Adapting and Evaluating an AI-Based Chatbot Through Patient and Stakeholder Engagement to Provide Information for Different Health Conditions: Master Protocol for an Adaptive Platform Trial (the MARVIN Chatbots Study)

Yuanchao Ma, Sofiane Achiche, Marie-Pascale Pomey, Jesseca Paquette, Nesrine Adjtoutah, Serge Vicente, Kim Engler, MARVIN chatbots Patient Expert Committee, Moustafa Laymouna, David Lessard, Benoît Lemire, Jamil Asselah, Rachel Therrien, Esli Osmanlliu, Ma'n H Zawati, Yann Joly, Bertrand Lebouché, Yuanchao Ma, Sofiane Achiche, Marie-Pascale Pomey, Jesseca Paquette, Nesrine Adjtoutah, Serge Vicente, Kim Engler, MARVIN chatbots Patient Expert Committee, Moustafa Laymouna, David Lessard, Benoît Lemire, Jamil Asselah, Rachel Therrien, Esli Osmanlliu, Ma'n H Zawati, Yann Joly, Bertrand Lebouché

Abstract

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.

Keywords: Canada; adaptive platform trial design; artificial intelligence; chatbot; co-construction; conversational agent; digital health; implementation science; master protocol; mobile phone; patient and stakeholder engagement; research ethics; self-management; telehealth.

Conflict of interest statement

Conflicts of Interest: B Lebouché has received research support, consulting fees, and speaker fees from ViiV Healthcare, Merck, and Gilead. The authors are developers of the MARVIN chatbot intervention.

©Yuanchao Ma, Sofiane Achiche, Marie-Pascale Pomey, Jesseca Paquette, Nesrine Adjtoutah, Serge Vicente, Kim Engler, MARVIN chatbots MARVIN chatbots Patient Expert Committee, Moustafa Laymouna, David Lessard, Benoît Lemire, Jamil Asselah, Rachel Therrien, Esli Osmanlliu, Ma'n H Zawati, Yann Joly, Bertrand Lebouché. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 13.02.2024.

Figures

Figure 1
Figure 1
Adaptive platform trial design without control group.
Figure 2
Figure 2
Study steps for each substudy.
Figure 3
Figure 3
Operation process of MARVIN.
Figure 4
Figure 4
Example of text processing in MARVIN.
Figure 5
Figure 5
Examples of MARVIN handling bad inputs: (A) fallback policy; (B) redirection after 2 failed attempts.
Figure 6
Figure 6
Conversation example with MARVIN.
Figure 7
Figure 7
MARVIN chatbot—message data flow diagram. API: application programming interface; AWS: Amazon Web Services.

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