PDF(Pubmed)
Abstract：
BACKGROUND: Health outcomes are significantly influenced by unmet social needs. Although screening for social needs has become common in health care settings, there is often poor linkage to resources after needs are identified. The structural barriers (eg, staffing, time, and space) to helping address social needs could be overcome by a technology-based solution.
OBJECTIVE: This study aims to present the design and evaluation of a chatbot, DAPHNE (Dialog-Based Assistant Platform for Healthcare and Needs Ecosystem), which screens for social needs and links patients and families to resources.
METHODS: This research used a three-stage study approach: (1) an end-user survey to understand unmet needs and perception toward chatbots, (2) iterative design with interdisciplinary stakeholder groups, and (3) a feasibility and usability assessment. In study 1, a web-based survey was conducted with low-income US resident households (n=201). Following that, in study 2, web-based sessions were held with an interdisciplinary group of stakeholders (n=10) using thematic and content analysis to inform the chatbot\'s design and development. Finally, in study 3, the assessment on feasibility and usability was completed via a mix of a web-based survey and focus group interviews following scenario-based usability testing with community health workers (family advocates; n=4) and social workers (n=9). We reported descriptive statistics and chi-square test results for the household survey. Content analysis and thematic analysis were used to analyze qualitative data. Usability score was descriptively reported.
RESULTS: Among the survey participants, employed and younger individuals reported a higher likelihood of using a chatbot to address social needs, in contrast to the oldest age group. Regarding designing the chatbot, the stakeholders emphasized the importance of provider-technology collaboration, inclusive conversational design, and user education. The participants found that the chatbot\'s capabilities met expectations and that the chatbot was easy to use (System Usability Scale score=72/100). However, there were common concerns about the accuracy of suggested resources, electronic health record integration, and trust with a chatbot.
CONCLUSIONS: Chatbots can provide personalized feedback for families to identify and meet social needs. Our study highlights the importance of user-centered iterative design and development of chatbots for social needs. Future research should examine the efficacy, cost-effectiveness, and scalability of chatbot interventions to address social needs.
OBJECTIVE: This study aims to present the design and evaluation of a chatbot, DAPHNE (Dialog-Based Assistant Platform for Healthcare and Needs Ecosystem), which screens for social needs and links patients and families to resources.
METHODS: This research used a three-stage study approach: (1) an end-user survey to understand unmet needs and perception toward chatbots, (2) iterative design with interdisciplinary stakeholder groups, and (3) a feasibility and usability assessment. In study 1, a web-based survey was conducted with low-income US resident households (n=201). Following that, in study 2, web-based sessions were held with an interdisciplinary group of stakeholders (n=10) using thematic and content analysis to inform the chatbot\'s design and development. Finally, in study 3, the assessment on feasibility and usability was completed via a mix of a web-based survey and focus group interviews following scenario-based usability testing with community health workers (family advocates; n=4) and social workers (n=9). We reported descriptive statistics and chi-square test results for the household survey. Content analysis and thematic analysis were used to analyze qualitative data. Usability score was descriptively reported.
RESULTS: Among the survey participants, employed and younger individuals reported a higher likelihood of using a chatbot to address social needs, in contrast to the oldest age group. Regarding designing the chatbot, the stakeholders emphasized the importance of provider-technology collaboration, inclusive conversational design, and user education. The participants found that the chatbot\'s capabilities met expectations and that the chatbot was easy to use (System Usability Scale score=72/100). However, there were common concerns about the accuracy of suggested resources, electronic health record integration, and trust with a chatbot.
CONCLUSIONS: Chatbots can provide personalized feedback for families to identify and meet social needs. Our study highlights the importance of user-centered iterative design and development of chatbots for social needs. Future research should examine the efficacy, cost-effectiveness, and scalability of chatbot interventions to address social needs.
摘要：
背景：健康结果受到未满足的社会需求的显著影响。尽管筛查社会需求在医疗机构中已变得很普遍，在确定需求后，与资源的联系往往很差。结构性障碍(例如，人员配备，时间,和空间)帮助解决社会需求可以通过基于技术的解决方案来克服。
目的：本研究旨在介绍聊天机器人的设计和评估，DAPHNE（基于对话的医疗保健和需求生态系统助理平台），筛选社会需求并将患者和家庭与资源联系起来。
方法：这项研究采用了三个阶段的研究方法：（1）最终用户调查，以了解未满足的需求和对聊天机器人的看法，(2)与跨学科利益相关者群体的迭代设计,(3)可行性和可用性评估。在研究1中，对美国低收入居民家庭（n=201）进行了基于网络的调查。在此之后,在研究2中，与一个跨学科的利益相关者小组（n=10）举行了基于网络的会议，使用主题和内容分析来告知chatbot的设计和开发。最后,在研究3中,对可行性和可用性的评估是通过基于网络的调查和焦点小组访谈的组合完成的,之后对社区卫生工作者(家庭倡导者;n=4)和社会工作者(n=9)进行基于情景的可用性测试.我们报告了家庭调查的描述性统计和卡方检验结果。采用内容分析和主题分析对定性数据进行分析。描述性报告可用性评分。
结果：在调查参与者中，受雇和年轻的个人报告说，使用聊天机器人来解决社会需求的可能性更高，与年龄最大的年龄组相比。关于设计聊天机器人，利益相关者强调了提供商与技术合作的重要性，包容性会话设计，和用户教育。参与者发现聊天机器人的功能符合预期，并且聊天机器人易于使用（系统可用性量表得分=72/100）。然而,人们普遍担心建议资源的准确性，电子健康记录集成，并信任聊天机器人。
结论：聊天机器人可以为家庭提供个性化的反馈，以识别和满足社会需求。我们的研究强调了以用户为中心的迭代设计和开发针对社交需求的聊天机器人的重要性。未来的研究应该检查疗效，成本效益，以及聊天机器人干预以满足社会需求的可扩展性。
目的：本研究旨在介绍聊天机器人的设计和评估，DAPHNE（基于对话的医疗保健和需求生态系统助理平台），筛选社会需求并将患者和家庭与资源联系起来。
方法：这项研究采用了三个阶段的研究方法：（1）最终用户调查，以了解未满足的需求和对聊天机器人的看法，(2)与跨学科利益相关者群体的迭代设计,(3)可行性和可用性评估。在研究1中，对美国低收入居民家庭（n=201）进行了基于网络的调查。在此之后,在研究2中，与一个跨学科的利益相关者小组（n=10）举行了基于网络的会议，使用主题和内容分析来告知chatbot的设计和开发。最后,在研究3中,对可行性和可用性的评估是通过基于网络的调查和焦点小组访谈的组合完成的,之后对社区卫生工作者(家庭倡导者;n=4)和社会工作者(n=9)进行基于情景的可用性测试.我们报告了家庭调查的描述性统计和卡方检验结果。采用内容分析和主题分析对定性数据进行分析。描述性报告可用性评分。
结果：在调查参与者中，受雇和年轻的个人报告说，使用聊天机器人来解决社会需求的可能性更高，与年龄最大的年龄组相比。关于设计聊天机器人，利益相关者强调了提供商与技术合作的重要性，包容性会话设计，和用户教育。参与者发现聊天机器人的功能符合预期，并且聊天机器人易于使用（系统可用性量表得分=72/100）。然而,人们普遍担心建议资源的准确性，电子健康记录集成，并信任聊天机器人。
结论：聊天机器人可以为家庭提供个性化的反馈，以识别和满足社会需求。我们的研究强调了以用户为中心的迭代设计和开发针对社交需求的聊天机器人的重要性。未来的研究应该检查疗效，成本效益，以及聊天机器人干预以满足社会需求的可扩展性。
