The Personalization of Conversational Agents in Health Care: Systematic Review

Kocaballı, A. Baki; Berkovsky, Shlomo; Quiroz, Juan C.; Laranjo, Liliana; Tong, Huong Ly; Rezazadegan, Dana; Briatore, Agustina; Coiera, Enrico

doi:10.2196/15360

Cited by 198 publications

(168 citation statements)

References 62 publications

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“…Conversational agents are increasingly used in diverse fields, including health care, and there is a need to identify different ways and outcomes of the use of conversational agents in health care. Existing reviews on conversational agents focus on a certain subtype of agents such as virtual coaches [ 31 - 33 ] or embodied conversational agents (ECAs) [ 34 ] or on specific functionalities of these agents such as behavior change [ 35 ] or mental health applications [ 36 , 37 ]. Other reviews report solely on the technical aspects of conversational agents such as system architecture and dialogues [ 38 ] or on the funding component of health care conversational interfaces [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

Car¹,

Dhinagaran²,

Kyaw³

et al. 2020

J Med Internet Res

312

197

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Background Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. Objective This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application. Methods We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis. Results The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education. Conclusions The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

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Section: Introductionmentioning

confidence: 99%

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

Car¹,

Dhinagaran²,

Kyaw³

et al. 2020

J Med Internet Res

312

197

View full text Add to dashboard Cite

show abstract

“…Systematic literature reviews investigated a variety of contexts of health care chatbots such as the role of conversational agents in health care in general [ 1 ] and in mental health [ 44 ], aspects of personalization of health care chatbots [ 45 ], as well as technical aspects of AI systems and architectures of conversational agents in health care [ 11 ]. However, there is surprisingly little systematic information on the application of AI-based conversational agents in health care for chronic diseases.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Schachner¹,

Keller²,

Wangenheim³

2020

J Med Internet Res

147

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Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

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“…In our pilot, we observed similar trends where participants rarely used the Alexa Skill. Enhancing the engagement of conversational agents through storytelling [1], personalization [9], and affect [3] has been proposed in the past. However, further research is needed to understand the design implication of such features for spoken conversational agents in the mental health context.…”

Section: Discussion and Challengesmentioning

confidence: 99%

Alexa depression and anxiety self-tests

Quiroz

Bongolan

Ijaz

2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Mental health resources available via websites and mobile apps provide support such as advice, journaling, and elements from cognitive behavioral therapy. The proliferation of spoken conversational agents, such as Alexa, Siri, and Google Home, has led to an increasing interest in developing mental health apps for these devices. We present the pilot study outcomes of an Alexa Skill that allows users to conduct depression and anxiety self-tests. Ten participants were given access to the Alexa Skill for two-weeks, followed by an online evaluation of the Skill's usability and trust. Our preliminary evaluation suggests that participants trusted the Skill and scored the usability and user experience as average. Usage of the Skill was low, with most participants using the Skill only once. In view of work-in-progress, we also present a discussion of implementation and study design challenges to guide the current literature on designing spoken conversational agents for mental health applications. CCS CONCEPTS • Human-centered computing → Sound-based input / output.

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The Personalization of Conversational Agents in Health Care: Systematic Review

Cited by 198 publications

References 62 publications

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

Alexa depression and anxiety self-tests

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