scholarly journals The professional representation of conversational agents for health care

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
A. Luke MacNeill ◽  
Lillian MacNeill ◽  
Shelley Doucet ◽  
Alison Luke
Keyword(s):  
Health Care ◽  
Conversational Agents ◽  
Professional Representation

scholarly journals Hybrid Ubiquitous Coaching With a Novel Combination of Mobile and Holographic Conversational Agents Targeting Adherence to Home Exercises: Four Design and Evaluation Studies (Preprint)

2020 ◽  
Author(s):  
Tobias Kowatsch ◽  
Kim-Morgaine Lohse ◽  
Valérie Erb ◽  
Leo Schittenhelm ◽  
Helen Galliker ◽  
...  
Keyword(s):  
Health Care ◽  
Augmented Reality ◽  
Working Alliance ◽  
Heart Diseases ◽  
Evaluation Studies ◽  
Relative Advantage ◽  
Adherence Rate ◽  
Future Research ◽  
Conversational Agents ◽  
Promising Tool

BACKGROUND Effective treatments for various conditions such as obesity, cardiac heart diseases, or low back pain require not only personal on-site coaching sessions by health care experts but also a significant amount of home exercises. However, nonadherence to home exercises is still a serious problem as it leads to increased costs due to prolonged treatments. OBJECTIVE To improve adherence to home exercises, we propose, implement, and assess the novel coaching concept of hybrid ubiquitous coaching (HUC). In HUC, health care experts are complemented by a conversational agent (CA) that delivers psychoeducation and personalized motivational messages via a smartphone, as well as real-time exercise support, monitoring, and feedback in a hands-free augmented reality environment. METHODS We applied HUC to the field of physiotherapy and conducted 4 design-and-evaluate loops with an interdisciplinary team to assess how HUC is perceived by patients and physiotherapists and whether HUC leads to treatment adherence. A first version of HUC was evaluated by 35 physiotherapy patients in a lab setting to identify patients’ perceptions of HUC. In addition, 11 physiotherapists were interviewed about HUC and assessed whether the CA could help them build up a working alliance with their patients. A second version was then tested by 15 patients in a within-subject experiment to identify the ability of HUC to address adherence and to build a working alliance between the patient and the CA. Finally, a 4-week n-of-1 trial was conducted with 1 patient to show one experience with HUC in depth and thereby potentially reveal real-world benefits and challenges. RESULTS Patients perceived HUC to be useful, easy to use, and enjoyable, preferred it to state-of-the-art approaches, and expressed their intentions to use it. Moreover, patients built a working alliance with the CA. Physiotherapists saw a relative advantage of HUC compared to current approaches but initially did not see the potential in terms of a working alliance, which changed after seeing the results of HUC in the field. Qualitative feedback from patients indicated that they enjoyed doing the exercise with an augmented reality–based CA and understood better how to do the exercise correctly with HUC. Moreover, physiotherapists highlighted that HUC would be helpful to use in the therapy process. The longitudinal field study resulted in an adherence rate of 92% (11/12 sessions; 330/360 repetitions; 33/36 sets) and a substantial increase in exercise accuracy during the 4 weeks. CONCLUSIONS The overall positive assessments from both patients and health care experts suggest that HUC is a promising tool to be applied in various disorders with a relevant set of home exercises. Future research, however, must implement a variety of exercises and test HUC with patients suffering from different disorders.

scholarly journals Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (Preprint)

2019 ◽  
Author(s):  
Lorainne Tudor Car ◽  
Dhakshenya Ardhithy Dhinagaran ◽  
Bhone Myint Kyaw ◽  
Tobias Kowatsch ◽  
Shafiq Joty ◽  
...  
Keyword(s):  
Health Care ◽  
Scoping Review ◽  
Literature Search ◽  
Care Service ◽  
Health Care Service ◽  
Improve Patient Care ◽  
Future Research ◽  
Free Text ◽  
Conversational Agents ◽  
Service Support

BACKGROUND Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. OBJECTIVE This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application. METHODS We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis. RESULTS The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education. CONCLUSIONS The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

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

2020 ◽  
Author(s):  
Madison Milne-Ives ◽  
Caroline de Cock ◽  
Ernest Lim ◽  
Melissa Harper Shehadeh ◽  
Nick de Pennington ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Care ◽  
Language Processing ◽  
Health Care Services ◽  
Interactive Voice Response ◽  
Future Research ◽  
Conversational Agents ◽  
Privacy And Security ◽  
Care Services

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 RR2-10.2196/16934

scholarly journals Trust Me, I’m a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test (Preprint)

2019 ◽  
Author(s):  
John Powell
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Health Informatics ◽  
Ethical Issues ◽  
Thought Experiment ◽  
Turing Test ◽  
Conversational Agents ◽  
The Social ◽  
Value Judgements ◽  
Social And Ethical Issues

UNSTRUCTURED Over the next decade, one issue which will dominate sociotechnical studies in health informatics is the extent to which the promise of artificial intelligence in health care will be realized, along with the social and ethical issues which accompany it. A useful thought experiment is the application of the Turing test to user-facing artificial intelligence systems in health care (such as chatbots or conversational agents). In this paper I argue that many medical decisions require value judgements and the doctor-patient relationship requires empathy and understanding to arrive at a shared decision, often handling large areas of uncertainty and balancing competing risks. Arguably, medicine requires wisdom more than intelligence, artificial or otherwise. Artificial intelligence therefore needs to supplement rather than replace medical professionals, and identifying the complementary positioning of artificial intelligence in medical consultation is a key challenge for the future. In health care, artificial intelligence needs to pass the implementation game, not the imitation game.

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

10.2196/20346 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e20346
Author(s):  
Madison Milne-Ives ◽  
Caroline de Cock ◽  
Ernest Lim ◽  
Melissa Harper Shehadeh ◽  
Nick de Pennington ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Care ◽  
Language Processing ◽  
Health Care Services ◽  
Interactive Voice Response ◽  
Future Research ◽  
Conversational Agents ◽  
Privacy And Security ◽  
Care Services

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

Virtual Patient Simulation Training in Graduate Dysphagia Management Education—A Research-Led Enhancement Targeting Development of Clinical Interviewing and Clinical Reasoning Skills

2016 ◽  
Vol 1 (13) ◽  
pp. 130-139
Author(s):  
Isaac Sia ◽  
Shivashankar Halan ◽  
Benjamin Lok ◽  
Michael A. Crary
Keyword(s):  
Health Care ◽  
Nursing Education ◽  
Clinical Reasoning ◽  
Clinical Education ◽  
Simulation Training ◽  
Virtual Patient ◽  
Conversational Agents ◽  
Clinical Interviewing ◽  
Reasoning Skills ◽  
Clinical Reasoning Skills

Access to patients for clinical education is a challenge for educators and students across all health care professions. Led by colleagues in medical and nursing education, health care educators have increasingly embraced new teaching technologies such as virtual patient (VP) simulation training to support certain aspects of clinical education. This paper presents an account of the systematic, multi-year, research-led incorporation of computer generated virtual humans (i.e., embodied conversational agents) in a dysphagia management course targeting development of clinical interviewing and clinical reasoning skills in graduate speech-language pathology students.

Clinical, Legal, and Ethical Aspects of Artificial Intelligence–Assisted Conversational Agents in Health Care

JAMA ◽  
2020 ◽  
Vol 324 (6) ◽  
pp. 552 ◽  
Author(s):  
John D. McGreevey ◽  
C. William Hanson ◽  
Ross Koppel
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Conversational Agents ◽  
Ethical Aspects

scholarly journals Hybrid Ubiquitous Coaching With a Novel Combination of Mobile and Holographic Conversational Agents Targeting Adherence to Home Exercises: Four Design and Evaluation Studies

10.2196/23612 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23612
Author(s):  
Tobias Kowatsch ◽  
Kim-Morgaine Lohse ◽  
Valérie Erb ◽  
Leo Schittenhelm ◽  
Helen Galliker ◽  
...  
Keyword(s):  
Health Care ◽  
Augmented Reality ◽  
Working Alliance ◽  
Heart Diseases ◽  
Evaluation Studies ◽  
Relative Advantage ◽  
Adherence Rate ◽  
Future Research ◽  
Conversational Agents ◽  
Promising Tool

Background Effective treatments for various conditions such as obesity, cardiac heart diseases, or low back pain require not only personal on-site coaching sessions by health care experts but also a significant amount of home exercises. However, nonadherence to home exercises is still a serious problem as it leads to increased costs due to prolonged treatments. Objective To improve adherence to home exercises, we propose, implement, and assess the novel coaching concept of hybrid ubiquitous coaching (HUC). In HUC, health care experts are complemented by a conversational agent (CA) that delivers psychoeducation and personalized motivational messages via a smartphone, as well as real-time exercise support, monitoring, and feedback in a hands-free augmented reality environment. Methods We applied HUC to the field of physiotherapy and conducted 4 design-and-evaluate loops with an interdisciplinary team to assess how HUC is perceived by patients and physiotherapists and whether HUC leads to treatment adherence. A first version of HUC was evaluated by 35 physiotherapy patients in a lab setting to identify patients’ perceptions of HUC. In addition, 11 physiotherapists were interviewed about HUC and assessed whether the CA could help them build up a working alliance with their patients. A second version was then tested by 15 patients in a within-subject experiment to identify the ability of HUC to address adherence and to build a working alliance between the patient and the CA. Finally, a 4-week n-of-1 trial was conducted with 1 patient to show one experience with HUC in depth and thereby potentially reveal real-world benefits and challenges. Results Patients perceived HUC to be useful, easy to use, and enjoyable, preferred it to state-of-the-art approaches, and expressed their intentions to use it. Moreover, patients built a working alliance with the CA. Physiotherapists saw a relative advantage of HUC compared to current approaches but initially did not see the potential in terms of a working alliance, which changed after seeing the results of HUC in the field. Qualitative feedback from patients indicated that they enjoyed doing the exercise with an augmented reality–based CA and understood better how to do the exercise correctly with HUC. Moreover, physiotherapists highlighted that HUC would be helpful to use in the therapy process. The longitudinal field study resulted in an adherence rate of 92% (11/12 sessions; 330/360 repetitions; 33/36 sets) and a substantial increase in exercise accuracy during the 4 weeks. Conclusions The overall positive assessments from both patients and health care experts suggest that HUC is a promising tool to be applied in various disorders with a relevant set of home exercises. Future research, however, must implement a variety of exercises and test HUC with patients suffering from different disorders.

scholarly journals Conversational Agents in Health Care: Scoping Review and Conceptual Analysis

10.2196/17158 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e17158 ◽  
Author(s):  
Lorainne Tudor Car ◽  
Dhakshenya Ardhithy Dhinagaran ◽  
Bhone Myint Kyaw ◽  
Tobias Kowatsch ◽  
Shafiq Joty ◽  
...  
Keyword(s):  
Health Care ◽  
Scoping Review ◽  
Literature Search ◽  
Care Service ◽  
Health Care Service ◽  
Improve Patient Care ◽  
Future Research ◽  
Free Text ◽  
Conversational Agents ◽  
Service Support

Background Conversational agents, also known as chatbots, are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including health care. By enabling better accessibility, personalization, and efficiency, conversational agents have the potential to improve patient care. Objective This study aimed to review the current applications, gaps, and challenges in the literature on conversational agents in health care and provide recommendations for their future research, design, and application. Methods We performed a scoping review. A broad literature search was performed in MEDLINE (Medical Literature Analysis and Retrieval System Online; Ovid), EMBASE (Excerpta Medica database; Ovid), PubMed, Scopus, and Cochrane Central with the search terms “conversational agents,” “conversational AI,” “chatbots,” and associated synonyms. We also searched the gray literature using sources such as the OCLC (Online Computer Library Center) WorldCat database and ResearchGate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by 2 reviewers. The included evidence was analyzed narratively by employing the principles of thematic analysis. Results The literature search yielded 47 study reports (45 articles and 2 ongoing clinical trials) that matched the inclusion criteria. The identified conversational agents were largely delivered via smartphone apps (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case studies describing chatbot development (n=18) were the most prevalent, and only 11 randomized controlled trials were identified. The 3 most commonly reported conversational agent applications in the literature were treatment and monitoring, health care service support, and patient education. Conclusions The literature on conversational agents in health care is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, artificial intelligence–driven, and smartphone app–delivered conversational agents. There is an urgent need for a robust evaluation of diverse health care conversational agents’ formats, focusing on their acceptability, safety, and effectiveness.

Sign in / Sign up

Export Citation Format

Share Document