SAFE.AI: Developing and Testing an AI-based Hybrid Chatbot for Financial Empowerment in Rural Cancer Care

May 5, 2026 updated by: University of Utah
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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Interventional

Enrollment (Estimated)

60

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • Utah
      • Salt Lake City, Utah, United States, 84112
        • Huntsman Cancer Institute/ University of Utah
        • Contact:
        • Principal Investigator:
          • Djin Tay, PhD, RN

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Adults (18 years and older)
  • Cancer patients or financially responsible caregivers of cancer patients who:
  • Reside in the Huntsman Cancer Institute's five-state catchment area (Utah, Idaho, Wyoming, Montana, and Nevada),
  • Are able to read and write in English, and
  • Live in a rural area, defined by endorsement of a residential ZIP code classified as non-metropolitan (RUCA codes 4-10) per the USDA Rural-Urban Commuting Area Codes.

Exclusion Criteria:

  • Respondents who do not live within this 5-state region--The SAFE toolkit material was developed for the Huntsman Cancer Institute patient population which serves UT, ID, MT, WY, & NV
  • Respondents who only speak Spanish or other exclusively non-English speaking groups--The large language model for the chatbot will be developed in the English language. As non-English language training for the chatbot is not part of the scope of this study, participants who are unable to read and write in English may not be appropriate. As such, we will not be recruiting participants who only speak Spanish or other exclusively non-English speaking groups. Future studies will include adaptation of the chatbot to other languages.
  • Caregivers who are not primarily responsible for the financial aspects of patients' cancer care--The topic of financial hardship of cancer care may be less relevant for caregivers who are not financially involved in care.
  • Non-rural dwelling

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Supportive Care
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: Rule-based Chatbot
Participants randomized to this arm will be asked to use the rule-based chatbot.
Other: Rule-Based 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.
Other: Hybrid Chatbot
Participants randomized to this arm will be asked to use the hybrid chatbot
Other: Hybrid Chatbot

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Single-Item Helpfulness Rating
Time Frame: up to 2 weeks
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.
up to 2 weeks
System Usability Scale (SUS)
Time Frame: up to 2 weeks
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.
up to 2 weeks
Acceptability of Intervention Measure (AIM)
Time Frame: up to 2 weeks
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.
up to 2 weeks
Chatbot User Satisfaction (CUS) - Satisfaction Subscale
Time Frame: up to 2 weeks
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.
up to 2 weeks
Trust in Automated Systems Test (TOAST)
Time Frame: up to 2 weeks
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.
up to 2 weeks

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Self-reported Financial Worry
Time Frame: up to 2 weeks

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.
up to 2 weeks
Health-Related Quality of Life (QOL)
Time Frame: up to 2 weeks

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.
up to 2 weeks
Self-Efficacy for Coping with Cancer
Time Frame: up to 2 weeks

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.
up to 2 weeks
Psychosocial Distress
Time Frame: up to 2 weeks

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.
up to 2 weeks
User Engagement with SAFE.AI
Time Frame: up to 2 weeks
User engagement with the SAFE.AI will be assessed using an engagement instrument developed for evaluating AI chatbots in a posttest survey.
up to 2 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Utah

Collaborators

Huntsman Cancer Institute

Investigators

  • Principal Investigator: Djin Tay, PhD, RN, Huntsman Cancer Institute/ University of Utah

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 1, 2026

Primary Completion (Estimated)

December 1, 2027

Study Completion (Estimated)

December 1, 2027

Study Registration Dates

First Submitted

December 31, 2025

First Submitted That Met QC Criteria

February 12, 2026

First Posted (Actual)

February 13, 2026

Study Record Updates

Last Update Posted (Actual)

May 6, 2026

Last Update Submitted That Met QC Criteria

May 5, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • HCI193501

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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