Empathic conversational agents for real-time monitoring and co-facilitation of patient-centered healthcare

Adikari, Achini; Silva, Daswin De; Moraliyage, Harsha; Alahakoon, Damminda; Wong, Jiahui; Gancarz, Mathew; Chackochan, Suja; Park, Sohee; Heo, Rachel; Leung, Yvonne

doi:10.1016/j.future.2021.08.015

Cited by 28 publications

(10 citation statements)

References 25 publications

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“…The proliferation of voice assistant chatbots such as Siri, Alexa, Cortana, and Google [ 34 ], as well as the numerous chatbot functions in online retail has familiarised a majority of modern society with the utility, engagement, and operation of a chatbot. For instance, in industrial settings, chatbots are used to provide information, instructions, detect fatigue, and address exceptions [ 35 , 36 ], while in healthcare, chatbots have been used for automated post-treatment communications and support groups, counselling, and healthcare service administrative support [ 37 , 38 ]. However, most chatbots are designed using Frequently Asked Questions (FAQs) for information provision or process specific in executing a well-defined repetitive or sequential series of tasks via conversational inputs.…”

Section: Related Workmentioning

confidence: 99%

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Rathnayaka

Mills

Burnett

et al. 2022

Sensors

Self Cite

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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.

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Section: Related Workmentioning

confidence: 99%

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Rathnayaka

Mills

Burnett

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Some studies addressed the problem specific to a domain. For example, Adikari et al [78] stated that previous chatbots in the healthcare sector mainly focused on question-answer systems. They developed a rule-based chatbot that detects patient emotion using NLP techniques and generates a response using a template.…”

Section: Lack Of Emotionally Labeled Conversational Datasetsmentioning

confidence: 99%

“…Both kinds of emotions, the group and individual emotions, are used to capture the emotion expression sequence. A rule-based system is used to generate responses based on negative emotions expressed by patients to predict and generate an automated personalized empathetic alert [78]. The second study uses a rule-based approach to detect, predict, and build a statistical response generator based on an utterance's tags.…”

Section: Response Generation Modelsmentioning

confidence: 99%

“…The most popular datasets used are Weibo, followed by Twitter, which are open-domain conversational datasets. Only one study used a domain-specific conversational dataset for the healthcare domain [78]. Since none of these datasets are labeled with emotions, researchers have used a machine learning, lexicon-based, or hybrid approach to label the conversations with emotions.…”

Section: Datasetsmentioning

confidence: 99%

“…A vast majority of research studies focus on developing an emotionally intelligent chatbot for an open domain, whereas only a few have focused on the closed domain, using a rule-based approach for generating responses. And only one of the reviewed studies sourced a domain-specific dataset for healthcare [78]. A generative chatbot that synthesizes human-like natural responses requires a massive dataset for training [39].…”

Section: Dataset Availabilitymentioning

confidence: 99%

See 2 more Smart Citations

Emotionally Intelligent Chatbots: A Systematic Literature Review

Bilquise

Ibrahim

Shaalan

2022

Human Behavior and Emerging Technologies

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Conversational technologies are transforming the landscape of human-machine interaction. Chatbots are increasingly being used in several domains to substitute human agents in performing tasks, answering questions, giving advice, and providing social and emotional support. Therefore, improving user satisfaction with these technologies is imperative for their successful integration. Researchers are leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to impart emotional intelligence capabilities in chatbots. This study provides a systematic review of research on developing emotionally intelligent chatbots. We employ a systematic approach to gather and analyze 42 articles published in the last decade. The review is aimed at providing a comprehensive analysis of past research to discover the problems addressed, the techniques used, and the evaluation measures employed by studies in embedding emotion in chatbot conversations. The study’s findings reveal that most studies are based on an open-domain generative chatbot architecture. Researchers mainly address the issue of accurately detecting the user’s emotion and generating emotionally relevant responses. Nearly 57% of the studies use an enhanced Seq2Seq encoding and decoding of the input of the conversational model. Almost all the studies use both the automatic and manual evaluation measures to evaluate the chatbots, with the BLEU measure being the most popular method for objective evaluation.

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Development of Virtual Reality SBIRT Skill Training with Conversational AI in Nursing Education

Seo

Chaudhury

et al. 2023

Lecture Notes in Computer Science

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Empathic conversational agents for real-time monitoring and co-facilitation of patient-centered healthcare

Cited by 28 publications

References 25 publications

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

A Mental Health Chatbot with Cognitive Skills for Personalised Behavioural Activation and Remote Health Monitoring

Emotionally Intelligent Chatbots: A Systematic Literature Review

Development of Virtual Reality SBIRT Skill Training with Conversational AI in Nursing Education

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