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.

Background: The COVID-19 pandemic has acted as a catalyst for the development and adoption of a broad range of remote monitoring technologies (RMTs) in health care delivery. It is important to demonstrate how these technologies were implemented during the early stages of this pandemic to identify their application and barriers to adoption, particularly among vulnerable populations. Objective: The purpose of this knowledge synthesis was to present the range of RMTs used in delivering care to patients with COVID-19 and to identify perceived benefits of and barriers to their use. The review placed a special emphasis on health equity considerations. Methods: A rapid review of published research was conducted using Embase, MEDLINE, and QxMD for records published from the inception of COVID-19 (December 2019) to July 6, 2020. Synthesis involved content analysis of reported benefits of and barriers to the use of RMTs when delivering health care to patients with COVID-19, in addition to health equity considerations. Results: Of 491 records identified, 48 publications that described 35 distinct RMTs were included in this review. RMTs included use of existing technologies (eg, videoconferencing) and development of new ones that have COVID-19–specific applications. Content analysis of perceived benefits generated 34 distinct codes describing advantages of RMTs, mapped to 10 themes overall. Further, 52 distinct codes describing barriers to use of RMTs were mapped to 18 themes. Prominent themes associated with perceived benefits included a lower burden of care (eg, for hospitals, health care practitioners; 28 records), reduced infection risk (n=33), and support for vulnerable populations (n=14). Prominent themes reflecting barriers to use of RMTs included equity-related barriers (eg, affordability of technology for users, poor internet connectivity, poor health literacy; n=16), the need for quality “best practice” guidelines for use of RMTs in clinical care (n=12), and the need for additional resources to develop and support new technologies (n=11). Overall, 23 of 48 records commented on equity characteristics that stratify health opportunities and outcomes, including general characteristics that vary over time (eg, age, comorbidities; n=17), place of residence (n=11), and socioeconomic status (n=7). Conclusions: Results of this rapid review highlight the breadth of RMTs being used to monitor and inform treatment of COVID-19, the potential benefits of using these technologies, and existing barriers to their use. Results can be used to prioritize further efforts in the implementation of RMTs (eg, developing “best practice” guidelines for use of RMTs and generating strategies to improve equitable access for marginalized populations).

Suboptimal adherence to antiretroviral therapy (ART) remains common. Patient-centered tools are needed to comprehensively assess adherence barriers in HIV clinical practice. Thus, we conducted a research synthesis to produce a conceptual framework for a new patient-reported outcome measure (PRO) for use in routine HIV care in Canada and France. A PRO’s conceptual framework graphically represents the concepts to be measured and the potential relationships between them. Towards ensuring the framework’s relevance to the target populations’ concerns, qualitative studies with HIV-positive adults on barriers to ART adherence in developed countries were synthesized with thematic analysis, attending to the cross-study prevalence and interrelationships of barrier themes. In March 2016, searches within Medline, PsychINFO, and Embase produced 5,284 records. Two reviewers determined the final sample (n = 41). Analysis generated three levels of ART adherence barrier themes. Twenty Level 2 themes and their component subthemes (Level 3) were organized into 6 higher-order themes (Level 1): Cognitive and emotional aspects (100% of studies contributing content -prevalence), Lifestyle factors (95%), Social and material context (95%), Characteristics of ART (90%), Health experience and state (73%), and Healthcare services and system (66%). As to interrelationships, study authors articulated relationships between all higher-order themes (Level 3). Linkages between Level 2 barrier themes showed great variability, from 21% to 95%. Overall, this synthesis contributes an exceptionally detailed conceptual framework and report of ART adherence barriers, applicable to a wide range of PLHIV. It suggests that a key to understanding many barriers is through their interconnections. It also identifies gaps in barrier research. Concerning the new PRO’s development, comprehensiveness will need to be weighed against other concerns (e.g., respondent burden) and the provision of barrier-specific guidance for clinically addressing its scores seems essential.

Background: We developed MARVIN, an artificial intelligence (AI)‐based chatbot that provides 24/7 expert‐validated information on self‐management‐related topics for people with HIV. This study assessed (1) the feasibility of using MARVIN, (2) its usability and acceptability, and (3) four usability subconstructs (perceived ease of use, perceived usefulness, attitude towards use, and behavioural intention to use).Methods: In a mixed‐methods study conducted at the McGill University Health Centre, enrolled participants were asked to have 20 conversations within 3 weeks with MARVIN on predetermined topics and to complete a usability questionnaire. Feasibility, usability, acceptability, and usability subconstructs were examined against predetermined success thresholds. Qualitatively, randomly selected participants were invited to semi‐structured focus groups/interviews to discuss their experiences with MARVIN. Barriers and facilitators were identified according to the four usability subconstructs.Results: From March 2021 to April 2022, 28 participants were surveyed after a 3‐week testing period, and nine were interviewed. Study retention was 70% (28/40). Mean usability exceeded the threshold (69.9/68), whereas mean acceptability was very close to target (23.8/24). Ratings of attitude towards MARVIN's use were positive (+14%), with the remaining subconstructs exceeding the target (5/7). Facilitators included MARVIN's reliable and useful real‐time information support, its easy accessibility, provision of convivial conversations, confidentiality, and perception as being emotionally safe. However, MARVIN's limited comprehension and the use of Facebook as an implementation platform were identified as barriers, along with the need for more conversation topics and new features (e.g., memorization).Conclusions: The study demonstrated MARVIN's global usability. Our findings show its potential for HIV self‐management and provide direction for further development.

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