We used a mixed-method approach that combined qualitative (interviews) and quantitative (questionnaires) analyses. The interviews were analyzed using a grounded theory approach, specifically a bottom-up analysis, which led to the identification of five primary categories reflecting teachers' main concerns in online distance teaching (i.e., social, emotional, cognitive, pedagogical, and system support. The two most prominent categories were pedagogy and emotions, illustrating their centrality in teachers' experiences. A regression analysis of the questionnaires' data revealed that the two main variables which predicted both positive and negative experiences in online distance teaching were self-efficacy and teachers' attitudes towards technology integration in teaching. Findings of this study allow formulation of guidelines to promote factors related to positive experiences in online distance teaching.

Implementation of a clinical trial recruitment support system based on fast healthcare interoperability resources (FHIR) in a cardiology department

Background: Clinical Trial Recruitment Support Systems can booster patient inclusion of clinical trials by automatically analyzing eligibility criteria based on electronic health records. However, missing interoperability has hindered introduction of those systems on a broader scale. Purpose(s): Our aim was to develop a recruitment support system based on FHIR R4 and evaluate its usage and features in a cardiology department. Methods/Implementation: Clinical conditions, anamnesis, examinations, allergies, medication, laboratory data and echocardiography results were imported as FHIR resources. Trial study nurses and physicians were enabled to add new and edit trial information and input inclusion and exclusion criteria using a web-browser user interface in the hospital intranet. All information were recorded on the server side as the FHIR resources ResearchStudy and Group . Eligibility criteria linked by the logical operation OR were represented by using multiple FHIR Group resources for enrollment. On the client side, eligibility criteria were transformed to a tree-like structure (see Figure 1). Upon user demand, all hospitalized and ambulatory patients in the cardiology department were instantly screened for trial eligibility using the FHIR eligibility criteria on the existing patients' FHIR resources. Furthermore, study personal was able to manually edit trial status (i.e. ineligible, on-study, ..) of patients, which was implemented using the FHIR resource ResearchSubject . Result(s): This implementation of a CTRSS based on FHIR R4 was evaluated in clinical practice: Beginning from 1st April 2021 the application was used as an additional patient screening tool for the four trials CLOSUREAF, FAIR-HF2, SPRIRIT-HF and TORCH-PLUS of the German Centre for Cardiovascular Research. As the COVID-19 pandemic is prohibiting any proper comparison of patient inclusion rates, efficacy of the recruitment support system was tested by comparing the numbers of patients identified by the recruitment support system and enrolled in a trial to the actual number of enrolled patients irrespective of the screening method from 1st April 2021 to 23rd November 2021. The system was able to identify 52 of 55 patients included in those four clinical trials. Conclusion(s): Use of FHIR for defining eligibility criteria of clinical trials may facilitate interoperability and allow automatic screening for eligible patients at multiple sites of different healthcare providers in the future. Upcoming changes in FHIR should allow easier description of OR -linked eligibility criteria. (Figure Presented).

Development of Personalized Sleep Induction System based on Mental States

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7% and stop auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants. © 2023 IEEE.

A virtual research showcase and judging platform created from a patchwork of workplace applications.

Background: Despite the challenges the COVID-19 pandemic placed on libraries' existing workflows and operations, many librarians developed and debuted new services that addressed novel needs that emerged during the pandemic. This report describes how two electronic resource librarians at regional hospitals within a healthcare corporation used exhibition platforms to showcase resident research in an online format as a complement to in-person resident research programming. Case Presentation: Over the course of the pandemic, two exhibition platform variants were implemented, one year apart. This case report describes how each platform was developed. The first online event was conducted using a virtual exhibit platform to minimize in-person contact. The second online event, held the following year, blended a traditional live event with virtual elements using the online exhibit platform. To ensure completion of tasks, project management techniques were adopted throughout the event planning process. Conclusions: The pandemic created opportunities for hospitals to explore transforming meetings from primarily live and onsite into hybrid and fully virtual events. While many corporate hospitals have transitioned back to primarily in-person programming, newly adopted online practices such as online judging platforms and automation of continuing medical education tasks will likely remain. As in-person restrictions within healthcare settings are lifted or eased at uneven rates, organizations may continue to explore the value of in-person meetings versus the video conference experience of the same meeting.

Designing Hybrid Mechanical Ventilator System Based on Arduino and Raspberry Pi 4

Mechanical ventilators are advanced life-supporting machines in this century. The ventilator needs to be safe, flexible, and easy for competent clinicians to use. Since ventilators supply the patient with gas, they need pneumatic components to be present. First technology ventilators were typically powered by pneumatic energy. Gas pressure is used to power ventilators as well as ventilate patients. Nowadays, ventilators are operated electronically with the useful microprocessor tool. This proposal aims to design a simple portable mechanical ventilator that includes measuring some important physiological variables such as respiratory rate, heart rate, and O2 saturation, which can be utilized in hospital and at home. The proposed system includes Arduino, Raspberry pi4, touch screen, and graphical user interface. This study showed a significant individual performance for measuring some important parameters such as flow rate, tidal volume, and minute ventilation. The accuracy of measuring the flow rate was 72%. The Cohen's kappa (CK) was estimated to be 0.61. The accuracy of calculated the tidal volume was estimated at 83% with 0.80 CK. The accuracy of measuring the O2 saturation was estimated at 99% with 0.99 CK. The advantages of the proposed design are cost-effective, safe, flexible, and easy to use. Also, this system is smart and can control its transactions, so it can be used at home without the need for professional help. The operating parameters can also be set by the user with a simple user interface.Copyright © 2022 by ASME.

Social isolation & toxic behavior of students in e-learning: Evidence during the time of the covid-19 pandemic

The global COVID-19 pandemic has led educational institutions to shut down and adopt e-learning practices through computer-mediated communication. An unanticipated switch of online classes from face-to-face classes isolates students from social groups and teachers, causing online disinhibition. Therefore, this paper investigates factors influencing university students' toxic disinhibition behavior in online classrooms, WhatsApp groups, and Telegram groups. Also, social isolation has been used as a moderating variable to identify whether social isolation strengthens or weakens the proposed association. The research holds the basis of "Social Cognitive Theory" and "Theory of Planned Behavior." The data from 506 university students have been collected for analysis. The proposed framework and research hypotheses have been assessed via PLS-SEM using Smart PLS software. Findings from the study show that toxic behavior victimization, attitude, subjective norms, and behavioral control are factors that positively & significantly affect toxic disinhibition online. Furthermore, motives and self-efficacy showed an insignificant influence on toxic disinhibition. Additionally, toxic disinhibition significantly & positively affects toxic behavior. At last, social isolation is likely to have a moderation effect on the variables. Hence, the research yields guidance on reducing toxic disinhibition online. Further, implications and recommendations are discussed at the end of the study. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

A case report: Upper limb recovery from stroke related to SARS-CoV-2 infection during an intervention with a brain-computer interface.

COVID-19 may increase the risk of acute ischemic stroke that can cause a loss of upper limb function, even in patients with low risk factors. However, only individual cases have been reported assessing different degrees of hospitalization outcomes. Therefore, outpatient recovery profiles during rehabilitation interventions are needed to better understand neuroplasticity mechanisms required for upper limb motor recovery. Here, we report the progression of physiological and clinical outcomes during upper limb rehabilitation of a 41-year-old patient, without any stroke risk factors, which presented a stroke on the same day as being diagnosed with COVID-19. The patient, who presented hemiparesis with incomplete motor recovery after conventional treatment, participated in a clinical trial consisting of an experimental brain-computer interface (BCI) therapy focused on upper limb rehabilitation during the chronic stage of stroke. Clinical and physiological features were measured throughout the intervention, including the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), the Modified Ashworth Scale (MAS), corticospinal excitability using transcranial magnetic stimulation, cortical activity with electroencephalography, and upper limb strength. After the intervention, the patient gained 8 points and 24 points of FMA-UE and ARAT, respectively, along with a reduction of one point of MAS. In addition, grip and pinch strength doubled. Corticospinal excitability of the affected hemisphere increased while it decreased in the unaffected hemisphere. Moreover, cortical activity became more pronounced in the affected hemisphere during movement intention of the paralyzed hand. Recovery was higher compared to that reported in other BCI interventions in stroke and was due to a reengagement of the primary motor cortex of the affected hemisphere during hand motor control. This suggests that patients with stroke related to COVID-19 may benefit from a BCI intervention and highlights the possibility of a significant recovery in these patients, even in the chronic stage of stroke.

Implementing Adult Covid-19 Electronic Order Sets in a Children's Hospital

BACKGROUND AND AIM: An eight-bed adult COVID-19 critical care (CC) unit was established within our pediatric intensive care unit (PICU) when SARS-CoV-2 variants increased CC bed demand. Our objective was to rapidly roll out electronic order sets (OS) to facilitate computerized provider order entry (CPOE) for adult patients admitted within a children's hospital. METHOD(S): OS development began from the assessment of OS from seven adult CC units. Using a pre-existing PICU Admission template, we created two OS: adult COVID-19 admission and on-going care. We tested the prototypes in a multidisciplinary onsite-virtual hybrid tabletop simulation to evaluate usability within established workflows. Participants utilized role-specific profiles within the electronic medical record (EMR) training environment which paralleled their computer interface, permitting charting and documentation. EMR analysts were present to gather change requests. Following implementation, we performed twice daily hot debriefs with end-users to further identify issues. RESULT(S): 16 multidisciplinary bedside providers participated in simulation testing of the prototypes. Two safety issues were addressed before implementation. The electronic OS were developed, tested, and implemented within 8 days. The post-implementation hot debriefs identified one medication addition, and no deletions were necessary. CONCLUSION(S): Caring for adult COVID-19 patients within a freestanding children's hospital presents challenges and has the potential to introduce latent safety threats. Rapid development and implementation of electronic OS within 8 days to facilitate CPOE and reduce healthcare provider cognitive burden relied on leveraging functionality within the EMR system, performing iterative testing with a tabletop simulation, integration into previously established workflows, and gathering post-implementation feedback for continuous improvement.

A framework for Automated E-Message alert of Covid status using Robotic Process Automation

Artificial intelligence (AI)/digital employees, or metaphorical software robots (bots), are the foundation of the business process automation technology known as robotic process automation (RPA). The term "software robotics" has been used sometimes (not to be confused with robot software). Using internal application programming interfaces (APIs) or specialized scripting languages, a software developer creates a set of steps to automate a job and interface to the back-end system in conventional workflow automation technologies. RPA systems, on the other hand, create the action list by seeing the user carry out the job in the graphical user interface (GUI) of the programme, and then carry out the automation by repeating those actions directly in the GUI. In products that may not normally have APIs for this purpose, this can lessen the barrier to the usage of automation. Nowadays, monitoring every day covid status is not possible for an individual. So, the plan is to send an updated covid status through email automatically to the end users by using Robotic Process Automation (RPA). The main goal of this paper is to send corona information to the end users who are in need of covid details. For this, the end user wants to provide their email id and country name which they want to know about. The rest of the RPA process will be done by the bot using data scraping. Then email automation will be done to send email automatically. It is easy to check the required particular data from the cluster of data. It is easy to read and understand for all end users. Copyright © 2022 Ubiquity Press. All rights reserved.

PRESTOapp for health workers with mental health symptoms related to the COVID-19 pandemic

Introduction: The COVID-19 pandemic has caused a significant impact on the mental health of health workers that has brought many hospitals to launch immediate preventive mental health programs. Objective(s): (1) To adapt and enhance a smartphone app (PRESTOapp) for health workers with mental health symptoms related to the COVID-19, and (2) to demonstrate its potential effectiveness in significantly reducing anxiety-depressive and PTSD symptoms in this population. We aim to incorporate Natural Language Processing (NLP)-based techniques in a chatbot userinterface that will enable a more personalized and accurate monitoring and intervention. Method(s): An 18-months study with a 6-months preliminary phase to adapt PRESTOapp to health workers, enhance it with NLP-based techniques and chatbot user-interface, and evaluate its feasibility, and effectiveness in 12-months. Result(s): PRESTOapp has the potential to provide a prompt, personalized and integral response to the mental health demand due to the COVID-19. It will help by providing an innovative digital platform, that will allow remote monitoring of the symptoms course, provide brief psychotherapeutic interventions, and detect urgent situations. If the preliminary results of this study point to a potential effectiveness of the intervention, PRESTOapp may be easily adapted to the general population. Conclusion(s): PRESTOapp may be one of the key digital platforms that may help preventing and treating potentially severe mental health consequences. Considering the unresolved problem of burnout in health workers even before the COVID-19, this project will develop the necessary technology for implementing cost-effective mental health solutions, not only during the pandemic.

Brain-Computer Interface Approach for improving the Pedagogical Practices for Virtual Learning: A Conceptual Framework

The coronavirus epidemic (COVID19) has com-pelled the global halting of various services, including educational service, resulting in a massive crisis-response movement of education institutions to online learning platforms. Therefore, teachers had to shift from the traditional face-to-face modality and quickly adapt to virtual learning to continue their education. This conceptual paper discusses a theoretical framework for mon-itoring and improving the level of interaction between students and teachers during virtual learning environments. Through this interaction, teachers can gather some essential cognitive learning behaviors of their students by collecting some biomedical signals. In this conceptual framework, we propose a theoretical end-to-end approach to support teachers in understanding the cognitive learning behaviors of their students during online learning and where face-to-face contact is not possible. This shall be enabled by monitoring the brain patterns of students during their learning, using Brain-computer interface techniques to enhance their cognitive skills and maximize their learning. This approach is also expected to underpin new pedagogical methodologies to support remote learning. © 2022 IEEE.

Review on Signal Generation for Neurofeedback

Neurofeedback (NF) is a scientific method that alters the brain states to improve mental disorders. Neurofeedback can perform through Brain-Computer Interface (BCI) which involves hardware, and software to communicate with the outside environment using the brain's thoughts. Coronavirus disease (COVID-19) has shown a substantial influence on mental health symptoms because individuals are working from home (WFH). However, A brain condition known as Mental Fatigue (MF) is induced by continuous cognitive work and lowers mental attentiveness as well as negatively affects performance. There are different approaches to address different mental states and Neurofeedback strategies to change mental states. Thus, Neurofeedback can be an Intervention technique to reduce mental fatigue and improve cognitive task performance. Furthermore, it is proven by researchers that Machine Learning Technique can successfully detect Mental Fatigue through electroencephalography (EEG). Currently, there is no BCI that integrated Mental Fatigue detection and applies Neurofeedback strategies to reduce Mental Fatigue. This review identified a neurofeedback process that includes signal acquisition, signal pre-processing, feature extraction, classification and generation of feedback signals. This review aims to develop a general architecture of mental fatigue intervention through BCI. © 2022 IEEE.

Human Factors Validation of the Tablo Hemodialysis System With Health Care Practitioners

Background: Hemodialysis is a lifesaving treatment warranting extensive training to perform safely and effectively in different use environments. Shortages in nurse staffing due to the COVID 19 pandemic caused a desire to innovate systems that can be safely and effectively used by healthcare professionals (HCPs). The Tablo Hemodialysis System ("Tablo") is easy-to-learn, indicated for clinic, hospital, and home settings. Features include a simplified user interface, interactive touchscreen GUI coupled with videos to assist users. Prior usability testing of Tablo had a use error rate of 1.5%. Here we report on the results of simulated use human factors validation testing on recent software version of the Tablo Hemodialysis System ("Tablo") with HCPs in the clinic setting. Method(s): HCPs tested the Tablo in a simulated clinic environment to validate safety and usability. HCPs underwent training on all aspects of device operation;including setup, takedown, monitoring, routine maintenance, and alarm resolution. After a decay of at least one hour, HCPs performed tasks without the trainer. Task performance to use errors, close calls, and difficulties were recorded along with interview data. Result(s): Fifteen (15) HCPs were recruited, consisting of 9 RNs with prior HD experience and 6 dialysis technicians. A total of 7365 tasks were performed, with the use error rate across all tasks less than <1%, with most use errors related to Manual Blood Return. 100% of HCPs reported that they felt they could use Tablo safely and effectively. Summary of user task assessments shown in Figure 1. Conclusion(s): After standard 3-hour training, HCPs were able to safely and effectively operate Tablo in a simulated use clinic setting. HF testing of this more recent software shows further reduction in Tablo's already low use error rate. This supports prior data regarding the ability of HCPs to easily learn and use Tablo and the device's ability to facilitate expansion of available dialysis nursing staff while increasing the quality and safety of dialysis treatments across the care continuum. (Table Presented).

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.

Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

E-Health: Self-Care of Social Networks Users Concerning COVID-19

Aims: This research aimed to study the effect of virtual social networks on self-care of the users concerning COVID-19. Instrument & Methods: This survey research was conducted from April to June 2020. The study sample included social network users (WhatsApp, Instagram and Telegram) in Hormozgan province who were selected by convenience sampling. The measuring instrument was an online questionnaire extracted from Dorthea E. Orem’s self-care model, Gerbner's cultivation theory and Kaplan and Haenlein's media-richness-theory. Modeling was carried out using SPSS 28 and Amos 26 software. Findings: The results of explanation and modeling in the present research not only indicate a significant and direct relationship between the independent variables of Presence and interaction in the social networks and user orientation to the type of social network with the dependent variable of users concerning COVID-19 self-care (p<0.0009), but also, 45% of changes in COVID-19 self-care variable was covered by a set of social networking indices. Structural equation modelling in the self-care variable also showed that independent variables;Presence and interaction in the social networks and user orientation to the type of social network had the highest and lowest effects on the psychological support dimension with a standard coefficient of 0.99 and the dimension of awareness and attention to COVID-19 effects and outcomes with a standard coefficient of 0.95, respectively. Conclusions: As a result, we suggest that health officials and disease control and prevention centers use the potential of social networks such as WhatsApp and Instagram in self-care of users concerning COVID-19. © 2022, Tarbiat Modares University. All rights reserved.