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Conversational agents in healthcare: a systematic review

Overview of attention for article published in Journal of the American Medical Informatics Association, July 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#50 of 2,033)
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

news
4 news outlets
twitter
65 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
55 Mendeley
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Title
Conversational agents in healthcare: a systematic review
Published in
Journal of the American Medical Informatics Association, July 2018
DOI 10.1093/jamia/ocy072
Pubmed ID
Authors

Liliana Laranjo, Adam G Dunn, Huong Ly Tong, Ahmet Baki Kocaballi, Jessica Chen, Rabia Bashir, Didi Surian, Blanca Gallego, Farah Magrabi, Annie Y S Lau, Enrico Coiera

Abstract

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. 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. 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. 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. The protocol for this systematic review is registered at PROSPERO with the number CRD42017065917.

Twitter Demographics

The data shown below were collected from the profiles of 65 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 55 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 38%
Student > Master 7 13%
Researcher 5 9%
Student > Bachelor 4 7%
Other 3 5%
Other 15 27%
Readers by discipline Count As %
Computer Science 18 33%
Unspecified 12 22%
Medicine and Dentistry 11 20%
Social Sciences 4 7%
Psychology 3 5%
Other 7 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 72. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 January 2019.
All research outputs
#202,558
of 12,470,444 outputs
Outputs from Journal of the American Medical Informatics Association
#50
of 2,033 outputs
Outputs of similar age
#9,607
of 266,492 outputs
Outputs of similar age from Journal of the American Medical Informatics Association
#5
of 61 outputs
Altmetric has tracked 12,470,444 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,033 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.2. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 266,492 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.