ABSTRACT
Messaging platforms like WhatsApp are some of the largest contributors to the spread of Covid-19 health misinformation but they also play a critical role in disseminating credible information and reaching populations at scale. This study explores the relationships between verification behaviours and intention to share information to users that report high trust in their personal network and users that report high trust in authoritative sources. The study was conducted as a survey delivered through WhatsApp to users of the WHO HealthAlert chatbot service. An adapted theoretical model from news verification behaviours was used to determine the correlation between the constructs. Due to an excellent response, 5477 usable responses were obtained, so the adapted research model could be tested by means of a Structural Equation Model (SEM) using the partial least squares algorithm on SmartPLS4. The findings suggest significant correlations between the constructs and suggest that participants that have reported high levels of trust in authoritative sources are less likely to share information due to their increased behaviours to verify information. © 2023 IEEE.
ABSTRACT
In the Philippines, a barangay is the smallest administrative unit serving as suburban neighborhoods' first line of defense. According to Bautista, barangays conduct a manual file-based process of storing the community's health information. Therefore, the need for a single platform enables a small government unit to manage its resources while being transparent to its community. The study aims to develop a web- based barangay health information system portal for Barangay 69 District 1 in Tondo Manila. The system would be a reference tool for barangays as their platform provides inventory management, the barangay's health programs, and a dashboard for data visualization inventory management, tracking of Covid cases, administration of health activities, and a dashboard for data visualization. As a result, the web portal is functional, and different test scenarios show above-average results. The study concludes that the system provided a platform for the barangay and its residents. It also concludes that it is user-friendly and efficiently disseminates the barangay's health programs and activities. © 2023 IEEE.
ABSTRACT
Open source Geographic Information System (GIS) have been fostering spatial data research such as Earth observation and environmental monitoring for more than 30 years. More recently, globally available geospatial information combined with web technologies are providing new environments and tools for data handling. Thus, binding the mapping and processing capabilities of traditional GIS to the accessibility and reliability of web-based data providers can bring new opportunities for research. In this paper, we built a QGIS plugin to explore the integration of different public data providers in Brazil along with field data produced by the BONDS project. The biOdiversity conservatioN with Development in Amazon wetlandS project (BONDS) proposes to develop biodiversity scenarios for the Amazonian floodplains aiming to support solutions to preserve biodiversity and ecosystem services. The use of web services enabled dynamic and fast access to several products ranging from remote sensing images, land use and land cover, territorial cartography, water quality, to COVID-19 health data, and more. © 2022 National Institute for Space Research, INPE. All rights reserved.
ABSTRACT
Social media has changed the way individuals and institutions approach healthcare and health information and offers opportunities to understand health-related interactions at all levels, from the micro to the macro. The Social Media and Healthcare Technology mini-track presents research papers that address a diverse array of social media and associated technology within healthcare and healthcare research;including macro analytics, text and data mining and the role of social media platforms and influencers in health care and health-related decision making. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient's utterance. To this end, we create a high-quality annotated Chinese Medical query (utterance) dataset, CMedQ (about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model Conco-ERNIE, using concept co-occurrence patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on CMedQ demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot, MedicalBot. To provide knowledge support for MedicalBot, we also build a Chinese medical graph, CMedKG (about 45k entities and 283k relationships). © 2023 Association for Computing Machinery.
ABSTRACT
During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health planning, and misinformation mitigation. However, creating such an information portal is challenging because 1) domain expertise is required to identify and curate credible and intelligible content, 2) the information needs to be updated promptly in response to the fast-changing environment, and 3) the information should be easily accessible by the general public;which is particularly difficult when most people do not have the domain expertise about the crisis. In this paper, we presented an expert-sourcing framework and created Jennifer, an AI chatbot, which serves as a credible and easy-to-access information portal for individuals during the COVID-19 pandemic. Jennifer was created by a team of over 150 scientists and health professionals around the world, deployed in the real world and answered thousands of user questions about COVID-19. We evaluated Jennifer from two key stakeholders' perspectives, expert volunteers and information seekers. We first interviewed experts who contributed to the collaborative creation of Jennifer to learn about the challenges in the process and opportunities for future improvement. We then conducted an online experiment that examined Jennifer's effectiveness in supporting information seekers in locating COVID-19 information and gaining their trust. We share the key lessons learned and discuss design implications for building expert-sourced and AI-powered information portals, along with the risks and opportunities of misinformation mitigation and beyond. © 2023 Owner/Author.
ABSTRACT
The scope and severity of the COVID-19 pandemic has introduced new challenges for people seeking health information online. To understand how an existing online community of people living with a chronic health condition navigate meeting their existing health goals alongside the challenges and tensions resulting from COVID-19, we performed a qualitative content analysis of six weeks of discussion in the r/infertility online community. We found that community members collaborated and debated to make dynamic structural and normative changes to their community in accordance with the changing impacts of COVID-19 on their experiences. Additionally, we found that community members information-seeking goals were centered around timelines for their own treatment plans and goals, with the scope of these timelines shifting based on their current state of knowledge of COVID-19. Implications of these findings for supporting health online communities broadly are discussed. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
During the COVID-19 pandemic, different groups had different perceptions of how dangerous the coronavirus was. This difference in behavior was intensified by the large amount of misinformation shared across social media. This work presents an analysis aimed at understanding the extent to which people perceived risk at different levels, and at uncovering the relationship between these differences and the spread of misinformation. In particular, we focus on Brazil, because it is well-known that its Ministry of Health has sponsored campaigns that raised suspicious regarding the effectiveness of the vaccines. To achieve this goal, we gathered tweets written in Portuguese related to the COVID-19 and analyzed their psycholinguistic traits. Among those traits, we found 'Anxiety' to be a good proxy for risk perception. We validate this choice by showing that, at moments of high (resp. low) infection rates in the world, the Anxiety score was higher (resp. lower). We grouped users into 'low' and 'high' risk perception based on the users' anxiety score, and analyzed the relation of each group with the spread of misinformation. Our results show that Twitter users with a lower perceived risk were more inclined to share fake news and harmful information, while the group with a higher level of anxiety tends to share more scientifically-backed information. This is an important step towards helping minimize the spread of false and harmful health information around the internet. © 2022 IEEE.
ABSTRACT
This preliminary study revisits a fundamental information problem of information behavior, focusing on needs, overload, and information source use, in the context of the COVID-19 pandemic. The associations between the impact of information source use on the extent of information needs, being exposed to information, and feeling of overload was examined. Furthermore, to understand the impact of context on information behavior, the differences in the degree of information resource use, needs, exposure, and overload between the two groups with different levels of health were investigated. 85th Annual Meeting of the Association for Information Science & Technology ;Oct. 29 – Nov. 1, 2022 ;Pittsburgh, PA. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.
ABSTRACT
Everyone understands the necessity of health management, especially in light of the COVID-19 viral infection. How to care for and manage health has emerged as the main topic of conversation, whether it concerns the elderly, adults, patients, or children. There are numerous ways to maintain one's health, and smartwatches are good at doing this because their owners can monitor their health constantly. The idea behind a smartwatch is to utilise its green light to measure the wearer's blood pressure before gathering information about their health. Because smartwatches can constantly detect and analyse users' daily health information. Users or guardians can use this information to take care of their bodies;therefore, they are an excellent choice for many people with dementia, depression, high-stress conditions, and athletes who need to monitor their physical fitness. This article analyses in depth the value of smartwatches, their applications for managing people's health, and their benefits and drawbacks. © 2023 IEEE.
ABSTRACT
Covid-19 Epidemic has significantly changed how hypospadias patients are delivered to healthcare services, particularly after hypospadias repairs (postoperative care). Some studies reported that using telemedicine schemes by sending digital documentation such as images and videos through cell phones can facilitate an assessment of postoperative monitoring of hypospadias patients. However, this approach raises various concerns, such as managing digital documentation of hypospadias patients, analyzing the data, and the security of individuals' health information. This study proposes a design of cloud-based architecture for early detection and postoperative monitoring of hypospadias patients to address the concerns above. The user acceptance test shows that most users agree that this application may be used for early detection, monitoring hypospadias patients, and helping capture videos and provide labeling to patients' data. © 2022 IEEE.
ABSTRACT
The development of cloud technology is a fundamental idea for offering unfettered access to many different sources in the planning of the networking, memory, infrastructure, and software. Computers are becoming more and more widespread across a wide range of industries due to their numerous advantages, notably in the healthcare industry. Typically, it is essential to the interchange of health information. In light of the ongoing issues with password security, sending private medical information via the internet still raises serious privacy concerns. Whether or whether they have complete permission, patients are not forced to divulge any of their private or personal information. This article examines several noteworthy recent studies that address the problems of password security and data privacy for cloud-based health services. These compare the benefits and drawbacks of different physical access preservation techniques. The paper also proposes a combined authentication procedure based on RFDE models. Cloud security is usually greatly hampered by the necessity for information privacy in an effort to protect sensitive and non-sensitive data for decision-making and to solve the problem of information leakage. One of the most challenging parts of the transfer of personal health records (PHRs) to the cloud is the reuse and exchange of accurate, complete medical evidence. When PHRs are outsourced to third-party businesses, such as cloud services, they are often used as patient-centered, private ways of exchanging health information. Data about a particular PHR doctor is coded for protection before being sent to the cloud. However, there are still substantial barriers due to issues with security, things that can be improved, lawful consumer privacy portfolio management, efficiency, and regulation over sensitive and non-sensitive data kept in the cloud. The PHR file may be encrypted using the Rail Fence Data Encryption (RFDE) technique to provide strong confidentiality rules and enable PHR and modular connectivity control to perform at their very best. Unauthorized users are managed to stop from accessing information with the aid of the transposition cypher, also used by RFDE and known as 'zigzag encryption.' The recommended technique generates the secret key while encrypting the PHR information. The recipient decrypts the PHR data using the private key. The algorithm works brilliantly in comparison to the prior strategy. © 2022 IEEE.
ABSTRACT
The health risks of socially vulnerable groups, such as the elderly, the sick, and the disabled, are significantly elevated under the COVID-19 epidemic. Therefore, the different factors affecting the use of information technology by socially vulnerable groups under COVID-19 are explored at the level of the use of emerging information technology. The impact on the information behavior of socially vulnerable groups under COVID-19 is also explored at the level of information behavior, including health information needs, the digital divide phenomenon, and the utilization of public information services. Based on the above findings, the current status of information behavior research for socially vulnerable groups is combined. Future research directions of information technology and information behavior for socially vulnerable groups are proposed. First, to improve the research theory of information behavior of socially vulnerable groups regarding information technology. Second, to apply big data technology and data analysis technology to explore the information technology adoption behavior of socially vulnerable groups in-depth. Third, to construct the information behavior model of socially vulnerable groups based on empirical research cases. Fourth, to use information technology for socially vulnerable groups according to information technology and the barriers faced by socially vulnerable groups in using information technology, and to provide strategies for using information technology that meet the needs of socially vulnerable groups. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
COVID-19 has become a global pandemic, and COVID-19 patients are in a medical dilemma with no effective treatment and no effective drugs. The questions and answers in the social Q&A community can reveal the characteristics and evolution rules of the health information needs of COVID-19 patients. Using the Q&A data in Baidu Zhidao (https://zhidao.baidu.com/ ) as the research object, using the web crawlers to capture the data, automatic topic recognition on the acquired data by constructing an LDA topic model, exploring the content of COVID-19 patients' health information needs, and revealing the change rule of Q&A publication volume and health information need topics from the time dimension. Combining statistical information such as the number of answers, the number of likes, and the level of respondents, cluster analysis is used to reveal the changing rules of social characteristics and health information need topics. By analyzing the Q&A data on COVID-19 patients in Baidu Zhidao, it is found that the topic distribution of health information needs topic is relatively concentrated. Moreover, the number of Q&A and the types of health information needs to be changed in different development periods. There are differences in social characteristics that correspond to different topics of health information needs. Through in-depth analysis of the characteristics of health information needs of COVID-19 patients in the social Q&A community, on the one hand, it is beneficial for COVID-19 patients to obtain the required health information content timely. On the other hand, it is beneficial to optimize the community information display mechanism and improve the organization of information resources. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The coronavirus disease pandemic which broke out in 2019 heavily affected world wide health systems which found themselves unprepared for managing this wide-ranging event and were therefore forced to reorganize and review the entire system management. In this scenario, the use of information technology in health has played a fundamental role;in fact, e-Health, which indicates all the information and communication technologies (ICT) supporting the health system, has allowed not only the continuation of activities otherwise not executable, but has also left a strong legacy both to the health system in terms of improvements in efficiency and effectiveness and towards the health of the citizen. Covid-19 has pushed towards a rapid growth of e-Health by modifying the operation of the health system, deeply changing the vision of management processes, and favoring data digitization from which meaningful information and insights can be extracted. The aim of this article is to analyze the impact of Covid on the acceleration of the digital transformation of the healthcare system, the innovative processes, the tools used and the best adapted Big Data techniques. © 2022 IEEE.
ABSTRACT
Privacy concerns around sharing personal health information are frequently cited as hindering COVID-19 contact tracing app adoption. We conducted a nationally representative survey of 304 adults in the United States to investigate their attitudes towards sharing two types of COVID-19 health status (COVID-19 Diagnosis, Exposure to COVID-19) with three different audiences (Anyone, Frequent Contacts, Occasional Contacts). Using the Internet User's Information Privacy Concern (IUIPC) scale, we were able to identify the effect of different types of privacy concerns on sharing this information with various audiences. We found that privacy concerns around data Collection predicted lower willingness to share either type of health status to all of these audiences. However, desire for Control and for Awareness of data practices increased willingness to share health information with certain audiences. We discuss the implications of our findings. © 2022 Owner/Author.
ABSTRACT
This study aims to examine the role of social media in filtering the spread of COVID-19 information. Covid-19 information is crowdsourcing Information. Anxiety caused by social media information must be addressed through explicit government regulations on handling COVID-19. As a result, governments must understand the emotions and sentiments conveyed on social media. Based on this point of view, this study examines the role of the government in responding to health information from social media through the ministry of health. This study uses a qualitative exploration method with the NvivoPlus12 tool in processing Ministry of Health data in providing health education, covid-19 information, and policy responses to COVID-19. This analysis shows that the ministry of health functions based on the type of information presented through official accounts, first, as a means of socialization. Second, providing real-time information as a medium for public information. Third, the Ministry of Health cooperates with other ministries to encourage policies, especially the handling of COVID-19. This finding is in line with the United Nations 2020 research that virtual services can be realized by using social media to build services and convey Information;In addition, this function makes it easier to know the community's response so that policies and management of COVID-19 run smoothly. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Despite the importance of safe sanitation and hygiene for sustainable development and public health, approximately half of India's rural population lacks access to safely managed sanitation. Policies prioritizing improved sanitation access have accelerated coverage, yet barriers to universal access and use remain. In this paper, we investigate how personal experience with a public health shock impacts recall of public health messages and households' sanitation and hygiene practices. Using a panel survey conducted before and after the first wave of the COVID-19 pandemic, and the resulting lockdown, in Bihar, India, we compare public health messaging recall and hygiene and sanitation beha-viors among households that experienced severe economic disruptions due to the COVID-19 lockdown and those that did not. We find that households that experienced economic disruptions had a higher recall of public health messaging around safe sanitation and hygiene. In addition, households that experienced these disruptions reported more social distancing, increased handwashing behavior, and reduced open defecation. A major public health shock, the COVID-19 pandemic, increased messaging around the importance of safe hygiene and sanitation for public health in India. We find that personal experience increased the salience of public health messaging, with positive returns to sanitation and hygiene practices. © 2022 The Authors.
ABSTRACT
Continuous patient care and the use of multiple medical machines are two challenges facing today's healthcare sector in terms of patient's healthcare. During the pandemic situation, many people isolated in their home, such as covid-19 positive patients, elderly people living away from their families, bedridden patients, etc., need regular health checks and controls, but during this pandemic is lacking. Recent advances in the Internet of Medical Things (IoMT) has been able to give good results in collecting health data of patients at home environment. Deep learning (DL) applications can able to run on edge nodes, it locally processes, computes and analyzes data from IOMT devices to make inferences on patient health information. This ensures the privacy and security of the patient's physiological information and also and allows patient health information to remain at the patient's side. Send all this information to healthcare professionals and relatives of patients. This framework will provide safety for isolated patients and a health support systemas a whole. © 2022 IEEE.