ABSTRACT
Diverse countries throughout the world were quar-antined due to the novel pandemic known as COVID-19, even after vaccination,. As a result of this grim circumstance, most socioeconomic and political spheres have encountered deep crisis and from there people have experienced stress, anxiety, depression, and even suicide, In this paper, we propose a smart pervasive conversational agent for psychological assistance during and after COVID-19 quarantine, which could converse with a regular citizen to raise awareness of the genuine threat of the outbreak and the importance of vaccination. Our proposed conversational agent could be able to recognize and manage stress and anxiety using natural language understanding (NLU) and international stress and anxiety scales. The messages given by our agent and its mode of communication may help to alleviate anxiety following the world's lockdown. Our agent's comment threads and management styles may be able to soothe people's worry during the world's lockdown. Our proposed approach is a mobile healthcare service with three interdependent units: an input processing (IP) that performs natural language understanding (NL), a Storage that stores every interaction, and a response manager (RM) that controls the responses of our conversational agent. © 2022 IEEE.
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Artificial-Intelligence (AI) is becoming more widespread in several areas, from economics and government to consumer-services and even healthcare. In fact, in the latter, there was a big use increase in the past three years, also due to the COVID-19 pandemic. Several solutions have been implemented to tackle the several challenges imposed by this new disease, being one of such solutions chatbots. In this article, we present the results of a Systematic Literature Review (SLR) that identifies the Chatbots applications in COVID-19 disease. In this SLR, we identified 9987 papers from which we selected 30 studies, on which we performed a full-text analysis. From our research, we could conclude that several solutions were implemented, with good acceptance by citizens, despite several limitations, such as limited time to develop the solutions (which narrowed some features, such as AI voice conversation), lack of global implementation and infrastructure limitations. © 2022 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.
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Population health management (PHM) is a systematic approach that uses information technology and digital health tools to improve health and healthcare at the population-level. PHM programs identify individuals who could benefit from a set of PHM interventions;implement computable logic to stratify patients according to risk;and implement protocol-based logic to assign individuals within each stratum to specific interventions. PHM is a promising approach to help achieve the Quintuple Aim of healthcare: (i) improving population health through population-level interventions;(ii) enhancing the care experience by shifting healthcare from the clinic to the patient's home;(iii) reducing costs by focusing on health promotion and prevention;(iv) improving the work life of the health care workforce by reducing clinic workload;and (v) advancing health equity by maximizing reach through a combination of digital and human-based patient outreach interventions. This chapter discusses the components of a technical infrastructure to support PHM, including data sources (registries, electronic health records), data analytics tools, patient outreach and engagement tools, and patient tracking dashboards. We also describe real-world examples of PHM programs focused on chronic disease management, genetic testing for hereditary cancers, colorectal cancer screening, COVID-19 testing and vaccination, and tobacco cessation. PHM is expected to experience substantial growth with novel digital health technologies, such as sensors, phone apps, conversational agents, and virtual reality;artificial intelligence;and new data sources. © 2023 Elsevier Inc. All rights reserved.
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The aims of nursing training include not only mastering skills but also fostering the competence to make decisions for problem solving. In prenatal education, cultivating nurses' knowledge and competence of vaccine administration is a crucial issue for protecting pregnant women and newborns from infection. Therefore, obstetric vaccination knowledge has become a basic and essential training program for nursing students. However, most of these training programs are given via the lecture-based teaching approach with skills practice, providing students with few opportunities to think deeply about the relevant issues owing to the lack of interaction and context. This could have a negative impact on their learning effectiveness and clinical judgment. To address this problem, a mobile chatbot-based learning approach is proposed in this study to enable students to learn and think deeply in the contexts of handling obstetric vaccine cases via interacting with the chatbot. In order to verify the effectiveness of the proposed approach, an experiment was implemented. Two classes of 36 students from a university in northern Taiwan were recruited as participants. One class was the experimental group learning with the proposed approach, while the other class was the control group learning with the conventional approach (ie, giving lectures to explain the instructional content and training cases). The results indicate that applying a mobile chatbot for learning can enhance nursing students' learning achievement and self-efficacy. In addition, based on the analysis of the interview results, students generally believed that learning through the mobile chatbot was able to promote their self-efficacy as well as their learning engagement and performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Despite improvements in the universal usability of digital technologies and growing numbers of conversational agents (CAs), new technologies rarely reflect older adults' needs, perceptions, and interests. Following this urgent call, we aim to identify older adults' (ranging in age from 65 to 75) perceptions, needs and challenges when conversing with a smart speaker-based voice assistant (Google Home) about the Covid-19 pandemic during the first-Time user interaction. Based on CASA (Computers are Social Actors) Paradigm and Similarity Attraction Theory, our user-centered qualitative research showed that an emphatic voice assistant triggered older adults to disclose to a CA more than a human being and build a digital companionship with them. Going beyond the standard universal usability considerations, we revealed how their cultural biases, beliefs and ageing stereotypes affected their perception of the voice assistant. However, the similarity attraction effect is not activated in older adults since a voice assistant with an older voice was not perceived as close or similar to them and they perceived technology as a young tool to be adapted. Unexpectedly, the older is wiser stereotype has been deactivated in a technological context for older adults in Turkey. © 2022 IEEE.
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The development of conversational agents to interact with patients and deliver clinical advice has attracted the interest of many researchers, particularly in light of the COVID-19 pandemic. The training of an end-to-end neural based dialog system, on the other hand, is hampered by a lack of multi-turn medical dialog corpus. We make the very first attempt to release a high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog, with over 1K conversations collected from the online medical counselling websites. We annotate each utterance of the conversation with seven different categories of medical entities, including diseases, symptoms, medical tests, medical history, remedies, medications and other aspects as additional labels. Finally, we propose a novel neural medical dialog system based on the CDialog dataset to advance future research on developing automated medical dialog systems. We use pre-trained language models for dialogue generation, incorporating annotated medical entities, to generate a virtual doctor's response that addresses the patient's query. Experimental results show that the proposed dialog models perform comparably better when supplemented with entity information and hence can improve the response quality. © 2022 Association for Computational Linguistics.
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With the COVID-19 pandemic serving as a trigger, 2020 saw an unparalleled global expansion of tele-health [23]. Tele-health successfully lowers the need for in-person consultations and, thus, the danger of contracting a virus. While the COVID-19 pandemic sped up the adoption of virtual healthcare delivery in numerous nations, it also accelerated the creation of a wide range of other different technology-enabled systems and procedures for providing virtual healthcare to patients. Rightly so, the COVID-19 has brought many difficulties for patients (https://www.who.int/news/item/02-03-2022-covid-19-pandemic-triggers-25-increase-in-prevalence-of-anxiety-and-depression-worldwide ) who need continuing care and monitoring for mental health issues and/or other chronic diseases. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The COVID-19 increased the importance of patient's continuous assessment of health outcomes. In 2021 WHO proposed some Digital Health guidelines arguing that health systems should consider the use of emergent technologies in health care services. This health environment is providing intelligent systems to guide patients in self-care. One example of that is the chatbot which is, a conversational agent that have been assuming an important role in how to improve health knowledge, reducing the incidence of diseases and avoiding new ones. Pregnant women are a profile where the self-care referred before is a critical issue. Prenatal services reveal to be an important part of the care process where most complications for that women happen. This article aims to comprehend how pregnant women interact with a conversational agent and how relevant this Digital Health tool is for primary health care services. The study presents the process and results of a systematic literature review about the user experience with of a chatbot in pregnant women self-care context; a summary of GISSA intelligent chatbot development including the use of technologies such as DialogFlow; and the process and results of GISSA usability evaluation in research field. Results show that a small amount of articles was gathered and the chatbot as a tool is a relevant opportunity for primary care health services in Brasil.
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Chatbots or conversational agents' tools have become important means of communication, especially with the needs arising from COVID-19 and the increase in artificial intelligence solutions. This work presents a case study of the use of a chatbot, specifically the WhatsApp messaging application, to assist in the pre-triage sector of a university dental clinic. To this end, several chatbot solutions were compared and the DialogFlow tool was chosen, which employs Machine Learning techniques to create the chatbot. The Twillio cloud communication platform was used to make the connection with the social network. The PSSUQ usability questionnaire (15-people sample space), was used to assess the satisfaction of chatbot users, analyze its responsiveness and possible failures, as well as verify if the bot achieves its initial objective and analyze its behavior. © 2022 IEEE.
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In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients' needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as 'chatbots', are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13-14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients' needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients. © 2022 IEEE.
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In light of the global crisis like COVID-19, many people are afraid to leave the house and visit the doctor for fear of these epidemics. On the other side, the amazing development of artificial intelligence has led to chatbots' emergence and use in several fields. Therefore, in this paper, we propose to build an automated chatbot system that interacts with people in the Arabic Algerian dialect and helps patients ask general medical questions. To achieve this purpose, we propose three sequence-to-sequence models based on three Recurrent Neural Networks encoder-decoders: Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, to understand the user's request and provide the right useful answer. Experimentally, we have collected medical data of 2150 pairs. The results were very promising, and the proposed chatbot performed excellently in handling user questions. © 2022 IEEE.
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The invention of the Coronavirus (nCOV-19), which has turn out to be a worldwide pandemic has affected big range of humans of just about all age companies and socio-demographic popularity in the global. It was not only a pandemic with physical challenges, it brought emotional and mental challenges with itself (WHO Health Alert brings COVID-19 facts to billions via WhatsApp.). People were/are confused to what to do or how to react to the situation, which news to believe in, which not to (www.messengerpeople.com ;523: Origin is unreachable.). So, we came up with an idea of a voice automated chatbot, which would bring a better version of chatbot to the people. It is designed in such a way that people with disabilities can also use it because of the speech control. Our team also decided to bring the accurate information from the professionals to the very devices of the people by feeding in the updated information in the code on a regular basis. Unlike doctors our Botch would provide precise information 24/7 and would also prevent people from leaving their homes in this hour of tragedy. We have made sure that our chatbot is interactive enough so that the users will not find it uncomfortable to use a new app for their health assistance (Jadhav KP, Thorat SA, Toward designing conversational agent systems. In Advances in Intelligent Systems and Computing. Springer: Berlin, Germany, 2020). It is high time we have been confused by the vast information available, half of them are hardly true. So, we have tried to provide our users with the most precise information available. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Rumors about the COVID-19 vaccines are spreading rapidly on social media platforms, questioning their intentions and efficiency. Currently, chatbots are used to combat the risk of misinformation amplification during the pandemic. They provide users with information from trusted and reliable sources. However, most of the current COVID-19 chatbots are non-personalized and do not focus on the vaccination process, rather they focus on answering general questions and performing symptom checking. In this paper, an empathetic chatbot named “Vaxera” was developed to provide users with accurate and up-to-date information about COVID-19 and its vaccines specifically. Vaxera provides users with information regarding COVID-19 frequently asked questions, advice and precautions, available vaccines, rumors and myths, and travel regulations. Additionally, it clears the circulating misconceptions about the vaccines and motivates the users on social media platforms to get vaccinated in a friendly manner. It tries to build a bond with the users through empathy and humor, so users will not feel forced. The results showed positive feedback from the participants who found the chatbot friendly and informative, as it corrected multiple rumors they believed. Moreover, a significant increase in the participants’ intentions to get vaccinated was observed after interacting with the chatbot. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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BACKGROUND: Chatbots are computer programs that present a conversation-like interface through which people can access information and services. The COVID-19 pandemic has driven a substantial increase in the use of chatbots to support and complement traditional health care systems. However, despite the uptake in their use, evidence to support the development and deployment of chatbots in public health remains limited. Recent reviews have focused on the use of chatbots during the COVID-19 pandemic and the use of conversational agents in health care more generally. This paper complements this research and addresses a gap in the literature by assessing the breadth and scope of research evidence for the use of chatbots across the domain of public health. OBJECTIVE: This scoping review had 3 main objectives: (1) to identify the application domains in public health in which there is the most evidence for the development and use of chatbots; (2) to identify the types of chatbots that are being deployed in these domains; and (3) to ascertain the methods and methodologies by which chatbots are being evaluated in public health applications. This paper explored the implications for future research on the development and deployment of chatbots in public health in light of the analysis of the evidence for their use. METHODS: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for scoping reviews, relevant studies were identified through searches conducted in the MEDLINE, PubMed, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Open Grey databases from mid-June to August 2021. Studies were included if they used or evaluated chatbots for the purpose of prevention or intervention and for which the evidence showed a demonstrable health impact. RESULTS: Of the 1506 studies identified, 32 were included in the review. The results show a substantial increase in the interest of chatbots in the past few years, shortly before the pandemic. Half (16/32, 50%) of the research evaluated chatbots applied to mental health or COVID-19. The studies suggest promise in the application of chatbots, especially to easily automated and repetitive tasks, but overall, the evidence for the efficacy of chatbots for prevention and intervention across all domains is limited at present. CONCLUSIONS: More research is needed to fully understand the effectiveness of using chatbots in public health. Concerns with the clinical, legal, and ethical aspects of the use of chatbots for health care are well founded given the speed with which they have been adopted in practice. Future research on their use should address these concerns through the development of expertise and best practices specific to public health, including a greater focus on user experience.
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Due to covid-19, educational institutions all around the world resorted to e-learning regardless of the readiness of e-learning tools used or the students' attitude and readiness towards using them. In this paper, we developed a pedagogical conversational agent, which is an e-learning tool and considered as a solution for the lack of social interaction that e-learning suffers from, to know the attitude of 5th-grade students in an elementary school in Tunisia towards learning « Fractions» with the help of a pedagogical conversational agent as learning support. The attitude towards using the conversational agent was favorable with the suggestions of providing a more natural interaction that focuses on making the conversational agent have more personality and emotionally intelligent and making the conversations longer with including multi-media elements. © 2021 IEEE.
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Accessibility to medical knowledge and healthcare costs are the two major impediments for the common man. Conversational agents like Medical Chatbots, which are designed keeping in view medical applications can potentially address these issues. Chatbots can either be generic of specificin nature. Covid-19 is a communicable disease and early detection of it can let people know about the serious consequences of this disorder and help save human lives. In this article, we present a specific text-to-text Chatbot that engagespatients in the conversation using advanced Natural Language Understanding (NLU) and Natural Language Processing (NLP) techniques using Rasa Framework, to provide a personalized prediction based on the various symptoms sought from the patient. The Chatbot handles two languages: Arabic and French, then according to the analysis result, it suggests measures and actions be taken in order to serve life and prevent the spread of this virus which has devastated the whole world.
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Artificial intelligence (AI) conversational agents (CA) or chatbots represent one of the technologies that can provide automated customer service for companies, a trend encountered in recent years. Chatbot use is beneficial for companies when associated with positive customer experience. The purpose of this paper is to analyze the overall customer experience with customer service chatbots in order to identify the main influencing factors for customer experience with customer service chatbots and to identify the resulting dimensions of customer experience (such as perceptions/attitudes and feelings and also responses and behaviors). The analysis uses the systematic literature review (SLR) method and includes a sample of 40 publications that present empirical studies. The results illustrate that the main influencing factors of customer experience with chatbots are grouped in three categories: chatbot-related, customer-related, and context-related factors, where the chatbot-related factors are further categorized in: functional features of chatbots, system features of chatbots and anthropomorphic features of chatbots. The multitude of factors of customer experience result in either positive or negative perceptions/attitudes and feelings of customers. At the same time, customers respond by manifesting their intentions and/or their behaviors towards either the technology itself (chatbot usage continuation and acceptance of chatbot recommendations) or towards the company (buying and recommending products). According to empirical studies, the most influential factors when using chatbots for customer service are response relevance and problem resolution, which usually result in positive customer satisfaction, increased probability for chatbots usage continuation, product purchases, and product recommendations.
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Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.
Subject(s)
COVID-19 , Mobile Applications , Artificial Intelligence , Cognition , Female , Humans , Male , Mental Health , Pandemics , Pilot ProjectsABSTRACT
During the lockdown period, people suffered a lot with much misinformation and conflicts regarding the hospitals and the dangerous COVID infection. A large number of people have common and simple doubts regarding the infection, quarantine, bed availability, oxygen cylinder availability. So, a patient with minor issues does not need to reach the hospital in person to consult with doctors for simple and similar queries in that lockdown situation. To neglect these difficulties, the chatbots were proposed with an AIML platform to provide details about infection rate, information regarding treatments, decision making regarding hospital admissions. Artificial Conversational Entity otherwise called chatbots acts as the conversational agent or talkbot or chatterbot which has the capability of performing an intellectual conversation with a human. There are many chatbots built using Watson, Google DialogFlow, Keras Seq2Seq, Gradient Descent algorithm, Beam search decoding methods, etc., Even though many chatbots respond in regional languages, not all the chatbots are appreciated for their efficacy and attractive customization. So, a chatbot with both the national language which is English, and the regional language which is the Tamil language, using the RASA framework has been developed. RASA NLU is well known for its efficacy and good customization methodologies. RASA NLU has a higher level of Application Programming Interface (API). So, the proposed method can be connected with a web page, and carousels are added to project the model very much attractive and deliver effective visualization. The carousels provide a wide range of information in regional language and it can be customized according to the user's requirements. © 2022 IEEE.
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BACKGROUND: Health chatbots are rising in popularity and capability for fighting the novel SARS-CoV-2 coronavirus (COVID-19). OBJECTIVES: This study aims to review the current literature on COVID-19 related chatbots in healthcare, identify and characterize these emerging technologies and their applications for combating COVID-19, and describe related challenges. METHODS: The authors conducted a scoping review of peer-reviewed literature on COVID-19, guided by the Arksey and O'Malley framework. PubMed/MEDLINE and Google Scholar were searched over a period between January and September 2020 by using the keywords "COVID* chatbot", "virtual assistant", "AI enabled platform COVID" and associated synonyms. Relevant studies' references were checked for further articles. The content of these studies was screened and thematically analyzed by the two authors. RESULTS: Out of 543 articles initially identified, 9 were eligible for inclusion. Studies describing chatbots' development and architecture (n=6) were the most common, and only 3 empirical studies on the user experience were identified. Our scoping review identified five key applications of the current health chatbots, which were: disseminating health information and knowledge; self-triage and personal risk assessment; monitoring exposure and notifications; tracking COVID-19 symptoms and health aspects; and combating misinformation and fake news. Furthermore, these technologies can accomplish the following tasks: ask and answer questions; create health records and history of use; complete forms and generate reports; and take simple actions. Nonetheless, the use of health chatbots poses many challenges both at the level of the social system (i.e., consumers' acceptability) as well as the technical system (i.e., design and usability). CONCLUSION: Using health chatbots to combat COVID-19 is a practice still in its infancy. We believe that our work will help researchers in this domain gain better understanding of this novel technology's design and applications, which are needed for continuous improvement in the health chatbots' functionalities and their usefulness to fight COVID-19.