Evaluating Conversational Artificial Intelligence for Depression Management

NCT ID: NCT07105397

Last Updated: 2025-08-05

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 130 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2026-04-15
• Study Completion Date: 2028-06-30

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 traditional online, closed-ended surveys. Both methods collect identical medical history information, can be completed by patients 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?

2. Does conversational intake strengthen the therapeutic bond patients feel toward their clinicians compared to traditional surveys?

Participants will be randomly assigned to one of two intake methods:

1. Conversational intake: Participants answer questions about their medical history through a natural, dialogue-based interface.

2. Closed-ended survey intake: Participants complete a structured, multiple-choice questionnaire about their medical history.

After completing their assigned intake method, participants will rate their experience, particularly in terms of empathy and therapeutic bond, and compare it to their usual interactions with their own clinicians.

Detailed Description

Conversational artificial intelligence (AI) systems, such as those based on Large Language Models (LLMs) like ChatGPT, offer innovative ways to engage patients in health-related conversations. Despite these advances, challenges remain regarding patient safety and system reliability. Specific concerns include biased recommendations against certain patient groups, inaccuracies or misleading responses, and mechanical, unempathic interactions, particularly during sensitive moments such as when patients express suicidal thoughts. Testing conversational AI in healthcare settings is complicated due to the diverse medical, linguistic, and behavioral characteristics exhibited by patients.

This study addresses these challenges by developing an advanced conversational AI system guided by a structured knowledge-based topic network to maintain conversation relevance and coherence. Additionally, the investigators introduce a novel patient simulator methodology that mimics diverse medical histories, linguistic styles, and behavioral interactions, enhancing pre-clinical testing rigor.

The research focuses specifically on the clinical context of depression management, aiming to optimize antidepressant selection. Currently, many patients undergo a frustrating and costly trial-and-error process to find effective antidepressants. The study compares two approaches designed to streamline and personalize this process:

1. Conversational AI Intake: Engages patients through flexible, open-ended dialogue to gather medical history and generate personalized antidepressant recommendations.

2. Structured Questionnaire Intake: Utilizes a closed-ended, multiple-choice format to systematically collect patient medical histories for antidepressant recommendation.

Both methods leverage a curated, evidence-based knowledgebase of 15 commonly used antidepressants, considering factors like patient age, gender, comorbidities, and previous antidepressant use. The accuracy and completeness of the AI-generated recommendations are rigorously verified in by clinicians prior to any medication changes, adhering to FDA safety requirements.

A primary goal of the project is to evaluate how conversational AI impacts patient-centered outcomes, specifically patient perceptions of empathy, therapeutic bond, and communication quality. Patients with major depressive disorder will be recruited online, enhancing participant diversity and representativeness. Participants will be randomly assigned to either the conversational AI or the structured questionnaire method. Outcomes will include differences in data completeness, patient perceptions of empathy, and strength of therapeutic alliance.

Beyond immediate clinical outcomes, the project's methodological advancements, particularly the development of robust, bias-mitigated conversational systems and comprehensive patient simulation for AI testing, will have broad applicability across healthcare domains. The conversational AI and patient simulator will be made publicly available at no cost, providing tools that other researchers, clinicians, and healthcare providers can utilize and adapt to various health contexts.

Patient and stakeholder engagement is integral to the study. A representative advisory board, including patients with lived experience of depression, clinicians, mental health advocates, and researchers, guides all phases of the project. This collaborative framework ensures that the research remains patient-centered and responsive to real-world clinical needs and experiences.

Conditions

Major Depressive Disorder (MDD)

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: HEALTH_SERVICES_RESEARCH
• Blinding Strategy: NONE

Study Groups

Structured Survey Questionnaire

Participants complete a structured, multiple-choice questionnaire designed to efficiently collect medical history. This intake method uses decision trees to sequentially select questions based on prior responses, maximizing information gain and minimizing unnecessary queries. The questionnaire systematically gathers patient demographics, depression history, and previous antidepressant use through closed-ended, mutually exclusive, and exhaustive response options.

Group Type: ACTIVE_COMPARATOR

Structured Survey Questionnaire

Intervention Type: OTHER

The structured questionnaire intake utilizes a predefined, multiple-choice survey that systematically collects patient medical history without conversational interaction. Questions are algorithmically selected based on the patient's earlier responses to maximize information efficiency, typically requiring fewer than 13 items. Patients select from standardized responses (including options like "I do not know" or "I do not want to say"). Contextual help messages are provided as needed. Upon completion, the system generates antidepressant recommendations using the same Antidepressant Knowledgebase as the Conversational AI system, clearly explaining its rationale. A prototype version is available at http://MeAgainMeds.com, with ongoing development to enhance features and usability.

Conversational AI system

Participants engage in medical history intake through an interactive conversational AI system designed to create patient-centered interactions. The system utilizes advanced Large Language Models (LLMs) to understand patient inputs, interpret context, and generate coherent, natural language responses with an empathetic tone. Within the conversational AI, a Dialogue Manager guides the conversation by prioritizing medically relevant topics, ensuring efficient data collection and minimizing off-topic dialogue. To enhance patient safety, conversations are continuously monitored in real-time by trained human-in-the-loop monitors, who can promptly intervene if potential safety risks, such as indications of self-harm, are identified. The primary intent of the conversational AI system is to streamline the antidepressant recommendation process, provide personalized patient interactions, and foster patient comfort and therapeutic alliance through empathetically toned responses.

Group Type: EXPERIMENTAL

Conversational AI system

Intervention Type: OTHER

The conversational AI intervention utilizes interactive dialogue to collect patient medical histories. Powered by advanced Large Language Models (LLMs), the system engages participants in open-ended, natural language conversations. A Dialogue Manager coordinates the dialogue, ensuring conversations remain medically relevant, personalized, and safe. Clinicians monitor the interactions in real-time, validating and ensuring accuracy of antidepressant recommendations informed by an evidence-based Antidepressant Knowledgebase.

Interventions

Conversational AI system

The conversational AI intervention utilizes interactive dialogue to collect patient medical histories. Powered by advanced Large Language Models (LLMs), the system engages participants in open-ended, natural language conversations. A Dialogue Manager coordinates the dialogue, ensuring conversations remain medically relevant, personalized, and safe. Clinicians monitor the interactions in real-time, validating and ensuring accuracy of antidepressant recommendations informed by an evidence-based Antidepressant Knowledgebase.

Intervention Type: OTHER

Structured Survey Questionnaire

The structured questionnaire intake utilizes a predefined, multiple-choice survey that systematically collects patient medical history without conversational interaction. Questions are algorithmically selected based on the patient's earlier responses to maximize information efficiency, typically requiring fewer than 13 items. Patients select from standardized responses (including options like "I do not know" or "I do not want to say"). Contextual help messages are provided as needed. Upon completion, the system generates antidepressant recommendations using the same Antidepressant Knowledgebase as the Conversational AI system, clearly explaining its rationale. A prototype version is available at http://MeAgainMeds.com, with ongoing development to enhance features and usability.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• 18 - 85 years old
• have a major depression diagnosis from a clinician and score 10 or higher on the Patient Health Questionnaire (PHQ-9)
• reside in a state where study clinicians are licensed
• have access to the Internet via phone or computer
• have no language, sensorial, or cognitive barriers to providing written informed consent
• must have a primary care provider, a mental health specialist, or agree to see a study clinician

Exclusion Criteria

• has clinically diagnosed bipolar disorder

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 85 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Patient-Centered Outcomes Research Institute

OTHER

Sponsor Role: collaborator

George Mason University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

George Mason University

Fairfax, Virginia, United States

Countries

United States

Central Contacts

Farrok Alemi, PhD

Role: CONTACT

fa3277@gmu.edu

703-893-3799

Kevin Lybarger, PhD

Role: CONTACT

klybarge@gmu.edu

206-491-6309

Facility Contacts

Farrokh Alemi, PhD

Role: primary

fa3277@gmu.edu

5712016859

Kevin Lybarger, PhD

Role: backup

klybarge@gmu.edu

206-491-6309

Other Identifiers

ME-2024C1-36732

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

STUDY00000316

Identifier Type: -

Identifier Source: org_study_id