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Yoon S, Tang H, Tan CM, Phang JK, Kwan YH, Low LL. Acceptability of Mobile App-Based Motivational Interviewing and Preferences for App Features to Support Self-Management in Patients With Type 2 Diabetes: Qualitative Study. JMIR Diabetes 2024; 9:e48310. [PMID: 38446526 PMCID: PMC10955395 DOI: 10.2196/48310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 11/05/2023] [Accepted: 01/28/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND Patients with type 2 diabetes mellitus (T2DM) experience multiple barriers to improving self-management. Evidence suggests that motivational interviewing (MI), a patient-centered communication method, can address patient barriers and promote healthy behavior. Despite the value of MI, existing MI studies predominantly used face-to-face or phone-based interventions. With the growing adoption of smartphones, automated MI techniques powered by artificial intelligence on mobile devices may offer effective motivational support to patients with T2DM. OBJECTIVE This study aimed to explore the perspectives of patients with T2DM on the acceptability of app-based MI in routine health care and collect their feedback on specific MI module features to inform our future intervention. METHODS We conducted semistructured interviews with patients with T2DM, recruited from public primary care clinics. All interviews were audio recorded and transcribed verbatim. Thematic analysis was conducted using NVivo. RESULTS In total, 33 patients with T2DM participated in the study. Participants saw MI as a mental reminder to increase motivation and a complementary care model conducive to self-reflection and behavior change. Yet, there was a sense of reluctance, mainly stemming from potential compromise of autonomy in self-care by the introduction of MI. Some participants felt confident in their ability to manage conditions independently, while others reported already making changes and preferred self-management at their own pace. Compared with in-person MI, app-based MI was viewed as offering a more relaxed atmosphere for open sharing without being judged by health care providers. However, participants questioned the lack of human touch, which could potentially undermine a patient-provider therapeutic relationship. To sustain motivation, participants suggested more features of an ongoing supportive nature such as the visualization of milestones, gamified challenges and incremental rewards according to achievements, tailored multimedia resources based on goals, and conversational tools that are interactive and empathic. CONCLUSIONS Our findings suggest the need for a hybrid model of intervention involving both app-based automated MI and human coaching. Patient feedback on specific app features will be incorporated into the module development and tested in a randomized controlled trial.
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Affiliation(s)
- Sungwon Yoon
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore
| | | | - Chao Min Tan
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore
| | - Jie Kie Phang
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore
| | - Yu Heng Kwan
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore
- Internal Medicine Residency, SingHealth Residency, Singapore, Singapore
| | - Lian Leng Low
- Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Centre for Population Health Research and Implementation, SingHealth Regional Health System, Singapore, Singapore
- Post-Acute and Continuing Care, Outram Community Hospital, Singapore, Singapore
- SingHealth Duke-NUS Family Medicine Academic Clinical Program, Singapore, Singapore
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Ding H, Simmich J, Vaezipour A, Andrews N, Russell T. Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review. J Am Med Inform Assoc 2024; 31:746-761. [PMID: 38070173 PMCID: PMC10873847 DOI: 10.1093/jamia/ocad222] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/04/2023] [Accepted: 11/24/2023] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVES Conversational agents (CAs) with emerging artificial intelligence present new opportunities to assist in health interventions but are difficult to evaluate, deterring their applications in the real world. We aimed to synthesize existing evidence and knowledge and outline an evaluation framework for CA interventions. MATERIALS AND METHODS We conducted a systematic scoping review to investigate designs and outcome measures used in the studies that evaluated CAs for health interventions. We then nested the results into an overarching digital health framework proposed by the World Health Organization (WHO). RESULTS The review included 81 studies evaluating CAs in experimental (n = 59), observational (n = 15) trials, and other research designs (n = 7). Most studies (n = 72, 89%) were published in the past 5 years. The proposed CA-evaluation framework includes 4 evaluation stages: (1) feasibility/usability, (2) efficacy, (3) effectiveness, and (4) implementation, aligning with WHO's stepwise evaluation strategy. Across these stages, this article presents the essential evidence of different study designs (n = 8), sample sizes, and main evaluation categories (n = 7) with subcategories (n = 40). The main evaluation categories included (1) functionality, (2) safety and information quality, (3) user experience, (4) clinical and health outcomes, (5) costs and cost benefits, (6) usage, adherence, and uptake, and (7) user characteristics for implementation research. Furthermore, the framework highlighted the essential evaluation areas (potential primary outcomes) and gaps across the evaluation stages. DISCUSSION AND CONCLUSION This review presents a new framework with practical design details to support the evaluation of CA interventions in healthcare research. PROTOCOL REGISTRATION The Open Science Framework (https://osf.io/9hq2v) on March 22, 2021.
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Affiliation(s)
- Hang Ding
- RECOVER Injury Research Centre, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, Australia
- STARS Education and Research Alliance, Surgical Treatment and Rehabilitation Service (STARS), The University of Queensland and Metro North Health, Brisbane, QLD, Australia
| | - Joshua Simmich
- RECOVER Injury Research Centre, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, Australia
- STARS Education and Research Alliance, Surgical Treatment and Rehabilitation Service (STARS), The University of Queensland and Metro North Health, Brisbane, QLD, Australia
| | - Atiyeh Vaezipour
- RECOVER Injury Research Centre, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, Australia
- STARS Education and Research Alliance, Surgical Treatment and Rehabilitation Service (STARS), The University of Queensland and Metro North Health, Brisbane, QLD, Australia
| | - Nicole Andrews
- RECOVER Injury Research Centre, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, Australia
- STARS Education and Research Alliance, Surgical Treatment and Rehabilitation Service (STARS), The University of Queensland and Metro North Health, Brisbane, QLD, Australia
- The Tess Cramond Pain and Research Centre, Metro North Hospital and Health Service, Brisbane, QLD, Australia
- The Occupational Therapy Department, The Royal Brisbane and Women’s Hospital, Metro North Hospital and Health Service, Brisbane, QLD, Australia
| | - Trevor Russell
- RECOVER Injury Research Centre, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, Australia
- STARS Education and Research Alliance, Surgical Treatment and Rehabilitation Service (STARS), The University of Queensland and Metro North Health, Brisbane, QLD, Australia
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Powell L, Nour R, Sleibi R, Al Suwaidi H, Zary N. Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation. JMIR Hum Factors 2023; 10:e43120. [PMID: 37290040 PMCID: PMC10760512 DOI: 10.2196/43120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/05/2023] [Accepted: 06/07/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Chatbots enable users to have humanlike conversations on various topics and can vary widely in complexity and functionality. An area of research priority in chatbots is democratizing chatbots to all, removing barriers to entry, such as financial ones, to help make chatbots a possibility for the wider global population to improve access to information, help reduce the digital divide between nations, and improve areas of public good (eg, health communication). Chatbots in this space may help create the potential for improved health outcomes, potentially alleviating some of the burdens on health care providers and systems to be the sole voices of outreach to public health. OBJECTIVE This study explored the feasibility of developing a chatbot using approaches that are accessible in low- and middle-resource settings, such as using technology that is low cost, can be developed by nonprogrammers, and can be deployed over social media platforms to reach the broadest-possible audience without the need for a specialized technical team. METHODS This study is presented in 2 parts. First, we detailed the design and development of a chatbot, VWise, including the resources used and development considerations for the conversational model. Next, we conducted a case study of 33 participants who engaged in a pilot with our chatbot. We explored the following 3 research questions: (1) Is it feasible to develop and implement a chatbot addressing a public health issue with only minimal resources? (2) What is the participants' experience with using the chatbot? (3) What kinds of measures of engagement are observed from using the chatbot? RESULTS A high level of engagement with the chatbot was demonstrated by the large number of participants who stayed with the conversation to its natural end (n=17, 52%), requested to see the free online resource, selected to view all information about a given concern, and returned to have a dialogue about a second concern (n=12, 36%). CONCLUSIONS This study explored the feasibility of and the design and development considerations for a chatbot, VWise. Our early findings from this initial pilot suggest that developing a functioning and low-cost chatbot is feasible, even in low-resource environments. Our results show that low-resource environments can enter the health communication chatbot space using readily available human and technical resources. However, despite these early indicators, many limitations exist in this study and further work with a larger sample size and greater diversity of participants is needed. This study represents early work on a chatbot in its virtual infancy. We hope this study will help provide those who feel chatbot access may be out of reach with a useful guide to enter this space, enabling more democratized access to chatbots for all.
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Affiliation(s)
- Leigh Powell
- Institute for Excellence in Health Professions Education, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Radwa Nour
- Institute for Excellence in Health Professions Education, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Randa Sleibi
- Institute for Excellence in Health Professions Education, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Hanan Al Suwaidi
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Nabil Zary
- Institute for Excellence in Health Professions Education, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
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Brown A, Kumar AT, Melamed O, Ahmed I, Wang YH, Deza A, Morcos M, Zhu L, Maslej M, Minian N, Sujaya V, Wolff J, Doggett O, Iantorno M, Ratto M, Selby P, Rose J. A Motivational Interviewing Chatbot With Generative Reflections for Increasing Readiness to Quit Smoking: Iterative Development Study. JMIR Ment Health 2023; 10:e49132. [PMID: 37847539 PMCID: PMC10618902 DOI: 10.2196/49132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND The motivational interviewing (MI) approach has been shown to help move ambivalent smokers toward the decision to quit smoking. There have been several attempts to broaden access to MI through text-based chatbots. These typically use scripted responses to client statements, but such nonspecific responses have been shown to reduce effectiveness. Recent advances in natural language processing provide a new way to create responses that are specific to a client's statements, using a generative language model. OBJECTIVE This study aimed to design, evolve, and measure the effectiveness of a chatbot system that can guide ambivalent people who smoke toward the decision to quit smoking with MI-style generative reflections. METHODS Over time, 4 different MI chatbot versions were evolved, and each version was tested with a separate group of ambivalent smokers. A total of 349 smokers were recruited through a web-based recruitment platform. The first chatbot version only asked questions without reflections on the answers. The second version asked the questions and provided reflections with an initial version of the reflection generator. The third version used an improved reflection generator, and the fourth version added extended interaction on some of the questions. Participants' readiness to quit was measured before the conversation and 1 week later using an 11-point scale that measured 3 attributes related to smoking cessation: readiness, confidence, and importance. The number of quit attempts made in the week before the conversation and the week after was surveyed; in addition, participants rated the perceived empathy of the chatbot. The main body of the conversation consists of 5 scripted questions, responses from participants, and (for 3 of the 4 versions) generated reflections. A pretrained transformer-based neural network was fine-tuned on examples of high-quality reflections to generate MI reflections. RESULTS The increase in average confidence using the nongenerative version was 1.0 (SD 2.0; P=.001), whereas for the 3 generative versions, the increases ranged from 1.2 to 1.3 (SD 2.0-2.3; P<.001). The extended conversation with improved generative reflections was the only version associated with a significant increase in average importance (0.7, SD 2.0; P<.001) and readiness (0.4, SD 1.7; P=.01). The enhanced reflection and extended conversations exhibited significantly better perceived empathy than the nongenerative conversation (P=.02 and P=.004, respectively). The number of quit attempts did not significantly change between the week before the conversation and the week after across all 4 conversations. CONCLUSIONS The results suggest that generative reflections increase the impact of a conversation on readiness to quit smoking 1 week later, although a significant portion of the impact seen so far can be achieved by only asking questions without the reflections. These results support further evolution of the chatbot conversation and can serve as a basis for comparison against more advanced versions.
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Affiliation(s)
- Andrew Brown
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Ash Tanuj Kumar
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Osnat Melamed
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - Imtihan Ahmed
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Yu Hao Wang
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Arnaud Deza
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Marc Morcos
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Leon Zhu
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Marta Maslej
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Nadia Minian
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Vidya Sujaya
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
| | - Jodi Wolff
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Olivia Doggett
- Faculty of Information, University of Toronto, Toronto, ON, Canada
| | - Mathew Iantorno
- Faculty of Information, University of Toronto, Toronto, ON, Canada
| | - Matt Ratto
- Faculty of Information, University of Toronto, Toronto, ON, Canada
| | - Peter Selby
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Jonathan Rose
- The Edward S Rogers Sr Department of Electrical & Computer Engineering, University of Toronto, Toronto, ON, Canada
- INTREPID Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
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He L, Balaji D, Wiers RW, Antheunis ML, Krahmer E. Effectiveness and Acceptability of Conversational Agents for Smoking Cessation: A Systematic Review and Meta-analysis. Nicotine Tob Res 2023; 25:1241-1250. [PMID: 36507916 PMCID: PMC10256885 DOI: 10.1093/ntr/ntac281] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 03/02/2024]
Abstract
INTRODUCTION Conversational agents (CAs; computer programs that use artificial intelligence to simulate a conversation with users through natural language) have evolved considerably in recent years to support healthcare by providing autonomous, interactive, and accessible services, making them potentially useful for supporting smoking cessation. We performed a systematic review and meta-analysis to provide an overarching evaluation of their effectiveness and acceptability to inform future development and adoption. AIMS AND METHODS PsycInfo, Web of Science, ACM Digital Library, IEEE Xplore, Medline, EMBASE, Communication and Mass Media Complete, and CINAHL Complete were searched for studies examining the use of CAs for smoking cessation. Data from eligible studies were extracted and used for random-effects meta-analyses. RESULTS The search yielded 1245 publications with 13 studies eligible for systematic review (total N = 8236) and six studies for random-effects meta-analyses. All studies reported positive effects on cessation-related outcomes. A meta-analysis with randomized controlled trials reporting on abstinence yielded a sample-weighted odds ratio of 1.66 (95% CI = 1.33% to 2.07%, p < .001), favoring CAs over comparison groups. A narrative synthesis of all included studies showed overall high acceptability, while some barriers were identified from user feedback. Overall, included studies were diverse in design with mixed quality, and evidence of publication bias was identified. A lack of theoretical foundations was noted, as well as a clear need for relational communication in future designs. CONCLUSIONS The effectiveness and acceptability of CAs for smoking cessation are promising. However, standardization of reporting and designing of the agents is warranted for a more comprehensive evaluation. IMPLICATIONS This is the first systematic review to provide insight into the use of CAs to support smoking cessation. Our findings demonstrated initial promise in the effectiveness and user acceptability of these agents. We also identified a lack of theoretical and methodological limitations to improve future study design and intervention delivery.
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Affiliation(s)
- Linwei He
- Department of Communication and Cognition, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
| | - Divyaa Balaji
- Amsterdam School for Communication Research, University of Amsterdam, Amsterdam, The Netherlands
| | - Reinout W Wiers
- Addiction Development and Psychopathology (ADAPT)-Lab, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Centre for Urban Mental Health, University of Amsterdam, Amsterdam, The Netherlands
| | - Marjolijn L Antheunis
- Department of Communication and Cognition, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
| | - Emiel Krahmer
- Department of Communication and Cognition, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, The Netherlands
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Guo N, Luk TT, Wu YS, Guo Z, Chu JCL, Cheung YTD, Chan CHH, Kwok TTO, Wong VYL, Wong CKH, Lee JJ, Kwok YK, Viswanath K, Lam TH, Wang MP. Effect of mobile interventions with nicotine replacement therapy sampling on long-term smoking cessation in community smokers: A pragmatic randomized clinical trial. Tob Induc Dis 2023; 21:44. [PMID: 36969982 PMCID: PMC10037427 DOI: 10.18332/tid/160168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/26/2022] [Accepted: 01/31/2023] [Indexed: 03/26/2023] Open
Abstract
INTRODUCTION Mobile interventions enable personalized behavioral support that could improve smoking cessation (SC) in smokers ready to quit. Scalable interventions, including unmotivated smokers, are needed. We evaluated the effect of personalized behavioral support through mobile interventions plus nicotine replacement therapy sampling (NRT-S) on SC in Hong Kong community smokers. METHODS A total of 664 adult daily cigarette smokers (74.4% male, 51.7% not ready to quit in 30 days) were proactively recruited from smoking hotspots and individually randomized (1:1) to the intervention and control groups (each, n=332). Both groups received brief advice and active referral to SC services. The intervention group received 1-week NRT-S at baseline and 12-week personalized behavioral support through SC advisor-delivered Instant Messaging (IM) and a fully automated chatbot. The control group received regular text messages regarding general health at a similar frequency. Primary outcomes were carbon monoxide-validated smoking abstinence at 6 and 12 months post-treatment initiation. Secondary outcomes included self-reported 7-day point-prevalence and 24-week continuous abstinence, quit attempts, smoking reduction, and SC service use at 6 and 12 months. RESULTS By intention-to-treat, the intervention group did not significantly increase validated abstinence at 6 months (3.9% vs 3.0%, OR=1.31; 95% CI: 0.57–3.04) and 12 months (5.4% vs 4.5%, OR=1.21; 95% CI: 0.60–2.45), as were self-reported 7-day point-prevalence abstinence, smoking reduction, and SC service use at 6 and 12 months. More participants in the intervention than control group made a quit attempt by 6 months (47.0% vs 38.0%, OR=1.45; 95% CI: 1.06–1.97). Intervention engagement rates were low, but engagement in IM alone or combined with chatbot showed higher abstinence at 6 months (adjusted odds ratios, AORs=4.71 and 8.95, both p<0.05). CONCLUSIONS Personalized behavioral support through mobile interventions plus NRT-S did not significantly improve abstinence in community smokers compared to text only messaging. The suboptimal intervention engagement needs to be addressed in future studies. TRIAL REGISTRATION ClinicalTrials.gov NCT04001972.
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Affiliation(s)
- Ningyuan Guo
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
- School of Nursing, The University of Hong Kong, Hong Kong, China
| | - Tzu Tsun Luk
- School of Nursing, The University of Hong Kong, Hong Kong, China
| | | | - Ziqiu Guo
- School of Nursing, The University of Hong Kong, Hong Kong, China
| | | | | | - Ching Han Helen Chan
- Tung Wah Group of Hospitals Integrated Centre on Smoking Cessation, Hong Kong, China
| | - Tyrone Tai On Kwok
- Technology-Enriched Learning Initiative, The University of Hong Kong, Hong Kong, China
| | - Victor Yiu Lun Wong
- Technology-Enriched Learning Initiative, The University of Hong Kong, Hong Kong, China
| | - Carlos King Ho Wong
- Department of Family Medicine and Primary Care, School of Clinical Medicine, The University of Hong Kong, Hong Kong, China
- Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Jung Jae Lee
- School of Nursing, The University of Hong Kong, Hong Kong, China
| | - Yu Kwong Kwok
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China
| | - Kasisomayajula Viswanath
- Center for Community-Based Research, Dana-Farber Cancer Institute, Boston, United States
- Department of Social and Behavioral Sciences, T.H. Chan School of Public Health, Harvard University, Boston, United States
| | - Tai Hing Lam
- School of Public Health, The University of Hong Kong, Hong Kong, China
| | - Man Ping Wang
- School of Nursing, The University of Hong Kong, Hong Kong, China
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Morrow E, Zidaru T, Ross F, Mason C, Patel KD, Ream M, Stockley R. Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Front Psychol 2023; 13:971044. [PMID: 36733854 PMCID: PMC9887144 DOI: 10.3389/fpsyg.2022.971044] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/05/2022] [Indexed: 01/18/2023] Open
Abstract
Background Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored. Objectives The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare? Materials and methods A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice. Results Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships. Conclusion There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships. Implications In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.
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Affiliation(s)
| | - Teodor Zidaru
- Department of Anthropology, London School of Economics and Political Sciences, London, United Kingdom
| | - Fiona Ross
- Faculty of Health, Science, Social Care and Education, Kingston University London, London, United Kingdom
| | - Cindy Mason
- Artificial Intelligence Researcher (Independent), Palo Alto, CA, United States
| | | | - Melissa Ream
- Kent Surrey Sussex Academic Health Science Network (AHSN) and the National AHSN Network Artificial Intelligence (AI) Initiative, Surrey, United Kingdom
| | - Rich Stockley
- Head of Research and Engagement, Surrey Heartlands Health and Care Partnership, Surrey, United Kingdom
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Kellogg KC, Sadeh-Sharvit S. Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians. Front Psychiatry 2022; 13:990370. [PMID: 36147984 PMCID: PMC9485594 DOI: 10.3389/fpsyt.2022.990370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for "pragmatic AI-augmentation" that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice-automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.
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Affiliation(s)
- Katherine C Kellogg
- Department of Work and Organization Studies, MIT Sloan School of Management, Cambridge, MA, United States
| | - Shiri Sadeh-Sharvit
- Eleos Health, Cambridge, MA, United States.,Center for M2Health, Palo Alto University, Palo Alto, CA, United States
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