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Health dialog systems for patients and consumers
Open accessAbstract
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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“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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]
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“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]
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“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]
Experimental — accuracy not yet measuredIn the citing paper
“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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Metadata
- 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)
- 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
- bronze
- doi.org
- 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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