Evaluating Conversational Artificial Intelligence for Depression Management

NCT07105397 | Not Yet Recruiting

• 2 other identifiers: STUDY00000316ME-2024C1-36732
• Study Type: interventional
• Target: 130
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this clinical trial is to evaluate how a conversational method of collecting medical history affects patients' perceptions and experiences compared to clinical care as usual. This conversational AI intake system collects medical history information, can be completed by participants at home, and do not disrupt routine clinical care. The primary questions this study aims to answer are: 1\) Does conversational intake affect patients' perceptions of empathy during their clinical interactions? This will be a prospective study that follows a cohort of participants for four (4) months after engaging with the AI intake system. Because each participant serves as his/her own control, both comparators will be administered within-subject, and the order of exposure (AI intake vs. usual care) will be randomized to minimize sequence effects. After completing the AI intake method, participants will rate their experience, particularly in terms of empathy and compare it to their usual interactions with their own clinicians.

Conditions

Major Depressive Disorder (MDD)

Keywords

major depression; antidepressant; large language model; conversational agent; artificial intelligence; AI; NLP; major depressive disorder; medical record augmented generation; patient simulation; testbed

Outcome Measures

Primary Outcomes (1)

• Perceptions of empathy

  The primary outcome assesses the impact of intake methods (conversational AI vs. structured survey) on patients' perceptions of empathy. Patient empathy perceptions will be measured using the Jefferson Scale of Empathy (JSE), a validated instrument. A higher JSE score means higher perceptions of empathy. We hypothesize that patients interacting with the conversational AI will report higher perceived empathy scores compared to those using the structured survey. For evaluation of impact of comparators, the primary analysis will focus on impact of LLM and clinician encounters on patient perceived empathy. These data will be analyzed using paired comparisons (ANOVA), with randomized order of exposure accounted for in the design. At the power of 0.80, a small-to medium effect size (d = 0.3), and significance level of 0.05, a sample size of 90 participants is needed, with 112 participants needed with attrition. In order to support higher power, a total of 130 participants will be recruited.

  From enrollment up to 4 months after participation

Secondary Outcomes (2)

• Communication Accommodation

  From enrollment up to 4 months after participation

• Adherence to recommendations

  From enrollment up to 4 months after participation

Study Arms (1)

Conversational AI system vs Usual Care

EXPERIMENTAL

Participants complete medical history intake through an interactive conversational AI designed to support patient-centered, empathetic dialogue. Using large language models (LLM), the system interprets patient input, maintains context, and generates natural-language responses. A dialogue manager prioritizes medically relevant topics to support efficient data collection and reduce off-topic discussion. For safety, trained human monitors oversee conversations in real time and can intervene if risks such as self-harm arise. The AI intake is compared with patients' experiences with their clinicians through monthly follow-up questionnaires over four months. The study evaluates patients' ratings of empathy, communication quality, and engagement, not conversation content. Each participant serves as their own control, with AI intake and usual care compared within-subject and randomized by order of exposure.

Other: Conversational AI system vs Usual Care

Interventions

Conversational AI system vs Usual Care OTHER

Participants complete medical history intake through an interactive conversational AI designed to support patient-centered, empathetic dialogue. Using large language models (LLM), the system interprets patient input, maintains context, and generates natural-language responses. A dialogue manager prioritizes medically relevant topics to support efficient data collection and reduce off-topic discussion. For safety, trained human monitors oversee conversations in real time and can intervene if risks such as self-harm arise. The AI intake is compared with patients' experiences with their clinicians through monthly follow-up questionnaires over four months. The study evaluates patients' ratings of empathy, communication quality, and engagement, not conversation content. Each participant serves as their own control, with AI intake and usual care compared within-subject and randomized by order of exposure.

Conversational AI system vs Usual Care

Eligibility Criteria

• Age: 18 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

1. Participant is between 18 to 85 years old. 2. Participant has been, or are likely to be, diagnosed with moderate to severe Major Depressive Disorder without signs of bipolar depression. 3. Participant is not in active suicidal crisis and do not face imminent risk of suicide within the next 3 hours. 4. Participant is not pregnant or seeking to be pregnant. 5. Participant able to communicate in English on the Internet. 6. Participant must reside in the United States. 7. Participant has access to a mental health clinician, or the participant is willing to see study clinicians to help review the advice and medication adjustments recommended by the AI Intake System to the participant. 8. If the participant is seeing study clinicians, the participant must reside in a state where study clinicians are licensed. 9. Participant must be using a device (phone or computer) that is located in the United States.

Contact the study team to discuss eligibility requirements. They can help determine if this study is right for you.

Sponsors & Collaborators

• George Mason University: lead
• Patient-Centered Outcomes Research Institute: collaborator

Study Sites (1)

George Mason University

Fairfax, Virginia, 22030, United States

Location

MeSH Terms

Conditions

Depressive Disorder, Major

Condition Hierarchy (Ancestors)

Depressive Disorder; Mood Disorders; Mental Disorders

Central Study Contacts

Farrokh Alemi, PhD

CONTACT

7579459484; rapidai@gmu.edu

Kevin Lybarger, PhD

CONTACT

7579459484; rapidai@gmu.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: This will be a prospective study that follows a cohort of participants for four (4) months after engaging with the AI intake system. Each subject serves as his/her own control, both comparators will be administered within-subject, and the order of exposure (AI intake vs. usual care) will be randomized to minimize sequence effects.

Study Record Dates

• First Submitted: July 1, 2025
• First Posted: August 5, 2025
• Study Start: April 15, 2026
• Primary Completion (Estimated): April 15, 2028
• Study Completion (Estimated): June 30, 2028
• Last Updated: April 9, 2026

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, ANALYTIC CODE
• Time Frame: Data will be available by June 2029, following completion of the final research report, and will remain available for at least 7 years.
• Access Criteria: Qualified researchers may request access to de-identified individual participant data and supporting documents (including the protocol, statistical analysis plan, informed consent form, and analytic code) via the PCORI-designated repository. Access to certain data elements may require submission of a research proposal and a signed data use agreement.

Locations

US (1)