A Conversational Interface for Self-screening for ADHD in Adults

Håvik, Robin; Wake, Jo Dugstad; Flobak, Eivind; Lundervold, Astri J.; Guribye, Frode

doi:10.1007/978-3-030-17705-8_12

Cited by 13 publications

(17 citation statements)

References 24 publications

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“…There were a wide variety of areas of health care targeted by the conversational agents of the included studies. The largest proportion of them (12/31, 39%) addressed mental health issues [ 13 , 32 - 42 ], with 19% (6/31) providing some form of clinical decision or triage support [ 8 , 12 , 40 , 42 - 44 ] and treatment support (including encouraging users to get screened) [ 9 , 45 - 49 ], 10% (3/31) being used to support training of health care students [ 15 , 41 , 50 ] and the screening or diagnosis of users [ 14 , 38 , 51 ], 7% (2/31) targeting physical health [ 52 , 53 ] and layperson medical education [ 54 , 55 ]; 1 agent was designed to help monitor users’ speech [ 56 ]. The percentages do not add up to 100% because some of the studies that addressed mental health also fit into one of the other categories.…”

Section: Resultsmentioning

confidence: 99%

“…Feedback from users in 5 studies expressed a preference for interactivity, with users in 1 study noting that they liked the interactivity of the chatbot [ 35 , 37 ], and users in the other 4 studies expressed a desire for greater interactivity or relational skills in the conversational agent [ 14 , 32 , 34 , 53 ]. Similarly, users in 4 studies reported liking that the agent had a personality and/or showed empathy [ 13 , 32 , 34 , 42 ], whereas users in other studies reported disliking the lack of personal connection or had difficulty in empathizing with the agent [ 35 , 37 , 50 ] or reported disliking its limited conversation and responses [ 35 , 56 ].…”

Section: Resultsmentioning

confidence: 99%

“…There was opposite feedback in 2 studies about the format of the response, with users preferring preformatted options for one chatbot [ 36 ], whereas some users preferred the free-text responses for a diagnostic chatbot because it allowed them to provide contextual information. In contrast, others found it more difficult to know how to respond so the agent would understand [ 14 ].…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, it is possible that these studies would perform better when assessed by a tool specific to their study type. Tables depicting the judgments for each question of the CASP cohort and qualitative checklists and the AXIS tool for the cross-sectional and other studies are included in Multimedia Appendices 6 - 9 [ 8 , 12 , 14 , 15 , 32 , 33 , 35 , 36 , 38 - 45 , 48 , 50 - 52 , 54 - 56 ].…”

Section: Resultsmentioning

confidence: 99%

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The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

Milne‐Ives¹,

Cock²,

Lim³

et al. 2020

J Med Internet Res

297

196

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Background The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption. Objective This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents. Methods PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another. Results A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback. Conclusions The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents. International Registered Report Identifier (IRRID) RR2-10.2196/16934

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

Milne‐Ives¹,

Cock²,

Lim³

et al. 2020

J Med Internet Res

297

196

View full text Add to dashboard Cite

show abstract

“…Conversational agents have been developed for many different aspects of the health sector to support health care professionals and the general public. Specific uses include screening for health conditions, triage, counseling, at-home health management support, and training for health care professionals [8,[13][14][15]. With phone, mobile, and online platforms being widely accessible, conversational agents can support populations with limited access to health care or poor health literacy [16,17].…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (Preprint)

Milne‐Ives¹,

Cock²,

Lim³

et al. 2020

Preprint

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BACKGROUND High demand on healthcare services and the growing capability of artificial intelligence has led to the development of conversational agents designed to support a variety of health-related activities - including behaviour change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase accessibility to healthcare services for the general public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in healthcare is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption. OBJECTIVE This systematic review aimed to assess the effectiveness and usability of conversational agents in healthcare and identify the elements that users like and dislike, to inform future research and development of these agents. METHODS PubMed, Medline (Ovid), EMBASE, CINAHL, Web of Science, and ACM Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in healthcare. Endnote (version X9; Clarivate Analytics) reference management software was used for initial screening, then full-text screening was conducted by one reviewer. Data was extracted and risk of bias was assessed by one reviewer and validated by another. RESULTS A total of 31 studies were selected and included a variety of conversational agents - 14 chatbots (two of which were voice chatbots), 6 embodied conversational agents, 3 each of interactive voice response calls, virtual patients, and speech recognition screening systems, as well as one contextual question answering agent and one voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31) and positive or mixed effectiveness was found in three quarters of the studies (23/30), but there were several limitations of the agents highlighted in specific qualitative feedback. CONCLUSIONS The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting is necessary to more accurately evaluate the usefulness of the agents in healthcare and identify key areas for improvement. Further research should also analyse the cost-effectiveness, privacy, and security of the agents. CLINICALTRIAL N/A INTERNATIONAL REGISTERED REPORT RR2-10.2196/16934

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Designing Chatbots for Guiding Online Peer Support Conversations for Adults with ADHD

Nordberg

Wake

Nordby

et al. 2020

Chatbot Research and Design

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A Conversational Interface for Self-screening for ADHD in Adults

Cited by 13 publications

References 24 publications

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (Preprint)

Designing Chatbots for Guiding Online Peer Support Conversations for Adults with ADHD

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