Using Chatbots to Support Medical and Psychological Treatment Procedures: Challenges, Opportunities, Technologies, Reference Architecture

Pryss, Rüdiger; Baumeister, Harald; Winkler, Jens; Probst, Thomas; Reichert, Manfred; Langguth, Berthold; Spiliopoulou, Myra; Schlee, Winfried

doi:10.1007/978-3-030-31620-4_16

Cited by 21 publications

(12 citation statements)

References 16 publications

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“…The current healthcare consultation can be enhanced by adopting intelligent technologies [50]. It has been proven that conversational user interfaces can support the application of CDSTs, in terms of increased transparency [16], and optimized workflow [19], improved collaboration [11]. In this paper, we have reported an exploratory field study investigating a conversational interface (CI)-assisted consultation, which was designed to use a chatbot assistant to provide real-time decision support to occupational physicians during consultations.…”

Section: Discussionmentioning

confidence: 99%

“…Conversational interfaces, such as a chatbot, can facilitate the application of CDSTs with many promises, such as building trust [32], engaging users [33], increasing transparency [16], and guiding the workflow [19]. As a new generation of AI systems, chatbots commonly use natural language processing to understand user inputs and adopt text messaging applications to provide feedback through the nuances of human language [34].…”

Section: B Conversational Interfaces For Clinical Routinesmentioning

confidence: 99%

“…During the last few years, it has been seen an increased interest in using conversational user interfaces to promote collaborative decision-making practices between clinicians and AI [11], [15], [16], emphasizing supporting medical reasoning [16], collecting patient intake [17], and facilitating clinical training [18]. These practical applications of conversational agents can offer several benefits in the healthcare context, including increased transparency of machine learning models [16], workflow optimization [19], and improved collaboration [11].…”

Section: Introductionmentioning

confidence: 99%

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Understanding Physician’s Experience With Conversational Interfaces During Occupational Health Consultation

et al. 2020

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This paper presents an exploratory study on using conversational interfaces (CIs) to support physicians in conducting occupational health consultations. The CI was achieved through a web-based information dashboard with a chatbot assistant for providing real-time suggestions through text messages. Two system designs were developed: the first using a proactive chatbot, the second using an on-demand type of interaction. The effectiveness of the proposed CI and the two types of chatbot designs were investigated in a field study consisted of eight healthcare consultations. Quantitative results showed that the CI was positively evaluated as a reliable tool to be used during medical consultations and that occupational health physicians were eager to use this technology in their work. The qualitative data analysis suggested that our design concept might improve the workflow during the consultation, particularly with respect to the access to relevant information and structured decision-making processes using valuable references. The on-demand, lightweight type of chatbot interaction was better perceived than the proactive one. Based on these findings, we discuss implications for the future development of occupational health consultation based on CI and their potential contribution to computer-assisted, data-driven healthcare.

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Section: Discussionmentioning

confidence: 99%

Section: B Conversational Interfaces For Clinical Routinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding Physician’s Experience With Conversational Interfaces During Occupational Health Consultation

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“…46 Furthermore, they are flexible regarding location and time, 47 can be used anonymously 48 49 and provide personalisation through implicit customisation. 50 Therefore, software agents could help to overcome barriers and provide psychological and health behaviour change interventions on a large scale in the future.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Study protocol of a randomised controlled trial on SISU, a software agent providing a brief self-help intervention for adults with low psychological well-being

Bendig

Meißner²,

Erb³

et al. 2021

BMJ Open

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IntroductionOnly a minority of people living with mental health problems are getting professional help. As digitalisation moves on, the possibility of providing internet/mobile-based interventions (IMIs) arises. One type of IMIs are fully automated conversational software agents (chatbots). Software agents are computer programs that can hold conversations with a human by mimicking a human conversational style. Software agents could deliver low-threshold and cost-effective interventions aiming at promoting psychological well-being in a large number of individuals. The aim of this trial is to evaluate the clinical effectiveness and acceptance of the brief software agent-based IMI SISU in comparison with a waitlist control group.Methods and analysisWithin a two-group randomised controlled trial, a total of 120 adult participants living with low well-being (Well-being Scale/WHO-5) will be recruited in Germany, Austria and Switzerland. SISU is based on therapeutic writing and acceptance and commitment therapy-based principles. The brief intervention consists of three modules. Participants work through the intervention on 3 consecutive days. Assessment takes place before (t1), during (t2) and after (t3) the interaction with SISU, as well as 4 weeks after randomisation (t4). Primary outcome is psychological well-being (WHO-5). Secondary outcomes are emotional well-being (Flourishing Scale), psychological flexibility (Acceptance and Action Questionnaire-II), quality of life (Assessment of Quality of Life -8D), satisfaction with the intervention (Client Satisfaction Questionnaire-8) and side effects (Inventory for the assessment of negative effectsof psychotherapy). Examined mediators and moderators are sociodemographic variables, personality (Big Five Inventory-10), emotion regulation (Emotion Regulation Questionnaire), alexithymia (Toronto Alexithymia Scale-20), centrality of events (Centrality of Events Scale), treatment expectancies (Credibility Expectancy Questionnaire) and technology alliance (Inventory of Technology Alliance–Online Therapy). Data analysis will be based on intention-to-treat principles. SISU guides participants through a 3-day intervention.Ethics and disseminationThis trial has been approved by the ethics committee of the Ulm University (No. 448/18, 18.02.2019). Results will be submitted for publication in a peer-reviewed journal and presented at conferences.Trial registrationThe trial is registered at the WHO International Clinical Trials Registry Platform via the German Clinical Trials Register (DRKS): DRKS00016799 (date of registration: 25 April 2019). In case of important protocol modifications, trial registration will be updated. This is protocol version number 1.

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“…Com aumento da geração de texto em plataformas baseadas na internet, a extração e interpretação de informação útil tem ganhado ainda mais relevância. Com o aumento da pesquisa em Inteligência Artificial, existem diversas linhas de pesquisa para a criação de chatbots, utilizados em áreas como Atendimento ao Cliente e Saúde (Smutny;Schreiberova, 2020;Pryss et al, 2019).…”

Section: Processamento De Linguagem Natural (Nlp)unclassified

Análise de redes sociais como estratégia de apoio à vigilância em saúde durante a Covid-19

et al. 2020

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RESUMO O grande volume de dados gerados em redes sociais é usado por empresas para monitoramento das opiniões do público sobre seus produtos e serviços. Na área da Saúde, esses dados podem conter informações também aplicáveis na vigilância, como na avaliação do impacto de políticas públicas ou na identificação de fake news. Este trabalho apresenta resultados de estudos demonstrando como a análise de dados de redes sociais pode ser utilizada nas atividades de vigilância, tendo como estudo de caso a pandemia da Covid-19. Foi utilizada uma abordagem baseada em Ciência de Dados, com extração de informações através de algoritmos de aprendizado de máquina. Os resultados indicam que essa abordagem pode revelar importantes informações para as atividades de vigilância, trazendo uma visão em tempo real de aspectos relacionados à pandemia.

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Using Chatbots to Support Medical and Psychological Treatment Procedures: Challenges, Opportunities, Technologies, Reference Architecture

Cited by 21 publications

References 16 publications

Understanding Physician’s Experience With Conversational Interfaces During Occupational Health Consultation

Understanding Physician’s Experience With Conversational Interfaces During Occupational Health Consultation

Study protocol of a randomised controlled trial on SISU, a software agent providing a brief self-help intervention for adults with low psychological well-being

Análise de redes sociais como estratégia de apoio à vigilância em saúde durante a Covid-19

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