scholarly journals The Challenge of Reorganizing Rehabilitation Services at the Time of COVID-19 Pandemic: A New Digital and Artificial Intelligence Platform to Support Team Work in Planning and Delivering Safe and High Quality Care

2021 ◽  
Vol 12 ◽  
Author(s):  
Alessia Saverino ◽  
Paola Baiardi ◽  
Giuseppe Galata ◽  
Gloria Pedemonte ◽  
Claudio Vassallo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Rating Scales ◽  
Functional Independence ◽  
Team Work ◽  
Rehabilitation Service ◽  
Rehabilitation Services ◽  
Staff Satisfaction ◽  
Bed Occupancy ◽  
System Usability Scale ◽  
The Impact

Introduction: The COVID-19 pandemic has posed great challenges in inpatient rehabilitation services, not only to implement the preventative measures to avoid the spreading of the virus in a highly interactive, multidisciplinary setting but also to create a rehabilitation pathway for post-COVID-19 patients. The aim of this retrospective study was to describe the role of a digital and artificial intelligence platform (DAIP) in facilitating the implementation of changes in a rehabilitation service during the COVID-19 pandemic.Materials and Methods: We gathered qualitative and quantitative descriptors of the DAIP, including measures to assess its efficiency in scheduling therapy sessions, and staff satisfaction using two simple numeric rating scales and the System Usability Scale. We describe how the volume of activity and the quality of care of our rehabilitation service have changed when the DAIP was implemented by comparing the pre-COVID-19 and the pandemic periods for patients' [sex, age, co-morbidities, diagnosis, and Functional Independence Measure (FIM) gain] and service's (bed occupancy, patients' length of stay, and staff capacity) characteristics.Results: Bed occupancy and the impact of rehabilitation on patients' outcome remained stable between the two periods. The DAIP provided a qualitative support for goal setting from remote; 95% of the planned sessions were delivered; the time for scheduling and registering sessions dropped by 50%. Staff satisfaction was about 70% for the easiness and 60% for the usefulness, and the mean “usability” score was close to the cut off for sufficient usability (mean score 65 where 68 is the cut off).Conclusion: By applying the DAIP to rehabilitation treatment, it was shown that the management of rehabilitation can be efficiently performed even in the COVID-19 pandemic. Staff satisfaction reflected a good acceptance of the changes considering the turbulent changes and the stress burden occurring at the time of the pandemic.

scholarly journals Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies (PERSIST): a multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Izidor Mlakar ◽  
Simon Lin ◽  
Ilona Aleksandraviča ◽  
Krista Arcimoviča ◽  
Jānis Eglītis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Cancer Survivors ◽  
Self Efficacy ◽  
Digital Tools ◽  
Care Plan ◽  
Survivorship Care Plan ◽  
Control Group ◽  
System Usability Scale ◽  
The Impact

Abstract Background It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL). Only few survivors are able to meet all of the recommendations regarding well-being and there is a body of evidence that cancer survivors’ needs often remain neglected from health policy and national cancer control plans. This increases the impact of inequalities in cancer care and adds a dangerous component to it. The inequalities affect the individual survivor, their career, along with their relatives and society as a whole. The current study will evaluate the impact of the use of big data analytics and artificial intelligence on the self-efficacy of participants following intervention supported by digital tools. The secondary endpoints include evaluation of the impact of patient trajectories (from retrospective data) and patient gathered health data on prediction and improved intervention against possible secondary disease or negative outcomes (e.g. late toxicities, fatal events). Methods/design The study is designed as a single-case experimental prospective study where each individual serves as its own control group with basal measurements obtained at the recruitment and subsequent measurements performed every 6 months during follow ups. The measurement will involve CASE-cancer, Patient Activation Measure and System Usability Scale. The study will involve 160 survivors (80 survivors of Breast Cancer and 80 survivors of Colorectal Cancer) from four countries, Belgium, Latvia, Slovenia, and Spain. The intervention will be implemented via a digital tool (mHealthApplication), collecting objective biomarkers (vital signs) and subjective biomarkers (PROs) with the support of a (embodied) conversational agent. Additionally, the Clinical Decision Support system (CDSS), including visualization of cohorts and trajectories will enable oncologists to personalize treatment for an efficient care plan and follow-up management. Discussion We expect that cancer survivors will significantly increase their self-efficacy following the personalized intervention supported by the m-HealthApplication compared to control measurements at recruitment. We expect to observe improvement in healthy habits, disease self-management and self-perceived QoL. Trial registration ISRCTN97617326. https://doi.org/10.1186/ISRCTN97617326. Original Registration Date: 26/03/2021.

Childbirth and Posttraumatic Stress Responses

2006 ◽  
Vol 22 (4) ◽  
pp. 259-267 ◽  
Author(s):  
Eelco Olde ◽  
Rolf J. Kleber ◽  
Onno van der Hart ◽  
Victor J.M. Pop
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Responses ◽  
Rating Scales ◽  
Rating Scale ◽  
Stress Disorder ◽  
Traumatic Experience ◽  
Event Scale ◽  
Self Rating ◽  
The Impact

Childbirth has been identified as a possible traumatic experience, leading to traumatic stress responses and even to the development of posttraumatic stress disorder (PTSD). The current study investigated the psychometric properties of the Dutch version of the Impact of Event Scale-Revised (IES-R) in a group of women who recently gave birth (N = 435). In addition, a comparison was made between the original IES and the IES-R. The scale showed high internal consistency (α = 0.88). Using confirmatory factor analysis no support was found for a three-factor structure of an intrusion, an avoidance, and a hyperarousal factor. Goodness of fit was only reasonable, even after fitting one intrusion item on the hyperarousal scale. The IES-R correlated significantly with scores on depression and anxiety self-rating scales, as well as with scores on a self-rating scale of posttraumatic stress disorder. Although the IES-R can be used for studying posttraumatic stress reactions in women who recently gave birth, the original IES proved to be a better instrument compared to the IES-R. It is concluded that adding the hyperarousal scale to the IES-R did not make the scale stronger.

The Ins and Outs of Teamworking: When University Teachers, in-Service Secondary Teachers and Pre-Service Teachers Collaborate to Transform Learning

2018 ◽  
Vol 5 (3) ◽  
pp. 185-193
Author(s):  
Dolors Masats ◽  
Paula Guerrero
Keyword(s):  
Secondary Students ◽  
Collaborative Action Research ◽  
Team Work ◽  
Professional Skills ◽  
Secondary English ◽  
Research And Innovation ◽  
Learning Success ◽  
Teachers Professional Development ◽  
Agent Of Change ◽  
The Impact

Abstract Initiatives for teachers’ professional development should rely on the epistemology of practice, that is, be founded on the premise that reflective teachers construct professional knowledge and develop professional skills through practice and through planning, observing or analysing practice. Reflection about teaching action and reflection in teaching action triggers innovation, especially when teachers work together to create the necessary conditions to transform learning. This paper advocates in favour of collaborative action research and innovation as a methodology to promote change in classroom practices. To illustrate this proposal, it presents a case study in which a secondary English teacher from a school which hosts adolescents at risk opens her classrooms to a researcher and a group of pre-service teachers with the objective to reflect upon her own practices and to become an agent of change. Our corpus is made of natural audio-recorded data from the discussions emerging during focus-group sessions held to evaluate the ongoing innovation and interviews to participating secondary students and trainee teachers. The analysis of those interactions will first lead us to reflect upon the challenges of promoting change in the classrooms. Then it will allow us to understand the impact of the experience and argue in favour of a model of teacher education based on team work as a tool to acquire professional skills and guarantee students’ learning success.

Trading faces: Complete AI face doubles avoid the uncanny valley

2020 ◽  
Author(s):  
Christopher Welker ◽  
David France ◽  
Alice Henty ◽  
Thalia Wheatley
Keyword(s):  
Artificial Intelligence ◽  
Sexual Orientation ◽  
Uncanny Valley ◽  
Torture Survivors ◽  
The Uncanny ◽  
The Creation ◽  
Full Face ◽  
The Impact

Advances in artificial intelligence (AI) enable the creation of videos in which a person appears to say or do things they did not. The impact of these so-called “deepfakes” hinges on their perceived realness. Here we tested different versions of deepfake faces for Welcome to Chechnya, a documentary that used face swaps to protect the privacy of Chechen torture survivors who were persecuted because of their sexual orientation. AI face swaps that replace an entire face with another were perceived as more human-like and less unsettling compared to partial face swaps that left the survivors’ original eyes unaltered. The full-face swap was deemed the least unsettling even in comparison to the original (unaltered) face. When rendered in full, AI face swaps can appear human and avoid aversive responses in the viewer associated with the uncanny valley.

Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: an Emerging Approach for Disease Detection and Treatment Optimization

2020 ◽  
Vol 28 ◽  
Author(s):  
Valeria Visco ◽  
Germano Junior Ferruzzi ◽  
Federico Nicastro ◽  
Nicola Virtuoso ◽  
Albino Carrizzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Disease Detection ◽  
Physician Patient Relationship ◽  
Invasive Approach ◽  
Tailored Treatment ◽  
The Impact

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

Artificial Intelligence and Inequality in the Middle East

2020 ◽  
pp. 624-649 ◽  
Author(s):  
Nagla Rizk
Keyword(s):  
Artificial Intelligence ◽  
Human Resources ◽  
Young Population ◽  
Mena Region ◽  
Poor Countries ◽  
Current State ◽  
Resource Poor ◽  
Youth Entrepreneurship ◽  
The Arab Spring ◽  
The Impact

This chapter looks at the challenges, opportunities, and tensions facing the equitable development of artificial intelligence (AI) in the MENA region in the aftermath of the Arab Spring. While diverse in their natural and human resource endowments, countries of the region share a commonality in the predominance of a youthful population amid complex political and economic contexts. Rampant unemployment—especially among a growing young population—together with informality, gender, and digital inequalities, will likely shape the impact of AI technologies, especially in the region’s labor-abundant resource-poor countries. The chapter then analyzes issues related to data, legislative environment, infrastructure, and human resources as key inputs to AI technologies which in their current state may exacerbate existing inequalities. Ultimately, the promise for AI technologies for inclusion and helping mitigate inequalities lies in harnessing grounds-up youth entrepreneurship and innovation initiatives driven by data and AI, with a few hopeful signs coming from national policies.

scholarly journals Assessing the Impact of Attendance Modality on the Learning Performance of a Course on Machines and Mechanisms Theory

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 558
Author(s):  
David Valiente ◽  
Héctor Campello-Vicente ◽  
Emilio Velasco-Sánchez ◽  
Fernando Rodríguez-Mas ◽  
Nuria Campillo-Davo
Keyword(s):  
Statistical Tests ◽  
Group Testing ◽  
Test Group ◽  
Historical Record ◽  
Team Work ◽  
University Education ◽  
Learning Performance ◽  
Control Group ◽  
Face To Face ◽  
The Impact

University education approaches related to the field of science, technology, engineering and mathematics (STEM), have generally particularized on teaching activity and learning programs which are commonly understood as reoriented lessons that fuse theoretic concepts interweaved with practical activities. In this context, team work has been widely acknowledged as a means to conduct practical and hands-on lessons, and has been revealed to be successful in the achievement of exercise resolution and design tasks. Besides this, methodologies sustained by ICT resources such as online or blended approaches, have also reported numerous benefits for students’ active learning. However, such benefits have to be fully validated within the particular teaching context, which may facilitate student achievement to a greater or lesser extent. In this work, we analyze the impact of attendance modalities on the learning performance of a STEM-related course on “Machines and Mechanisms Theory”, in which practical lessons are tackled through a team work approach. The validity of the results is reinforced by group testing and statistical tests with a sample of 128 participants. Students were arranged in a test group (online attendance) and in a control group (face-to-face attendance) to proceed with team work during the practical lessons. Thus, the efficacy of distance and in situ methodologies is compared. Moreover, additional variables have also been compared according to the historical record of the course, in regards to previous academic years. Finally, students’ insights about the collaborative side of this program, self-knowledge and satisfaction with the proposal have also been reported by a custom questionnaire. The results demonstrate greater performance and satisfaction amongst participants in the face-to-face modality. Such a modality is prooven to be statistically significant for the final achievement of students in detriment to online attendance.

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