The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Response to a relational agent by hospital patients with depressive symptoms
Open accessAbstract
Depression affects approximately 15% of the US population, and is recognized as an important risk factor for poor outcomes among patients with various illnesses. Automated health education and behavior change programs have the potential to help address many of the shortcomings in health care. However, the role of these systems in the care of patients with depression has been insufficiently examined. In the current study, we sought to evaluate how hospitalized medical patients would respond to a computer animated conversational agent that has been developed to provide information in an empathic fashion about a patient's hospital discharge plan. In particular, we sought to examine how patients who have a high level of depressive symptoms respond to this system. Therapeutic alliance-the trust and belief that a patient and provider have in working together to achieve a desired therapeutic outcome- was used as the primary outcome measure, since it has been shown to be important in predicting outcomes across a wide range of health problems, including depression. In an evaluation of 139 hospital patients who interacted with the agent at the time of discharge, all patients, regardless of depressive symptoms, rated the agent very high on measures of satisfaction and ease of use, and most preferred receiving their discharge information from the agent compared to their doctors or nurses in the hospital. In addition, we found that patients with symptoms indicative of major depression rated the agent significantly higher on therapeutic alliance compared to patients who did not have major depressive symptoms. We conclude that empathic agents represent a promising technology for patient assessment, education and counseling for those most in need of comfort and caring in the inpatient setting.
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In the citing paper
“Previous studies have demonstrated that chatbot interventions improve psychological difficulties including major depressive disorder [15], panic disorder [16], posttraumatic stress disorder [17], antipsychotic medication adherence [18], and perceived stress [19].”
From A Cognitive Behavioral Therapy Chatbot (Otis) for Health Anxiety Management: Mixed Methods Pilot Study (2022) · cites this paper as [15]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Designing for certain character traits, such as empathy, in a chatbot can increase the likelihood of users establishing relationships with it (Bickmore et al., 2010).”
From My Chatbot Companion - a Study of Human-Chatbot Relationships (2021) · cites this paper as Bickmore et al., 2010
In the citing paper
“In one example, patients reported high satisfaction with a conversational agent that delivered hospital discharge instructions, and patients with depression and/or low health literacy reported stronger therapeutic alliance with the agent.”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]
In the citing paper
“The agent also elicited more questions from those with lower health literacy, suggesting such agents may have the potential to provide health information in a supportive, non-threatening manner (57).”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]
In the citing paper
“The agent also elicited more questions from those with lower health literacy, suggesting such agents may have the potential to provide health information in a supportive, non-threatening manner (57).”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]
In the citing paper
“In one example, patients reported high satisfaction with a conversational agent that delivered hospital discharge instructions, and patients with depression and/or low health literacy reported stronger therapeutic alliance with the agent.”
From Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health (2013) · cites this paper as [57]
In the citing paper
“The study for symptoms of depression noted that the written chatbot showed an accuracy of 99.2% and a specificity of 99.7% [52].”
From A systematic review on healthcare artificial intelligent conversational agents for chronic conditions (2022) · cites this paper as [52]
In the citing paper
“Other use of conversational agents include replacing nurses to provide hospital discharge planning, for collecting relevant health information (such as public health surveys), for training health-care providers by simulating patients, and for providing public health information and education.3,5–9”
From Ethical implications of conversational agents in global public health (2020) · cites this paper as [7]
In the citing paper
“Thus, users may experience less anxiety when discussing private concerns, such as disease exposure risk behaviours, with a conversational agent than they would with another person.3,4,7”
From Ethical implications of conversational agents in global public health (2020) · cites this paper as [7]
In the citing paper
“conversational agents (ie, computer programs that imitate interaction with human beings) have shown promising results with respect to patient satisfaction [15]”
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Metadata
- Authors
- Bickmore, T. W., Mitchell, S. E., Jack, B. W., Paasche-Orlow, M. K., Pfeifer, L. M., & O’Donnell, J.
- Affiliations
- Northeastern University (US)
- Boston University (US)
- Corresponding author
- Bickmore, Timothy W.
- Venue
- Interacting with Computers
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-07
- Volume
- 22
- Issue
- 4
- Pages
- 289-298
- ISSN
- 0953-5438 (ISSN-L)
- PMC
- PMC2901553
- OpenAlex
- W1989452694
- Cited by
- 218
- References
- 62
- Citations by year
- 2026: 17 · 2025: 22 · 2024: 23 · 2023: 23 · 2022: 27 · 2021: 28 · 2020: 20 · 2019: 24 · 2018: 5 · 2017: 9 · 2016: 3 · 2015: 4 · 2014: 4 · 2013: 6 · 2012: 2
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Interacting with Computers (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R01HL081307)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
- Keywords
- chatbots, depression, depressive symptoms, therapeutic relationship, discharge planning, patient satisfaction, ease of use, health education, inpatient care, patient-provider relationship
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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