Skip to main content
OpenTrials
Not Yet Recruiting

NCT Number: NCT07641478

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

Not Yet Recruiting

Trial opening soon.

Get Notified

Key information

Conditions

Outpatient Care

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Doctors:

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

Patients:

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

Exclusion criteria

Patients:

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

Treatment and study plan

Large Language Model Based Tool

Other

A large language model based tool is introduced into the outpatient consultation workflow to support the consultation and documentation process.

Workflow Support for Large Language Model Tool Integration

Other

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.

Primary outcomes

  1. Duration of the Outpatient Consultation

    Time frame: During the outpatient visit

    Time of the outpatient consultation, measured in milliseconds

  2. Doctor-Reported Efficiency of the Consultation

    Time frame: Immediately after 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.

  3. Doctor-Reported Satisfaction With the Consultation Process

    Time frame: Immediately after the consultation

    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.

Secondary outcomes

  1. Doctor-Reported Efficiency of Obtaining Patient Information

    Time frame: Immediately after the consultation

    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.

  2. Doctor-Reported Cognitive Effort in Clinical Decision-Making

    Time frame: Immediately after the consultation

    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.

  3. Doctor-Reported Burden of Clinical Documentation

    Time frame: Immediately after the consultation

    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.

  4. Doctor's Intention to Continue Using the Tool

    Time frame: Within 1 week after the participating doctor completes all enrolled consultations

    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.

  5. Patient Trust in the Physician

    Time frame: Immediately after the consultation

    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.

  6. Patient Satisfaction With the Visit

    Time frame: Immediately after the consultation

    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.

  7. Patient-Perceived Physician Attentiveness

    Time frame: Immediately after the consultation

    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.

  8. Patient Satisfaction With the AI Pre-Consultation (Arm 2 and Arm 3 )

    Time frame: Immediately after the consultation

    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.

  9. Patient's Intention to Use AI Pre-Consultation in the Future (Arm 2 and Arm 3)

    Time frame: Immediately after the consultation

    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.

Sponsors and collaborators

Lead sponsor

Tsinghua University

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Jun 11, 2026
Registry last updated
Jun 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.