An interventional study of Rule-Based Chatbot and Hybrid Chatbot in Cancer and Financial Toxicity, sponsored by University of Utah. Recruiting at 1 site in United States. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-25.
Sponsored by University of Utah · Not applicable, Interventional, and Supportive care
This is a randomized, two-arm, parallel-group pilot trial investigating a new chatbot tool designed to support cancer patients and caregivers, particularly those in rural communities. Approximately 60 participants will be randomized 1:1 to interact with either a hybrid chatbot or an AI-enabled chatbot. Participants will use their assigned chatbot to obtain clear and helpful information related to insurance, travel costs, and other financial aspects of cancer care.
Costs of cancer care will approach $246 billion by 2030, making cancer one of the most expensive health conditions for individuals. Cancer-related financial hardships negatively impacts psychological wellbeing, health-related quality of life, medication adherence, and decisions to delay or forgo care. Rural cancer patients and families have a higher prevalence of financial hardships, incur greater travel-related expenses, face unique employment and income stressors, and have lower access to specialized cancer care services and providers-- including those that support financial needs.
Few financial toxicity interventions are designed for the needs of rural cancer patients and families. While financial navigation can effectively reduce cancer patients' out-of-pocket costs, cancer programs' financial and rural patient navigation services, including at the Huntsman Cancer Institute (HCI), are overstrained. Most centers respond to financial hardships reactively rather than proactively, and programs are less equipped to assist with non-medical sources of cancer costs, such as travel and employment hardships.
To address this gap, Self-Advocacy for Financial Empowerment (SAFE) resource toolkit with a community advisory board consisting of rural cancer patients, caregivers, and healthcare stakeholders. Community-engaged research also identified the need for individualized and accessible information about financial resources and supports, stigma as a barrier to seeking help, and the time and resource-intensive nature of financial navigation that limits the penetration and reach of these essential services among rural communities impacted by cancer.
Chatbots, or conversational agents, are a type of artificial intelligence (AI) system that applies machine learning to reproduce realistic human conversations. Scripted chatbots, based on clearly defined information boundaries, offer accurate, reliable, and individualized responses to questions. Conversely, AI-based chatbots that use large language models (LLM) like GPT4, can address ambiguous, open-ended questions while continuing to preserve privacy. Chatbots facilitate individualized, chunked information that enhances complex information communication, promotes users' privacy and support needs, and addresses workforce challenges.[
GARDE-Chat, an open-source platform, has been established for health system-level risk assessment and genetic testing for hereditary cancer at HCI. GARDE-Chat supports scripted, hybrid, and AI-chatbots. Prior to this pilot test, GARDE-Chat will be used to create a chatbot designed to provide responses for financial toxicity, based on the SAFE toolkit content and verified resources to develop the scripted version of the chatbot. A large language model component of the chatbot will be incorporated for the hybrid version that will enable users to ask more complex and open-ended questions, refined with community stakeholder input.
This is a randomized, two-arm, parallel-group pilot trial investigating a new chatbot tool designed to support cancer patients and caregivers, particularly those in rural communities. Approximately 60 participants will be randomized 1:1 to interact with either a hybrid chatbot or an AI-enabled chatbot. Participants will use their assigned chatbot to obtain clear and helpful information related to insurance, travel costs, and other financial aspects of cancer care. Enrolled participants will complete three surveys (pretest, posttest, and 2-week follow-up) and interact with the chatbot prior to the posttest. Participants can interact with the chatbot between the posttest and the 2-week followup as they choose. Participants will share feedback on the usefulness, ease of use, and overall experience using the chatbot and complete pre-and posttest measures to assess preliminary efficacy for secondary outcome measures. All research activities will be done online.
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This study's planned enrollment of 60 is close to the median of 60 across 7,703 interventional studies indexed under Neoplasms.
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Exclusion Criteria:
Participants randomized to this arm will be asked to use the rule-based chatbot.
Other: Rule-Based Chatbot
Participants randomized to this arm will be asked to use the hybrid chatbot
Other: Hybrid Chatbot
The rule-based (scripted) SAFE.ai chatbot is a guided conversational tool built to provide structured, accurate, and consistent information to rural cancer patients and caregivers experiencing cancer-related financial toxicity. This chatbot is grounded in the Self-Advocacy for Financial Empowerment (SAFE) resource toolkit, which was co-developed with a community advisory board (CAB) composed of rural patients, caregivers, nurses, and financial navigation experts across HCI's five-state catchment area. All scripted responses reflect priorities identified during qualitative needs assessment sessions, ensuring that content is culturally aligned with rural patient experiences and real-world financial challenges. The chatbot follows a rule-based decision tree. Users progress through the conversation by selecting a response from a set of fixed options displayed on-screen. This ensures that all content is clinically vetted, safe, consistent, and aligned with evidence-based practices.
The hybrid SAFE.ai chatbot builds on the existing rule-based system by integrating a large language model (LLM) layer to support more flexible, open-ended, and conversational interactions. While the rule-based chatbot provides structured conversations through predefined content, the hybrid approach allows users to ask complex or personalized questions about financial toxicity. To ensure safety and accuracy, the hybrid chatbot is not allowed to generate responses from the open internet. By combining the consistency of rule-based logic with the adaptability of an LLM, the hybrid chatbot will enable users to ask follow-up questions, describe nuanced financial situations, request clarification in their own words, and receive more tailored guidance while still ensuring adherence to SAFE content.
Single-Item Helpfulness Rating
Helpfulness is assessed using a 5-point Likert-scale item with response options ranging from Very unhelpful (1) to Very helpful (5). Total scores range from a minumum of 1 to a maximum of 5, with lower scores indicating lower perceived helpfulness, and higher scores indicating greater perceived helpfulness.
Time frame: up to 2 weeks
System Usability Scale (SUS)
The SUS is a 10-item, 5 point Likert scale (0 = strongly disagree, 5 = strongly agree). Total scores range from a minimum of 0 to a maximum of 100, with lower values indicating less usability and higher values indicating higher usability.
Time frame: up to 2 weeks
Acceptability of Intervention Measure (AIM)
The AIM is a 4-item, 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Total scores range from a minimum of 4 a maximum of 20, with lower values indicating lower acceptability and higher values indicating higher acceptability.
Time frame: up to 2 weeks
Chatbot User Satisfaction (CUS) - Satisfaction Subscale
The Satisfaction subscale of the CUS measure is a 5 item Likert-scale ( 1 = strongly disagree, 5 = strongly agree). Total scores range from a minimum of 5 to a maximum of 25, with lower values indicating lower user satisfaction, and higher values indicating higher satisfaction.
Time frame: up to 2 weeks
Trust in Automated Systems Test (TOAST)
The TOAST is a 9-item 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Total scores range from a minimum of 9 to a maximum of 63, with lower values indicating lower trust, and higher values indicating higher trust.
Time frame: up to 2 weeks
Self-reported Financial Worry
Financial Worry will be assessed by the COST (COmprehensive Score for financial Toxicity) assessment. COST is a 12-item, 5-point Likert scale (0=Not at All to 4 =Very Much). Total scores range from a minimum of 0 to a maximum of 44, with lower scores indicating worse Financial Well-Being and higher scores indicating better Financial Well-Being. This outcome measure will report mean COST score.
Time frame: up to 2 weeks
Health-Related Quality of Life (QOL)
Health-related quality of life will be assessed with the 36-item short-form Health Survey (SF-36). The SF-36 has two subscales, the Physical Component Summary (PCS) and the Mental Component Summary (MCS). Each subscale ranges from 0-100, with lower scores indicating worse QOL and higher scores indicating better QOL. This outcome measure will report the mean PCS and MCS scores.
Time frame: up to 2 weeks
Self-Efficacy for Coping with Cancer
Self-efficacy for coping with cancer will be assessed with the Cancer Behavior Inventory-Brief Version (CBI-B) questionnaire. CBI-B is a 12-item, 9-point Likert scale (1=Not at All Confident to 9=Totally Confident). Total CBI-B scores range from 9 to 108, with lower scores indicating worse self-efficacy and higher scores indicating better self-efficacy.
Time frame: up to 2 weeks
Psychosocial Distress
Psychosocial distress will be measured by the Patient Health Questionnaire-4 (PHQ-4). PHQ-4 is a 4-item, 4-point Likert scale (0 = Not at All to 3 = Nearly every day). Total PHQ-4 scores range from 0 to 12, with lower scores indicating less psychosocial distress and higher scores indicating more severe psychosocial distress.
Time frame: up to 2 weeks
User Engagement with SAFE.AI
User engagement with the SAFE.AI will be assessed using an engagement instrument developed for evaluating AI chatbots in a posttest survey.
Time frame: up to 2 weeks
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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