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BACKGROUND: Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field. OBJECTIVE: This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations. METHODS: A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis. RESULTS: The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts. CONCLUSIONS: Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.
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Atenção à Saúde , Humanos , Telemedicina , ComunicaçãoRESUMO
BACKGROUND: Artificial intelligence (AI)-based chatbots could help address some of the challenges patients face in acquiring information essential to their self-health management, including unreliable sources and overburdened health care professionals. Research to ensure the proper design, implementation, and uptake of chatbots is imperative. Inclusive digital health research and responsible AI integration into health care require active and sustained patient and stakeholder engagement, yet corresponding activities and guidance are limited for this purpose. OBJECTIVE: In response, this manuscript presents a master protocol for the development, testing, and implementation of a chatbot family in partnership with stakeholders. This protocol aims to help efficiently translate an initial chatbot intervention (MARVIN) to multiple health domains and populations. METHODS: The MARVIN chatbots study has an adaptive platform trial design consisting of multiple parallel individual chatbot substudies with four common objectives: (1) co-construct a tailored AI chatbot for a specific health care setting, (2) assess its usability with a small sample of participants, (3) measure implementation outcomes (usability, acceptability, appropriateness, adoption, and fidelity) within a large sample, and (4) evaluate the impact of patient and stakeholder partnerships on chatbot development. For objective 1, a needs assessment will be conducted within the setting, involving four 2-hour focus groups with 5 participants each. Then, a co-construction design committee will be formed with patient partners, health care professionals, and researchers who will participate in 6 workshops for chatbot development, testing, and improvement. For objective 2, a total of 30 participants will interact with the prototype for 3 weeks and assess its usability through a survey and 3 focus groups. Positive usability outcomes will lead to the initiation of objective 3, whereby the public will be able to access the chatbot for a 12-month real-world implementation study using web-based questionnaires to measure usability, acceptability, and appropriateness for 150 participants and meta-use data to inform adoption and fidelity. After each objective, for objective 4, focus groups will be conducted with the design committee to better understand their perspectives on the engagement process. RESULTS: From July 2022 to October 2023, this master protocol led to four substudies conducted at the McGill University Health Centre or the Centre hospitalier de l'Université de Montréal (both in Montreal, Quebec, Canada): (1) MARVIN for HIV (large-scale implementation expected in mid-2024), (2) MARVIN-Pharma for community pharmacists providing HIV care (usability study planned for mid-2024), (3) MARVINA for breast cancer, and (4) MARVIN-CHAMP for pediatric infectious conditions (both in preparation, with development to begin in early 2024). CONCLUSIONS: This master protocol offers an approach to chatbot development in partnership with patients and health care professionals that includes a comprehensive assessment of implementation outcomes. It also contributes to best practice recommendations for patient and stakeholder engagement in digital health research. TRIAL REGISTRATION: ClinicalTrials.gov NCT05789901; https://classic.clinicaltrials.gov/ct2/show/NCT05789901. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54668.
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BACKGROUND: Like other chronic viral illnesses, HIV infection necessitates consistent self-management and adherence to care and treatment, which in turn relies on optimal collaboration between patients and healthcare professionals (HCPs), including physicians, nurses, pharmacists, and clinical care coordinators. By providing people living with HIV (PLHIV) with access to their personal health information, educational material, and a communication channel with HCPs, a tailored patient portal could support their engagement in care. Our team intends to implement a patient portal in HIV-specialized clinics in Canada and France. We sought to understand the perceived risks and benefits among PLHIV and HCPs of patient portal use in HIV clinical care. METHODS: This qualitative study recruited PLHIV and HIV-specialized HCPs, through maximum variation sampling and purposeful sampling, respectively. Semi-structured focus group discussions (FGDs) were held separately with PLHIV and HCPs between August 2019 and January 2020. FGDs were recorded, transcribed, coded using NVivo 12 software, and analyzed using content analysis. RESULTS: A total of twenty-eight PLHIV participated in four FGDs, and thirty-one HCPs participated in six FGDs. PLHIV included eighteen men, nine women, and one person identifying as other; while, HCPs included ten men, twenty women, and one person identifying as other. A multi-disciplinary team of HCPs were included, involving physicians, nurses, pharmacists, social workers, and clinical coordinators. Participants identified five potential risks: (1) breach of confidentiality, (2) stress or uncertainty, (3) contribution to the digital divide, (4) dehumanization of care, and (5) increase in HCPs' workload. They also highlighted four main benefits of using a patient portal: (1) improvement in HIV self-management, (2) facilitation of patient visits, (3) responsiveness to patient preferences, and (4) fulfillment of current or evolving patient needs. CONCLUSION: PLHIV and HCPs identified both risks and benefits of using a patient portal in HIV care. By engaging stakeholders and understanding their perspectives, the configuration of a patient portal can be optimized for end-users and concerns may be mitigated during its implementation.