Scalability of digital psychological innovations for refugees: A comparative analysis in Egypt, Germany, and Sweden

E-mental health interventions may offer innovative means to increase access to psychological support and improve the mental health of refugees. However, there is limited knowledge about how these innovations can be scaled up and integrated sustainably into routine services. This study examined the scalability of a digital psychological intervention called Step-by-Step (SbS) for refugees in Egypt, Germany, and Sweden. We conducted semi-structured interviews (n = 88) with Syrian refugees, and experts in SbS or mental health among refugees in the three countries. Data collection and analysis were guided by a system innovation perspective. Interviewees identified three contextual factors that influenced scalability of SbS in each country: increasing use of e-health, the COVID-19 pandemic, and political instability. Nine factors lay at the interface between the innovation and potential delivery systems, and these were categorised by culture (ways of thinking), structure (ways of organising), and practice (ways of doing). Factors related to culture included: perceived need and acceptability of the innovation. Acceptability was influenced by mental health stigma and awareness, digital trust, perceived novelty of self-help interventions, and attitudes towards non-specialist (e-helper) support. Factors related to structure included financing, regulations, accessibility, competencies of e-helpers, and quality control. Factors related to practice were barriers in the initial and continued engagement of end-users. Many actors with a potential stake in the integration of SbS across the three countries were identified, with nineteen stakeholders deemed most powerful. Several context-specific integration scenarios were developed, which need to be tested. We conclude that integrating novel e-mental health interventions for refugees into routine services will be a complex task due to the many interrelated factors and actors involved. Multi-stakeholder collaboration, including the involvement of end-users, will be essential.

Artificial Intelligence-based Speech Signal for COVID-19 Diagnostics

The speech signal has numerous features that represent the characteristics of a specific language and recognize emotions. It also contains information that can be used to identify the mental, psychological, and physical states of the speaker. Recently, the acoustic analysis of speech signals offers a practical, automated, and scalable method for medical diagnosis and monitoring symptoms of many diseases. In this paper, we explore the deep acoustic features from confirmed positive and negative cases of COVID-19 and compare the performance of the acoustic features and COVID-19 symptoms in terms of their ability to diagnose COVID-19. The proposed methodology consists of the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images to extract deep audio features. In addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 from acoustic features compared to COVID-19 symptoms, achieving an accuracy of 97%. The experimental results show that the proposed method remarkably improves the accuracy of COVID-19 detection over the handcrafted features used in previous studies. © 2022 ACM.

Advancing disease genomics beyond COVID-19 and reducing health disparities: what does the future hold for Africa?

The COVID-19 pandemic has ushered in high-throughput sequencing technology as an essential public health tool. Scaling up and operationalizing genomics in Africa is crucial as enhanced capacity for genome sequencing could address key health problems relevant to African populations. High-quality genomics research can be leveraged to improve diagnosis, understand the aetiology of unexplained illnesses, improve surveillance of infectious diseases and inform efficient control and therapeutic methods of known, rare and emerging infectious diseases. Achieving these within Africa requires strong commitment from stakeholders. A roadmap is needed to guide training of scientists, infrastructural development, research funding, international collaboration as well as promote public-private partnerships. Although the COVID-19 pandemic has significantly boosted genomics capacity in Africa, the continent still lags other regions. Here, we highlighted key initiatives in genomics research and efforts to address health challenges facing the diverse and fast-growing populations on the continent. We explore the scalability of genomic tools and techniques to tackle a broader range of infectious diseases in Africa, a continent that desperately requires a boost from genomic science.

Patient-centric soulbound NFT framework for electronic health record (EHR)

Interest in leveraging blockchain technology to boost healthcare and e-health solutions has lately increased. Blockchain has proven to have enormous promise in a range of e-health industries because of its decentralized and reliable nature, including the secure exchange of electronic health records (EHRs) and database access management among numerous medical entities. A unique paradigm known as the "patient-centric approach” places the patient at the center of the healthcare system and gives them complete control over who has access to and can share their personal health information. Strong confidentiality and safety requirements are necessary for health information. Additionally, other concerns must be resolved, such as secrecy, interoperability, scalability, cost-effectiveness, and timeliness. This paper offers a patient-centric privacy-preserving framework for an efficient and safe medical record to address these problems. Based on three parameters transaction cost, execution time, and gas cost. Three blockchain platforms are compared by using the smart contract to find out the suitable platform for the implementation of this framework. Blockchain platforms served as a benchmark for the performance assessment of a designed framework. Although blockchain will not fix every issue in healthcare organizations, it will undoubtedly assist in dramatically reducing some of the most critical ones.

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs

The medical conversational system can relieve doctors' burden and improve healthcare effi-ciency, especially during the COVID-19 pan-demic. However, the existing medical dialogue systems have die problems of weak scalability, insufficient knowledge, and poor controlla-bility. Thus, we propose a medical conversa-tional question-answering (CQA) system based on the knowledge graph, namely MedConQA, which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures, including medi-cal triage, consultation, image-text drug recom-mendation, and record. Each module has been open-sourced as a tool, which can be used alone or in combination, with robust scalability. Besides, to conduct knowledge-grounded dia-logues with users, we first construct a Chinese Medical Knowledge Graph (CMKG) and col-lect a large-scale Chinese Medical CQA (CM-CQA) dataset, and we design a series of meth-ods for reasoning more intellectually. Finally, we use several state-of-the-art (SOTA) tech-niques to keep the final generated response more controllable, which is further assured by hospital and professional evaluations. We have open-sourced related code, datasets, web pages, and tools, hoping to advance future research. © 2022 Association for Computational Linguistics.

Learning and Performance Science for Digital Transformation

Select any industry and you will find scores of articles detailing how the SARS-CoV-2 pandemic fostered an explosion of online learning over the past two years. Within social distancing guidelines, organizations were able to meet many challenges of the learning and performance space. At the same time, training budgets have shrunk to offset pandemic headwinds as organizations rally to survive. Focus has shifted instead toward digital transformation of big data, to unify, manage, and visualize the flow of structured business data and, thus, improve overall efficiency and outcomes. While impressive innovations have emerged to focus learning and performance (L&P) on what is most pertinent, L&P content science lags woefully behind data science. Scalability of L&P content remains largely elusive. This paper outlines a vision for an L&P science to foster a cogent digital transformation on par with modern data science. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Lightweight Smart Camera Solution for Covid-19

With the growth of IoT devices and edge computing technologies, a challenge is to deal with the network capacity and response latency in real-time applications. Such an approach to the solutions is to deploy the Artificial Intelligence (AI) and Internet of Thing (IoT) applications, namely AIoT and Internet of Things (IoT) applications at the edge. In this paper, we introduce a smart AIoT solution to support the control of covid-19 epidemic with two main features: social distance application to control social distance violators in different areas and facemask detection to identify violators who didn't wear a mask. The proposed design architecture comprises three layers: the first one is the Centralized Cloud, which implements the backend, a web server connects to a secure database. The web dashboard helps the administrators manage the streams of surveillance cameras, visualize the number of social distance violators in different areas, and show recorded facemask violators in table for convenient;the second layer is the Edge, social distance and facemask services are packaged into Docker container and deployed in a lightweight Kubernetes (K3s) cluster which have GPU. The Cluster should be deployed on both Jetson Nano and Raspberry Pi 3 devices. This design intends to increase high availability, scalability, self-healing, resource utilization, stability, and automation deployment for the edge services;and the final layer - the End Devices, which includes multiple cameras connected directly to the AI services at Edge. These cameras help us collect and send data to the edge servers for analyzing purpose. The results show that the proposed system works correctly, edge services run at an acceptable framerate. © 2022 IEEE.

Federated Learning for Smart Healthcare: A Survey

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare. [ FROM AUTHOR]

Scalable Management Architecture for Electronic Health Records Based on Blockchain

The rise of communication and information technology in conjunction with the health emergency due to COVID-19 has accelerated the implementation of electronic health records (EHR). These registries are constantly growing and put people's privacy, information security and health data at risk. The use of the blockchain is shown as an alternative for the distribution and management of health data. However, the blockchain has limitations of scalability and interoperability. This work proposes a scalable management architecture based on blockchain that will be analyzed by comparing it with other works reviewed in the document. The model proposed in three modules aims to deliver an alternative to EHR's scalable management solution based on the private blockchain such as Hyperledger Fabric. © 2022 IEEE.

Learning and Performance Science for Digital Transformation

Select any industry and you will find scores of articles detailing how the SARS-CoV-2 pandemic fostered an explosion of online learning over the past two years. Within social distancing guidelines, organizations were able to meet many challenges of the learning and performance space. At the same time, training budgets have shrunk to offset pandemic headwinds as organizations rally to survive. Focus has shifted instead toward digital transformation of big data, to unify, manage, and visualize the flow of structured business data and, thus, improve overall efficiency and outcomes. While impressive innovations have emerged to focus learning and performance (L&P) on what is most pertinent, L&P content science lags woefully behind data science. Scalability of L&P content remains largely elusive. This paper outlines a vision for an L&P science to foster a cogent digital transformation on par with modern data science. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data.

BACKGROUND: Genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) involving 1 million GWAS samples from dozens of population-based biobanks present a considerable computational challenge and are carried out by large scientific groups under great expenditure of time and personnel. Automating these processes requires highly efficient and scalable methods and software, but so far there is no workflow solution to easily process 1 million GWAS samples. RESULTS: Here we present BIGwas, a portable, fully automated quality control and association testing pipeline for large-scale binary and quantitative trait GWAS data provided by biobank resources. By using Nextflow workflow and Singularity software container technology, BIGwas performs resource-efficient and reproducible analyses on a local computer or any high-performance compute (HPC) system with just 1 command, with no need to manually install a software execution environment or various software packages. For a single-command GWAS analysis with 974,818 individuals and 92 million genetic markers, BIGwas takes ∼16 days on a small HPC system with only 7 compute nodes to perform a complete GWAS QC and association analysis protocol. Our dynamic parallelization approach enables shorter runtimes for large HPCs. CONCLUSIONS: Researchers without extensive bioinformatics knowledge and with few computer resources can use BIGwas to perform multi-cohort GWAS with 1 million GWAS samples and, if desired, use it to build their own (genome-wide) PheWAS resource. BIGwas is freely available for download from http://github.com/ikmb/gwas-qc and http://github.com/ikmb/gwas-assoc.

Empirical evaluation of machine learning models for analysis of CoVID related diseases on different body organs

Corona Virus (or COVID19) has shown long-term effects on different human body organs, which include lung diseases, kidney malfunctions, heart dysrhythmia, changes in brain nutrient levels, psychological issues, abrupt changes in blood pressure, etc. Due to such a wide variation in the effects on different body parts, it is difficult for researchers to design models that can integrate these effects for treatment recommendations, and future disease prevention scenarios. Thus, this text reviews some of the recently proposed models that efficiently identify effects of COVID19 on different body organs. This review discusses the underlying models in terms of their clinical nuances, functional advantages, contextual limitations, and empirical future scopes. Based on this discussion, researchers will be able to identify optimal models for the identification of different diseases on individual body parts. It was observed that hybrid bio-inspired models, when combined with deep learning-based classification techniques, can efficiently identify these effects. This text also parametrically evaluates these models in terms of their accuracy, precision, classification delay, deployment cost, and scalability parameters, which will allow readers to identify optimal models for their performance specific use cases. To further contemplate this discussion, a novel COVID19 Classification Rank Metric (CCRM), which combines these parameters for comprehensive identification of optimal models is evaluated in this text. Based on this metric, researchers will be able to identify optimal models that can be deployed with high-accuracy, low delay, and high-scalability, along with lower cost for clinical scenarios. Copyright © 2022, Anka Publishers. All rights reserved.

Remote mental health care interventions during the COVID-19 pandemic: An umbrella review.

Mitigating the COVID-19 related disruptions in mental health care services is crucial in a time of increased mental health disorders. Numerous reviews have been conducted on the process of implementing technology-based mental health care during the pandemic. The research question of this umbrella review was to examine what the impact of COVID-19 was on access and delivery of mental health services and how mental health services have changed during the pandemic. A systematic search for systematic reviews and meta-analyses was conducted up to August 12, 2022, and 38 systematic reviews were identified. Main disruptions during COVID-19 were reduced access to outpatient mental health care and reduced admissions and earlier discharge from inpatient care. In response, synchronous telemental health tools such as videoconferencing were used to provide remote care similar to pre-COVID care, and to a lesser extent asynchronous virtual mental health tools such as apps. Implementation of synchronous tools were facilitated by time-efficiency and flexibility during the pandemic but there was a lack of accessibility for specific vulnerable populations. Main barriers among practitioners and patients to use digital mental health tools were poor technological literacy, particularly when preexisting inequalities existed, and beliefs about reduced therapeutic alliance particularly in case of severe mental disorders. Absence of organizational support for technological implementation of digital mental health interventions due to inadequate IT infrastructure, lack of funding, as well as lack of privacy and safety, challenged implementation during COVID-19. Reviews were of low to moderate quality, covered heterogeneously designed primary studies and lacked findings of implementation in low- and middle-income countries. These gaps in the evidence were particularly prevalent in studies conducted early in the pandemic. This umbrella review shows that during the COVID-19 pandemic, practitioners and mental health care institutions mainly used synchronous telemental health tools, and to a lesser degree asynchronous tools to enable continued access to mental health care for patients. Numerous barriers to these tools were identified, and call for further improvements. In addition, more high quality research into comparative effectiveness and working mechanisms may improve scalability of mental health care in general and in future infectious disease outbreaks.

EdTech: Why the project-based approach must change in order to contribute to system resilience.

This viewpoint article argues that there is an urgent need to reform the project-based EdTech approach in order to allow EdTech to contribute to the resilience of education systems in the aftermath of Covid-19. Looking at the contrast between the multiplication of EdTech pilot projects presented as a necessary step in a process that will eventually lead to scaled solutions and the lack of solutions that actually scale, the article highlights those long-standing issues perceived as most pressing by the actors involved in project-based EdTech initiatives. Their perspective and statements allow one to grasp how the EdTech project approach favors the setup of EdTech projects that are by design unscalable, driven by a utopian perception of scalability and instrumentalized in the name of a goal that is de facto only a branding. As a result, and despite the mobilization of tremendous resources, the EdTech project-based approach cannot be system-transformative.

Scalability of an ACT-Based strategy for improving well-being in health care providers: A mix-method and preliminary evaluation of efficacy.

The burden of the COVID-19 pandemic has been mainly carried by health care providers. Technology-Mediated Interventions (TMI) seem to be a feasible alternative to increase access to behavioral health resources in this population. However, scaling-up treatments into TMI requires developing user-friendly, accepted, and accessible formats. A two-stage study was conducted to assess scalability of an Acceptance and Commitment Therapy (ACT) based strategy (named FACE COVID) delivered using technology. First, a mix-method design connected qualitative and quantitative data from health providers and ACT experts by which changes were performed to enhance scalability. Second, a pretest-posttest study was conducted to preliminary evaluate the efficacy of FACE COVID intervention on well-being, psychological distress, and psychological flexibility. Results showed a positive impact on well-being, but not distress and psychological flexibility. While this intervention has promising results, changes in dose intensity, social support, and mental health literacy could improve retention as well as increase opportunities to target distress and psychological flexibility in future studies.

A Hybrid Recommender System for Improving Rating Prediction of Movie Recommendation

Because of COVID-19 pandemic, online movies are now extremely popular. While the movie theaters have not serviced and people are staying quarantine, movies are the best choice for relaxing and treating stress. In present, recommender systems are widely integrated into many platforms of movie applications. A hybrid recommender system is one promising technique to improve the system performance, especially for cold-start, data sparsity, and scalability. This paper proposed a hybrid of matrix factorization, biased matrix factorization, and factor wise matrix factorization to solve all mentioned drawback problems. Simulation shows that the proposed hybrid algorithm can decrease approximately 11.91% and 10.70% for RMSE and MAE, respectively, when compared with the traditional methods. In addition, the proposed algorithm is capable of scalability. While the number of datasets is tremendously increased by 10 times, it is still effectively executed. © 2022 IEEE.

Sharded Blockchain-based Online Diagnostic System for Suspected Patients During Pandemics

During pandemics, diagnostic tests are essential to provide quick treatment of patients and limit the disease spread. The high demand for testing resources can stress the healthcare system. Thus, a remote collection of symptoms and reporting the results via an automated diagnostic system is highly desirable. However, such a system is challenged by privacy and scalability issues. Hence, we propose a sharded blockchain-based system that (a) introduces a set of shards that distributes the testing load among a group of local nodes (LNs), hence, offering high scalability for country-wide adoption, (b) uses ring signatures and unique random identifiers to ensure the anonymity of the users and the unlinkability of test requests, hence, supporting privacy-preservation, (c) deploys a detection strategy at the LNs based on deep neural networks, which is implemented on smart contracts, hence, enabling autonomous diagnosis, and (d) provides healthcare entities with authorized access to the symptoms and test results, hence, enabling efficient data sharing that supports future research. We provide an implementation of the proposed system and our experimental results demonstrate the high scalability and privacy of the system while achieving a testing accuracy up to 90%. We present a case study for U.S. wide deployment showing that a total daily test request of 2, 407, 462 can be performed and reported in 11 minutes compared to 63 days in absence of sharding. Moreover, sharding decreased the user storage requirement to be 0.18 MB at maximum instead of 723 MB without sharding.

Society 5.0 as Digital Strategy for Scalability: Tamba’s COVID-19 Vaccination Management System and Its Expansion

Society 5.0, Japan’s innovation policy, aims to build a human-centered society with information technologies. However, it is not easy to satisfy human-centered design and country wide or global scalability. In this paper, we discuss ways to realize a regional digital strategy of enhancing the services provided by utilizing data provided by various stakeholders outside of the region with case analysis of COVID-19 vaccine management system in Tamba city with lens of the adaptive integrated digital architecture framework (AIDAF). Society 5.0 Reference Architecture and related specifications help municipalities to enhance their digital strategy to comply with global environment. It is especially important to realize alignment in technology architecture of trust framework and data architecture. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Library Publishing Programs at Capacity: Addressing Issues of Sustainability and Scalability

Introduction: This article discusses the changes to overall goals, direction, and services that were made to two library publishing programs at Pacific University and the University of South Florida when they were no longer able to grow their programs due to an inability to hire additional staff and COVID-19-instigated staff reassignments. Description of Programs: Pacific University’s publishing program grew out of its institutional repository and, at its peak, published seven open access journals. In addition, Pacific University Libraries founded a University Press in 2016, which has published six books as of 2021. The University of South Florida’s publishing program began publishing open access journals in 2008, and it has grown to include over 20 journals. Lessons Learned: Both the Pacific University and the University of South Florida publishing programs have faced scalability and sustainability issues, which were further exacerbated by COVID-19. The focus of our library publishing programs, as well as many others, has been on continual growth, which is not sustainable without the ability to hire additional staff or allocate staff time differently. We argue that standardizing services as well as creating a business plan can help ensure that publishing programs are sustainable and scalable. Next Steps: We hope to begin a conversation among library publishers about acknowledging limits and creating achievable definitions of success outside of continual growth.

A Fog and Blockchain Software Architecture for a Global Scale Vaccination Strategy

Nowadays, there are many fragmented records of patient’s health data in different locations like hospitals, clinics, and organizations all around the world. With the arrival of the COVID-19 pandemic, several governments and institutions struggled to have satisfactory, fast, and accurate decision-making in a wide, dispersed, and global environment. In the current literature, we found that the most common related challenges include delay (network latency), software scalability, health data privacy, and global patient identification. We propose to design, implement and evaluate a healthcare software architecture focused on a global vaccination strategy, considering healthcare privacy issues, latency mitigation, support of scalability, and the use of a global identification. We have designed and implemented a prototype of a healthcare software called Fog-Care, evaluating performance metrics like latency, throughput and send rate of a hypothetical scenario where a global integrated vaccination campaign is adopted in wide dispensed locations (Brazil, USA, and United Kingdom), with an approach based on blockchain, unique identity, and fog computing technologies. The evaluation results demonstrate that the minimum latency spends less than 1 second to run, and the average of this metric grows in a linear progression, showing that a decentralized infrastructure integrating blockchain, global unique identification, and fog computing are feasible to make a scalable solution for a global vaccination campaign within other hospitals, clinics, and research institutions around the world and its data-sharing issues of privacy, and identification. Author