Outpatient CareNCT07641478
A Large Language Model in Outpatient Care
A study testing Large Language Model Based Tool and Workflow Support for Large Language Model Tool Integration in people with Outpatient Care, run by Tsinghua University.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Outpatient Care
- What is being tested?
- Large Language Model Based Tool and Workflow Support for Large Language Model Tool Integration
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 3,500 participants
- Where
- No sites listed yet
- Study dates
- 12 Jun 2026to 4 Sep 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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
Show all 9Show fewer
- 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
Who cannot join
- Patients:
- Psychiatric conditions, unstable vital signs, or other medical situations considered unsuitable for AI-based interaction
- Declines to provide informed consent
About this study
Official title: 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?
Read more
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
- Conditions
- Outpatient Care
- Being tested
- Large Language Model Based Tool, Workflow Support for Large Language Model Tool Integration
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large Language Model Based ToolOther
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 IntegrationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Standard Outpatient Care (No AI) | No intervention | Neither doctors nor patients use a large language model based tool. Outpatient consultations and documentation are conducted following routine clinical practice. |
| Outpatient Care With a Large Language Model Tool | Experimental | 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. Receives: Other: Large Language Model Based Tool |
| Outpatient Care With a Large Language Model Tool and Workflow Support | Experimental | 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. Receives: Other: Workflow Support for Large Language Model Tool Integration |
What is being measured
The primary outcome is the main question the study is designed to answer.
Duration of the Outpatient Consultation
Time of the outpatient consultation, measured in milliseconds
Measured: 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.
Measured: 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.
Measured: Immediately after the consultation
Secondary outcomes (9)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Immediately after the consultation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jun 2026
- Study start12 Jun 2026 (estimated)
- Primary completion4 Sep 2026 (estimated)
- Study completion4 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Outpatient CareStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- This is a cluster-randomized crossover trial in which the unit of randomization is the doctor-session (i.e., an individual outpatient doctor on a given clinic day). Each participating doctor is assigned, in randomized order, to each of the three study conditions across multiple clinic sessions, so that every doctor contributes sessions to all three groups (crossover at the doctor level). Patients are nested within doctor-sessions. Each patient is allocated to a single study group, determined by the group assignment of the doctor-session in which their visit occurs, and therefore receives only one condition (no crossover at the patient level). At the time of registering for a clinic appointment, patients are not aware of which group their chosen doctor's session has been assigned to; this becomes known only after registration.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 3,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Tsinghua University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Tien Yin Wong, Tsinghua University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07641478
- Sponsor's protocol ID
- THU-01-2026-0055
- Status last verified
- Jun 2026
- First submitted
- 29 May 2026
- Last updated
- 11 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Randomized Controlled Trial of a Large Language Model in Outpatient Care
- ClinicalTrials.gov ID
- NCT07641478
- Acronym / sponsor ID
- THU-01-2026-0055
- Conditions
- Outpatient Care
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 3,500 participants (estimated)
- Lead sponsor
- Tsinghua UniversityUniversity, hospital or other
- Study start
- 12 Jun 2026 (estimated)
- Primary completion
- 4 Sep 2026 (estimated)
- Study completion
- 4 Sep 2026 (estimated)
- First posted
- 11 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 3
- Model description
- This is a cluster-randomized crossover trial in which the unit of randomization is the doctor-session (i.e., an individual outpatient doctor on a given clinic day). Each participating doctor is assigned, in randomized order, to each of the three study conditions across multiple clinic sessions, so that every doctor contributes sessions to all three groups (crossover at the doctor level). Patients are nested within doctor-sessions. Each patient is allocated to a single study group, determined by the group assignment of the doctor-session in which their visit occurs, and therefore receives only one condition (no crossover at the patient level). At the time of registering for a clinic appointment, patients are not aware of which group their chosen doctor's session has been assigned to; this becomes known only after registration.
| Arm | Type | Receives |
|---|---|---|
| Standard Outpatient Care (No AI) | No intervention | –Neither doctors nor patients use a large language model based tool. Outpatient consultations and documentation are conducted following routine clinical practice. |
| Outpatient Care With a Large Language Model Tool | Experimental | Other: Large Language Model Based ToolBefore 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. |
| Outpatient Care With a Large Language Model Tool and Workflow Support | Experimental | Other: Workflow Support for Large Language Model Tool IntegrationBefore 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (9 listed)
- 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
- Age 18 years or older
- Attending an outpatient consultation with a participating doctor
Key exclusion criteria (3 listed)
- Psychiatric conditions, unstable vital signs, or other medical situations considered unsuitable for AI-based interaction
- Declines to provide informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large Language Model Based ToolA large language model based tool is introduced into the outpatient consultation workflow to support the consultation and documentation process. | Other | Outpatient Care With a Large Language Model Tool |
| Workflow Support for Large Language Model Tool IntegrationAdditional 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. | Other | Outpatient Care With a Large Language Model Tool and Workflow Support |
Primary outcome
Duration of the Outpatient Consultation
Time frame: During the outpatient visit
Time of the outpatient consultation, measured in milliseconds
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.
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.
9 secondary outcomes
Doctor-Reported Efficiency of Obtaining Patient Information
Time frame: Immediately after the consultation
Doctor-Reported Cognitive Effort in Clinical Decision-Making
Time frame: Immediately after the consultation
Doctor-Reported Burden of Clinical Documentation
Time frame: Immediately after the consultation
Doctor's Intention to Continue Using the Tool
Time frame: Within 1 week after the participating doctor completes all enrolled consultations
Patient Trust in the Physician
Time frame: Immediately after the consultation
Patient Satisfaction With the Visit
Time frame: Immediately after the consultation
Patient-Perceived Physician Attentiveness
Time frame: Immediately after the consultation
Patient Satisfaction With the AI Pre-Consultation (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)
Time frame: Immediately after the consultation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Jun 2026.
Related trials for Outpatient Care
Not yet recruiting
Multimodal Stewardship Programme to Improve Antibiotic Prescribing for Children With Lower Respiratory Tract Infections
Bronchitis, Acute, Community-acquired Pneumonia
Recruiting
Bring BPaL2Me Trial Comparing Nurse-Led RR-TB Treatment to Physician-Led RR-TB Treatment
Drug Resistant Tuberculosis
Not yet recruitingObservational study
Community-Based Intensive Treatment (CBIT): Meeting the Complex Needs of Rural Adults With Serious Mental Illness
Severe Mental Illness, Hospitalizations Psychiatric
Active, not recruiting
Group-based Stabilization and Skill Training for Patients With Lasting Posttraumatic Reactions in Mental Health Care
Stress Disorders, Post-Traumatic, Mental Disorders
Recruiting
MORPH-A - Multidisciplinary Obesity Rehabilitation & Psychophysical Health and Awareness
Obesity
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Large Language Model Based Tool and Workflow Support for Large Language Model Tool Integration in people with Outpatient Care, run by Tsinghua University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 9 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 11 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Duration of the Outpatient Consultation (time frame: During the outpatient visit). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Jun 2026.