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Laymouna M, Ma Y, Lessard D, Engler K, Therrien R, Schuster T, Vicente S, Achiche S, El Haj MN, Lemire B, Kawaiah A, Lebouché B. Needs-Assessment for an Artificial Intelligence-Based Chatbot for Pharmacists in HIV Care: Results from a Knowledge-Attitudes-Practices Survey. Healthcare (Basel) 2024; 12:1661. [PMID: 39201222 PMCID: PMC11353819 DOI: 10.3390/healthcare12161661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2024] [Revised: 08/14/2024] [Accepted: 08/15/2024] [Indexed: 09/02/2024] Open
Abstract
BACKGROUND Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists in considering evidence-based needs. METHODS From December 2022 to December 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. RESULTS Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M = 3.74/6). They held largely favorable attitudes towards providing HIV care (M = 4.02/6). They reported a "little" involvement in the delivery of HIV care services (M = 2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M ≥ 4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the 'undecided' range (M = 4.34, M = 4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M = 5.6/7). CONCLUSION MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot's development seems vital for its future uptake and usability.
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Affiliation(s)
- Moustafa Laymouna
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H3S 1Z1, Canada; (M.L.)
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC H4A 3S5, Canada
| | - Yuanchao Ma
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Chronic Viral Illness Service, Division of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Department of Biomedical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada
| | - David Lessard
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Chronic Viral Illness Service, Division of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Kim Engler
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC H4A 3S5, Canada
| | - Rachel Therrien
- Department of Pharmacy and Chronic Viral Illness Service, Research Centre of the University of Montreal Hospital Centre, Montreal, QC H2X 0A9, Canada
| | - Tibor Schuster
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H3S 1Z1, Canada; (M.L.)
| | - Serge Vicente
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H3S 1Z1, Canada; (M.L.)
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Department of Mathematics and Statistics, University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Sofiane Achiche
- Department of Biomedical Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada
| | - Maria Nait El Haj
- Faculty of Pharmacy, University of Montreal, Montreal, QC H3C 3J7, Canada
| | - Benoît Lemire
- Chronic Viral Illness Service, Division of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Abdalwahab Kawaiah
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Chronic Viral Illness Service, Division of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Bertrand Lebouché
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H3S 1Z1, Canada; (M.L.)
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC H4A 3S5, Canada
- Chronic Viral Illness Service, Division of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, QC H4A 3J1, Canada
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Ni Z, Peng ML, Balakrishnan V, Tee V, Azwa I, Saifi R, Nelson LE, Vlahov D, Altice FL. Implementation of Chatbot Technology in Health Care: Protocol for a Bibliometric Analysis. JMIR Res Protoc 2024; 13:e54349. [PMID: 38228575 PMCID: PMC10905346 DOI: 10.2196/54349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/07/2023] [Accepted: 01/16/2024] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings. OBJECTIVE This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health. METHODS In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks. RESULTS The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024. CONCLUSIONS Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/54349.
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Affiliation(s)
- Zhao Ni
- School of Nursing, Yale University, Orange, CT, United States
- Center for Interdisciplinary Research on AIDS, Yale University, New Haven, CT, United States
| | - Mary L Peng
- Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, United States
| | - Vimala Balakrishnan
- Department of Information Systems, Faculty of Computer Science and Information Technology, Unversity of Malaya, Kuala Lumpur, Malaysia
| | - Vincent Tee
- Centre of Excellence for Research in AIDS, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Iskandar Azwa
- Centre of Excellence for Research in AIDS, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Infectious Disease Unit, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Rumana Saifi
- Centre of Excellence for Research in AIDS, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - LaRon E Nelson
- School of Nursing, Yale University, Orange, CT, United States
- Center for Interdisciplinary Research on AIDS, Yale University, New Haven, CT, United States
| | - David Vlahov
- School of Nursing, Yale University, Orange, CT, United States
- Center for Interdisciplinary Research on AIDS, Yale University, New Haven, CT, United States
| | - Frederick L Altice
- Center for Interdisciplinary Research on AIDS, Yale University, New Haven, CT, United States
- Centre of Excellence for Research in AIDS, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Section of Infectious Disease, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States
- Division of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, United States
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Wutz M, Hermes M, Winter V, Köberlein-Neu J. Factors Influencing the Acceptability, Acceptance, and Adoption of Conversational Agents in Health Care: Integrative Review. J Med Internet Res 2023; 25:e46548. [PMID: 37751279 PMCID: PMC10565637 DOI: 10.2196/46548] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 05/10/2023] [Accepted: 07/10/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Conversational agents (CAs), also known as chatbots, are digital dialog systems that enable people to have a text-based, speech-based, or nonverbal conversation with a computer or another machine based on natural language via an interface. The use of CAs offers new opportunities and various benefits for health care. However, they are not yet ubiquitous in daily practice. Nevertheless, research regarding the implementation of CAs in health care has grown tremendously in recent years. OBJECTIVE This review aims to present a synthesis of the factors that facilitate or hinder the implementation of CAs from the perspectives of patients and health care professionals. Specifically, it focuses on the early implementation outcomes of acceptability, acceptance, and adoption as cornerstones of later implementation success. METHODS We performed an integrative review. To identify relevant literature, a broad literature search was conducted in June 2021 with no date limits and using all fields in PubMed, Cochrane Library, Web of Science, LIVIVO, and PsycINFO. To keep the review current, another search was conducted in March 2022. To identify as many eligible primary sources as possible, we used a snowballing approach by searching reference lists and conducted a hand search. Factors influencing the acceptability, acceptance, and adoption of CAs in health care were coded through parallel deductive and inductive approaches, which were informed by current technology acceptance and adoption models. Finally, the factors were synthesized in a thematic map. RESULTS Overall, 76 studies were included in this review. We identified influencing factors related to 4 core Unified Theory of Acceptance and Use of Technology (UTAUT) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) factors (performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation), with most studies underlining the relevance of performance and effort expectancy. To meet the particularities of the health care context, we redefined the UTAUT2 factors social influence, habit, and price value. We identified 6 other influencing factors: perceived risk, trust, anthropomorphism, health issue, working alliance, and user characteristics. Overall, we identified 10 factors influencing acceptability, acceptance, and adoption among health care professionals (performance expectancy, effort expectancy, facilitating conditions, social influence, price value, perceived risk, trust, anthropomorphism, working alliance, and user characteristics) and 13 factors influencing acceptability, acceptance, and adoption among patients (additionally hedonic motivation, habit, and health issue). CONCLUSIONS This review shows manifold factors influencing the acceptability, acceptance, and adoption of CAs in health care. Knowledge of these factors is fundamental for implementation planning. Therefore, the findings of this review can serve as a basis for future studies to develop appropriate implementation strategies. Furthermore, this review provides an empirical test of current technology acceptance and adoption models and identifies areas where additional research is necessary. TRIAL REGISTRATION PROSPERO CRD42022343690; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=343690.
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Affiliation(s)
- Maximilian Wutz
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| | - Marius Hermes
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| | - Vera Winter
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| | - Juliane Köberlein-Neu
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
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Thirunavukarasu AJ, Hassan R, Mahmood S, Sanghera R, Barzangi K, El Mukashfi M, Shah S. Trialling a Large Language Model (ChatGPT) in General Practice With the Applied Knowledge Test: Observational Study Demonstrating Opportunities and Limitations in Primary Care. JMIR MEDICAL EDUCATION 2023; 9:e46599. [PMID: 37083633 PMCID: PMC10163403 DOI: 10.2196/46599] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND Large language models exhibiting human-level performance in specialized tasks are emerging; examples include Generative Pretrained Transformer 3.5, which underlies the processing of ChatGPT. Rigorous trials are required to understand the capabilities of emerging technology, so that innovation can be directed to benefit patients and practitioners. OBJECTIVE Here, we evaluated the strengths and weaknesses of ChatGPT in primary care using the Membership of the Royal College of General Practitioners Applied Knowledge Test (AKT) as a medium. METHODS AKT questions were sourced from a web-based question bank and 2 AKT practice papers. In total, 674 unique AKT questions were inputted to ChatGPT, with the model's answers recorded and compared to correct answers provided by the Royal College of General Practitioners. Each question was inputted twice in separate ChatGPT sessions, with answers on repeated trials compared to gauge consistency. Subject difficulty was gauged by referring to examiners' reports from 2018 to 2022. Novel explanations from ChatGPT-defined as information provided that was not inputted within the question or multiple answer choices-were recorded. Performance was analyzed with respect to subject, difficulty, question source, and novel model outputs to explore ChatGPT's strengths and weaknesses. RESULTS Average overall performance of ChatGPT was 60.17%, which is below the mean passing mark in the last 2 years (70.42%). Accuracy differed between sources (P=.04 and .06). ChatGPT's performance varied with subject category (P=.02 and .02), but variation did not correlate with difficulty (Spearman ρ=-0.241 and -0.238; P=.19 and .20). The proclivity of ChatGPT to provide novel explanations did not affect accuracy (P>.99 and .23). CONCLUSIONS Large language models are approaching human expert-level performance, although further development is required to match the performance of qualified primary care physicians in the AKT. Validated high-performance models may serve as assistants or autonomous clinical tools to ameliorate the general practice workforce crisis.
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Affiliation(s)
| | - Refaat Hassan
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Shathar Mahmood
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Rohan Sanghera
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Kara Barzangi
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | | | - Sachin Shah
- Attenborough Surgery, Bushey Medical Centre, Bushey, United Kingdom
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Rodriguez-Arrastia M, Martinez-Ortigosa A, Ruiz-Gonzalez C, Ropero-Padilla C, Roman P, Sanchez-Labraca N. Experiences and perceptions of final-year nursing students of using a chatbot in a simulated emergency situation: A qualitative study. J Nurs Manag 2022; 30:3874-3884. [PMID: 35411629 PMCID: PMC10084062 DOI: 10.1111/jonm.13630] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 02/15/2022] [Accepted: 04/04/2022] [Indexed: 12/30/2022]
Abstract
AIM The aim of this study is to explore the experiences and perceptions of final-year nursing students on the acceptability and feasibility of using a chatbot for clinical decision-making and patient safety. BACKGROUND The effective and inclusive use of new technologies such as conversational agents or chatbots could support nurses in increasing evidence-based care and decreasing low-quality services. METHODS A descriptive qualitative study was used through focus group interviews. The data analysis was conducted using a thematic analysis. RESULTS This study included 114 participants. After our data analysis, two main themes emerged: (i) experiences in the use of a chatbot service for clinical decision-making and and (ii) integrating conversational agents into the organizational safety culture. CONCLUSIONS The findings of our study provide preliminary support for the acceptability and feasibility of adopting SafeBot, a chatbot for clinical decision-making and patient safety. Our results revealed substantial recommendations for refining navigation, layout and content, as well as useful insights to support its acceptance in real nursing practice. IMPLICATIONS FOR NURSING MANAGEMENT Leaders and managers may well see artificial intelligence-based conversational agents like SafeBot as a potential solution in modern nursing practice for effective problem-solving resolution, innovative staffing and nursing care delivery models at the bedside and criteria for measuring and ensure quality and patient safety.
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Affiliation(s)
| | | | - Cristofer Ruiz-Gonzalez
- Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain
| | | | - Pablo Roman
- Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain.,Research Group CTS-451 Health Sciences, University of Almeria, Almeria, Spain.,Health Research Centre, University of Almeria, Almeria, Spain
| | - Nuria Sanchez-Labraca
- Department of Nursing Science, Physiotherapy and Medicine, University of Almeria, Almeria, Spain
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