PDF(Pubmed)
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.
BACKGROUND: ClinicalTrials.gov NCT05789901; https://classic.clinicaltrials.gov/ct2/show/NCT05789901.
UNASSIGNED: PRR1-10.2196/54668.
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.
BACKGROUND: ClinicalTrials.gov NCT05789901; https://classic.clinicaltrials.gov/ct2/show/NCT05789901.
UNASSIGNED: PRR1-10.2196/54668.
摘要：
背景：基于人工智能（AI）的聊天机器人可以帮助解决患者在获取自我健康管理所必需的信息时面临的一些挑战，包括不可靠的来源和负担过重的医疗保健专业人员。研究以确保正确的设计，实施，和吸收聊天机器人是势在必行的。包容性数字健康研究和负责任的AI整合到医疗保健中需要患者和利益相关者的积极和持续参与。然而，为此目的，相应的活动和指导是有限的。
目标：作为回应，这份手稿为开发提供了一个主协议，测试,并与利益相关者合作实施聊天机器人家族。该协议旨在帮助有效地将初始聊天机器人干预（MARVIN）转换为多个健康领域和人群。
方法：MARVIN聊天机器人研究具有自适应平台试验设计，由多个并行的个体聊天机器人子组件组成，具有四个共同目标：（1）为特定的医疗保健环境共同构建量身定制的AI聊天机器人，(2)用小样本的参与者评估其可用性，(3)衡量实施成果(可用性、可接受性,适当性，收养,和保真度)在大样本中，（4）评估患者和利益相关者伙伴关系对聊天机器人开发的影响。对于目标1，将在设定范围内进行需求评估，包括四个2小时的焦点小组，每个小组有5名参与者。然后，将与患者合作伙伴组成一个共建设计委员会，卫生保健专业人员,以及将参加6个聊天机器人开发研讨会的研究人员，测试,和改进。对于目标2，共有30名参与者将与原型互动3周，并通过调查和3个焦点小组评估其可用性。积极的可用性结果将导致目标3的启动，即公众将能够访问聊天机器人进行为期12个月的真实世界实施研究，使用基于网络的问卷来衡量可用性。可接受性,以及150名参与者和元使用数据的适当性，以告知采用和保真度。在每个目标之后，对于目标4，将与设计委员会一起进行焦点小组，以更好地了解他们对参与过程的看法。
结果：从2022年7月到2023年10月，这个主方案导致了在麦吉尔大学健康中心或蒙特利尔大学医院中心进行的四项研究（均在蒙特利尔，魁北克,加拿大):(1)艾滋病毒防治(预计在2024年中大规模实施)，（2）为社区药剂师提供艾滋病毒护理的MARVIN-Pharma（计划于2024年中期进行可用性研究），（3）MARVINA治疗乳腺癌，和（4）MARVIN-CHAMP用于儿科感染状况（两者都在准备中，开发将于2024年初开始)。
结论：这个主协议提供了一种与患者和医疗保健专业人员合作开发聊天机器人的方法，包括对实施结果的全面评估。它还有助于为患者和利益相关者参与数字健康研究提供最佳实践建议。
背景：ClinicalTrials.govNCT05789901;https://classic.clinicaltrials.gov/ct2/show/NCT05789901.
■PRR1-10.2196/54668。
目标：作为回应，这份手稿为开发提供了一个主协议，测试,并与利益相关者合作实施聊天机器人家族。该协议旨在帮助有效地将初始聊天机器人干预（MARVIN）转换为多个健康领域和人群。
方法：MARVIN聊天机器人研究具有自适应平台试验设计，由多个并行的个体聊天机器人子组件组成，具有四个共同目标：（1）为特定的医疗保健环境共同构建量身定制的AI聊天机器人，(2)用小样本的参与者评估其可用性，(3)衡量实施成果(可用性、可接受性,适当性，收养,和保真度)在大样本中，（4）评估患者和利益相关者伙伴关系对聊天机器人开发的影响。对于目标1，将在设定范围内进行需求评估，包括四个2小时的焦点小组，每个小组有5名参与者。然后，将与患者合作伙伴组成一个共建设计委员会，卫生保健专业人员,以及将参加6个聊天机器人开发研讨会的研究人员，测试,和改进。对于目标2，共有30名参与者将与原型互动3周，并通过调查和3个焦点小组评估其可用性。积极的可用性结果将导致目标3的启动，即公众将能够访问聊天机器人进行为期12个月的真实世界实施研究，使用基于网络的问卷来衡量可用性。可接受性,以及150名参与者和元使用数据的适当性，以告知采用和保真度。在每个目标之后，对于目标4，将与设计委员会一起进行焦点小组，以更好地了解他们对参与过程的看法。
结果：从2022年7月到2023年10月，这个主方案导致了在麦吉尔大学健康中心或蒙特利尔大学医院中心进行的四项研究（均在蒙特利尔，魁北克,加拿大):(1)艾滋病毒防治(预计在2024年中大规模实施)，（2）为社区药剂师提供艾滋病毒护理的MARVIN-Pharma（计划于2024年中期进行可用性研究），（3）MARVINA治疗乳腺癌，和（4）MARVIN-CHAMP用于儿科感染状况（两者都在准备中，开发将于2024年初开始)。
结论：这个主协议提供了一种与患者和医疗保健专业人员合作开发聊天机器人的方法，包括对实施结果的全面评估。它还有助于为患者和利益相关者参与数字健康研究提供最佳实践建议。
背景：ClinicalTrials.govNCT05789901;https://classic.clinicaltrials.gov/ct2/show/NCT05789901.
■PRR1-10.2196/54668。
