Journal of the American Medical Informatics Association· 2018Q1· Review
Conversational agents in healthcare: a systematic review
- 1,344citations
- Q1SCImago
- 2018year
Short summary
A systematic review of 17 studies (14 agents) found that conversational agents for health tasks, while emerging, are mostly evaluated with quasi-experimental designs, with only one RCT showing a significant reduction in depression symptoms (d=0.44, p=.04).
AI-generated from the title and abstract; the full text is not read.
Key points
- 17 articles and 14 conversational agents were included in the review.
- Most agents used finite-state or frame-based dialogue management.
- Half of the agents supported consumer self-care tasks.
- Only one RCT showed a significant reduction in depression symptoms (d=0.44, p=.04).
- Patient safety was rarely evaluated.
AI-generated from the title and abstract; the full text is not read.
Abstract
Objective: Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes. Methods: We searched PubMed, Embase, CINAHL, PsycInfo, and ACM Digital using a predefined search strategy. Studies were included if they focused on consumers or healthcare professionals; involved a conversational agent using any unconstrained natural language input; and reported evaluation measures resulting from user interaction with the system. Studies were screened by independent reviewers and Cohen's kappa measured inter-coder agreement. Results: The database search retrieved 1513 citations; 17 articles (14 different conversational agents) met the inclusion criteria. Dialogue management strategies were mostly finite-state and frame-based (6 and 7 conversational agents, respectively); agent-based strategies were present in one type of system. Two studies were randomized controlled trials (RCTs), 1 was cross-sectional, and the remaining were quasi-experimental. Half of the conversational agents supported consumers with health tasks such as self-care. The only RCT evaluating the efficacy of a conversational agent found a significant effect in reducing depression symptoms (effect size d = 0.44, p = .04). Patient safety was rarely evaluated in the included studies. Conclusions: The use of conversational agents with unconstrained natural language input capabilities for health-related purposes is an emerging field of research, where the few published studies were mainly quasi-experimental, and rarely evaluated efficacy or safety. Future studies would benefit from more robust experimental designs and standardized reporting. Protocol Registration: The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.
The authors' abstract, as published at the source. Journal of the American Medical Informatics Association, 2018 · DOI ↗
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Field: Applied Psychology
Applied PsychologyPsychology