A Large Language Model in Outpatient Care

A Prospective Randomized Controlled Trial of a Large Language Model in Outpatient Care

The goal of this clinical trial is to learn how the use of a large language model (LLM) based tool affects outpatient clinical care in adult patients attending general hospital outpatient clinics. The main questions it aims to answer are:

Does the use of an LLM-based tool affect the efficiency of outpatient visits? Does the use of an LLM-based tool affect the experience of doctors and patients during outpatient care?

Researchers will compare outpatient visits supported by an LLM-based tool to standard outpatient visits without such a tool, to see whether and how the tool influences the care process and the experiences of doctors and patients.

Participants will:

Take part in outpatient visits that may or may not involve an LLM-based tool, depending on their assigned group Complete a short questionnaire about their visit experience after the consultation

Study Overview

• Status: Not yet recruiting
• Conditions: Outpatient Care
• Intervention / Treatment: Other: Large Language Model Based Tool; Other: Workflow Support for Large Language Model Tool Integration

• Study Type: Interventional
• Enrollment (Estimated): 3500
• Phase: Not Applicable

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

Doctors:

1. Licensed physicians providing outpatient consultations at a participating study hospital
2. Expected to complete a sufficient number of outpatient clinic sessions during the study period
3. Provides written informed consent

Patients:

1. Age 18 years or older
2. Attending an outpatient consultation with a participating doctor
3. Able to interact with the tool using an internet-connected device such as a smartphone
4. Provides written informed consent

Exclusion Criteria:

Patients:

1. Psychiatric conditions, unstable vital signs, or other medical situations considered unsuitable for AI-based interaction
2. Declines to provide informed consent

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: Single

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Standard Outpatient Care (No AI); Neither doctors nor patients use a large language model based tool. Outpatient consultations and documentation are conducted following routine clinical practice.
Experimental: Outpatient Care With a Large Language Model Tool; Before the consultation, patients complete an AI-based pre-consultation interaction. During the visit, a large language model based tool is available to support the outpatient consultation process. Doctors may refer to the tool during the visit.	Other: Large Language Model Based Tool; A large language model based tool is introduced into the outpatient consultation workflow to support the consultation and documentation process.
Experimental: Outpatient Care With a Large Language Model Tool and Workflow Support; Before the consultation, patients complete an AI-based pre-consultation interaction. During the visit, a large language model based tool is used together with additional workflow support to integrate the tool's output into the outpatient consultation process.	Other: Workflow Support for Large Language Model Tool Integration; Additional workflow support is provided to integrate the output of the large language model based tool into the consultation process, approximating a more integrated deployment of the tool.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Duration of the Outpatient Consultation	Time of the outpatient consultation, measured in milliseconds	During the outpatient visit
Doctor-Reported Efficiency of the Consultation	Doctor's self-rated efficiency of the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high), with higher scores indicating higher perceived efficiency.	Immediately after the consultation
Doctor-Reported Satisfaction With the Consultation Process	Doctor's satisfaction with the consultation process, measured on a 5-point Likert scale (1 = very low to 5 = very high), with higher scores indicating higher satisfaction.	Immediately after the consultation

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Doctor-Reported Efficiency of Obtaining Patient Information	Doctor's self-rated efficiency in obtaining the patient's clinical information (such as symptoms, history, prior examinations) during the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate higher efficiency.	Immediately after the consultation
Doctor-Reported Cognitive Effort in Clinical Decision-Making	Doctor's self-rated cognitive effort invested in clinical decision-making during the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater effort.	Immediately after the consultation
Doctor-Reported Burden of Clinical Documentation	Doctor's self-rated burden of completing the outpatient medical record for the consultation, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater burden.	Immediately after the consultation
Doctor's Intention to Continue Using the Tool	Doctor's intention to continue using the large language model based tool in routine practice, measured on a 5-point Likert scale (1 = strongly unwilling to 5 = strongly willing); higher scores indicate stronger intention.	Within 1 week after the participating doctor completes all enrolled consultations
Patient Trust in the Physician	Patient's level of trust in the physician after the visit, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater trust.	Immediately after the consultation
Patient Satisfaction With the Visit	Patient's satisfaction with the visit, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater satisfaction.	Immediately after the consultation
Patient-Perceived Physician Attentiveness	Patient-perceived attentiveness of the physician during the visit, assessed by a multi-item measure and reported as a composite score on a 1-5 scale; higher scores indicate greater perceived attentiveness.	Immediately after the consultation
Patient Satisfaction With the AI Pre-Consultation (Arm 2 and Arm 3 )	Patient's satisfaction with the AI-based pre-consultation interaction, measured on a 5-point Likert scale (1 = very low to 5 = very high); higher scores indicate greater satisfaction. Assessed only in Arm 2 and Arm 3.	Immediately after the consultation
Patient's Intention to Use AI Pre-Consultation in the Future (Arm 2 and Arm 3)	Patient's intention to use AI-based pre-consultation again in the future, measured on a 5-point Likert scale (1 = strongly unwilling to 5 = strongly willing); higher scores indicate stronger intention. Assessed only in Arm 2 and Arm 3.	Immediately after the consultation

Collaborators and Investigators

• Sponsor: Tsinghua University

Study record dates

Study Major Dates

• Study Start (Estimated): June 12, 2026
• Primary Completion (Estimated): September 4, 2026
• Study Completion (Estimated): September 4, 2026

Study Registration Dates

• First Submitted: May 29, 2026
• First Submitted That Met QC Criteria: June 7, 2026
• First Posted (Actual): June 11, 2026

Study Record Updates

• Last Update Posted (Actual): June 11, 2026
• Last Update Submitted That Met QC Criteria: June 7, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Other Study ID Numbers: THU-01-2026-0055

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No