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Cited by 242Speech and dialogue systemsMobile Health and mHealth ApplicationsArtificial Intelligence in Healthcare and Education

Health dialog systems for patients and consumers

Open access
Bickmore, Timothy, Giorgino, Toni·2006·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2005.12.004
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Abstract

There is a growing need for automated systems that can interview patients and consumers about their health and provide health education and behavior change interventions using natural language dialog. A number of these health dialog systems have been developed over the last two decades, many of which have been formally evaluated in clinical trials and shown to be effective. This article provides an overview of the theories, technologies and methodologies that are used in the construction and evaluation of these systems, along with a description of many of the systems developed and tested to date. The strengths and weaknesses of these approaches are also discussed, and the needs for future work in the field are delineated.

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Crossref · 10.1016/j.jbi.2005.12.004

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  • Prior studies of ECAs have simulated a health provider for health coaching and reviewing hospital discharge materials.

    “Prior studies of ECAs have simulated a health provider for health coaching and reviewing hospital discharge materials.11,20,65,66”

    From A chatbot for hypertension self-management support: user-centered design, development, and usability testing (2023) · cites this paper as [11]

    Section 5.3 describes ECAs as offering a virtual consultation with a simulated health provider, and §5.3.1 describes FitTrack’s ECA as an exercise advisor motivating sedentary adults. These passages support the health-coaching part of the claim, but our copy does not describe an ECA reviewing hospital discharge materials (searched: hospital discharge materials, discharge instructions, discharge). No page numbers were provided. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Overall, ECAs have been received positively by patients and increased adherence to treatment regimens.

    “Overall, these have been received positively by patients and increased adherence to treatment regimens.11,20,65,66”

    From A chatbot for hypertension self-management support: user-centered design, development, and usability testing (2023) · cites this paper as [11]

    Section 8.2 (page unavailable) reports that users of the FitTrack ECA increased physical activity relative to controls, supporting a narrower claim about health behavior change. It does not report increased adherence to treatment regimens. Section 7.2 mentions satisfied users of a telephone-based system, but does not establish that ECAs were generally received positively by patients. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Learning from a history of previous conversations plays a key role in ensuring the continuity of health communications that take place over multiple interactions over time.

    “Learning from a history of previous conversations plays a key role in ensuring the continuity of health communications that take place over multiple interactions over time [31].”

    From The Personalization of Conversational Agents in Health Care: Systematic Review (2019) · cites this paper as [31]

  • Based on existing literature, there is a lack of consensus regarding the definitions of conversational agents, dialogue systems, embodied conversational agents, smart conversational interfaces, or chatbots.

    “Based on existing literature, there is a lack of consensus regarding the definitions of conversational agents, dialogue systems, embodied conversational agents, smart conversational interfaces, or chatbots.”

    From Conversational agents in healthcare: a systematic review (2018) · cites this paper as [13]

    Section 5.3 defines embodied conversational agents and describes them as a form of multimodal dialog system, but does not say that definitions lack consensus. Searches for definitional disagreement and terminology found no passage addressing the claimed lack of consensus across the listed terms; page numbers were not available in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • The virtual patient advocate mimics an empathic clinician's face-to-face conversation, including gaze, posture, and hand gestures, to deliver tailored patient education, check comprehension, and track progress.

    “The VPA emulates the face-to-face conversational behavior of an empathic clinician, including nonverbal communicative behavior such as gaze, posture, and hand gestures to deliver patient education messages tailored to individual needs, assess patient comprehension, and record progress.14, 15, 16”

    From Reaching Women through Health Information Technology: The Gabby Preconception Care System (2013) · cites this paper as [15]

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Cites

Flagged first, then by citations

5 of the 94 references couldn’t be matched to a known paper.

Research works printed but not linked: 11

  1. [1]1. de Vries H, Brug J. Computer-tailored interventions motivating people to adopt health promoting behaviours: Introduction to a new approach. Patient Educ Couns 1999;36:99-105.
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  3. [7]7. AAAI. Fall Symposium on Dialog Systems for Health Communica- tion; 2004. Available from: http://www.ccs.neu.edu/home/bickmore/ dshc.
  4. [8]8. AAAI. Spring Symposium on Argumentation for Consumers of Healthcare; 2006. Available from: http://www.uncg.edu/~nlgreen/ aaaisss06/home.html.
  5. [13]13. Larsson S, Ljunglof P, Cooper R, Engdahl E, Ericsson S. GoDIS- An Accommodating Dialogue System. In: ANLP/NAACL-2000 Workshop on Conversational Systems; 2000. p. 7-10.
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  7. [33]33. Beskow J, McGlashan S. Olga: a conventional agent with gestures. In: IJCAI 97; 1997.
  8. [50]50. Young M, Sparrow D, Gottlieb D, Selim A, Friedman R. A telphone-linked computer system for COPD care. Chest 2001;119(5):1565-75.
  9. [56]56. Cassell J, Sullivan J, Prevost S, Churchill E. Embodied conversa- tional agents. Cambridge: MIT Press; 2000.
  10. [69]69. Brown G, Yule G. Discourse analysis. Cambridge: Cambridge University Press; 1983.
  11. [78]78. Fitzmaurice G, Laird N, Ware J. Applied longitudinal analy- sis. Hoboken, NJ: Wiley; 2004.

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Authors
Bickmore, T., & Giorgino, T.
Affiliations
Northeastern University (US)
Corresponding author
Bickmore, Timothy
ORCID
Bickmore, Timothy
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2006-10
Volume
39
Issue
5
Pages
556-571
ISSN
1532-0464 (ISSN-L)
DOI
10.1016/j.jbi.2005.12.004
OpenAlex
W2118238333
Semantic Scholar
b4ee0646fd6674a22749871b664652d6dccb6613
Cited by
242
References
94
Citations by year
2026: 13 · 2025: 13 · 2024: 11 · 2023: 10 · 2022: 11 · 2021: 10 · 2020: 9 · 2019: 23 · 2018: 16 · 2017: 8 · 2016: 14 · 2015: 15 · 2014: 15 · 2013: 7 · 2012: 9
FWCI
1.67
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
patient interviews, health education, behaviour change interventions, clinical trials, system evaluation, chatbots
MeSH terms
Communication, Computing Methodologies, Humans, Medical Informatics, Patient Education as Topic, Reproducibility of Results

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