An interventional study of Large Language Model Based Tool and Workflow Support for Large Language Model Tool Integration in Outpatient Care, sponsored by Tsinghua University. Not yet recruiting. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-06-11.
Sponsored by Tsinghua University · Not applicable, Interventional, and Health services research
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
Tsinghua University is the lead sponsor of 24 studies on the registry; 15 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Doctors:
Patients:
Exclusion Criteria:
Patients:
Neither doctors nor patients use a large language model based tool. Outpatient consultations and documentation are conducted following routine clinical practice.
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
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
A large language model based tool is introduced into the outpatient consultation workflow to support the consultation and documentation process.
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.
Duration of the Outpatient Consultation
Time of the outpatient consultation, measured in milliseconds
Time frame: 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.
Time frame: 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.
Time frame: Immediately after the consultation
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: Immediately after the consultation
No study locations are listed for this record.
This study is not yet recruiting, as verified in Jun 2026. You cannot join it, but the record below documents what was studied.
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Tsinghua University