Clinical Decision Support SystemsNCT07760051
Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission
A study testing Agent-assisted workflow, LLM-assisted workflow and Traditional Workflow in people with Clinical Decision Support Systems and Diagnostic Reasoning, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
Trial summary
The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Clinical Decision Support Systems, Diagnostic Reasoning, Artificial Intelligence (AI)
- What is being tested?
- Agent-assisted workflow, LLM-assisted workflow and Traditional Workflow
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 180 participants
- Where
- 1 siteChina
- Study dates
- 6 Sep 2026to Dec 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
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Hold a Medical Practitioner Qualification Certificate and/or Medical License, or be a recognized standardized resident physician; able to independently read electronic medical records, laboratory and imaging reports on an HIS.
- Currently engaged in clinical work in internal medicine or surgery at one of the 15 participating hospitals.
- Able to complete the case assessment in one continuous hour without breaks.
- Able to participate remotely under video proctoring, with a stable internet connection and a working camera.
- Voluntarily agree to participate and sign the informed consent form, including the declaration not to use unauthorized AI tools during the assessment.
- Have not participated in case drafting, review, rubric development, or any activity that may leak the reference standard.
Who cannot join
- Have previously accessed the official test cases or reference standard of this study.
- Unable to complete the training module, qualification test, or all experimental tasks.
- Have conflicts of interest, e.g. participation in developing core algorithms of the tested system.
- Unwilling to comply with remote proctoring, including keeping the camera on throughout.
- Judged unsuitable by the investigators.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
2nd Affiliated Hospital, School of Medicine, Zhejiang University
Hangzhou, Zhejiang, 310009
Human Subject Research Ethics Committee, +86 0571 87783759, keyanlunli_zheer@163.com
About this study
Official title: Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission: A Randomized Controlled Trial
The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system.
The main questions it aims to answer are:
Read more
- Does the Agent-assisted workflow yield better structured admission diagnosis and management planning scores than standalone LLM assistance?
- Does the Agent-assisted workflow outperform the traditional workflow without AI tools? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the Agent tool can improve diagnostic accuracy and efficiency.
The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system.
The main questions it aims to answer are:
- Does the Agent-assisted workflow yield better diagnosis and management planning scores than the LLM-assisted workflow?
- Does the Agent-assisted workflow yield better diagnosis and management planning scores than the conventional workflow without AI assistance? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the agent tool can improve diagnostic and management planning performance and efficiency.
Participants will:
- Be recruited from 15 hospitals in China and participate remotely under video proctoring
- Be randomly assigned to one of the three fixed workflows, with randomization stratified by hospital tier, specialty and seniority
- Work through 6 anonymized simulated HIS admission cases and complete as many as they can.
- Submit structured answers for each case covering principal diagnosis, secondary diagnoses, differential diagnoses, diagnostic justification, next diagnostic or therapeutic steps, consultation and referral decisions, and diagnostic confidence.
- Have their operation logs and time consumption recorded automatically by the study platform.
- Complete a post-session questionnaire after submitting all cases.
- Being tested
- Agent-assisted workflow, LLM-assisted workflow, Traditional Workflow
- Keywords
- AI agent, Large Language Model, Clinical Decision Support, Admission Diagnosis, Diagnostic Accuracy, Diagnostic Reasoning, Randomized Controlled Trial, Management Planning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Agent-assisted workflowOther
Conventional resources (the pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources) plus an in-system Agent entry that automatically reads the full record and report images, produces a structured summary with source-text tracing, and supports multi-turn Q\&A and one-click editable drafts.
LLM-assisted workflowOther
Conventional resources (the pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources) plus an in-system multi-turn AI dialogue entry. The AI does not automatically read the record; participants paste text or send partial screenshots.
Traditional WorkflowOther
Conventional resources only: the pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources. No AI assistance.
| Study group | Role | What they receive |
|---|---|---|
| Agent-assisted group | Experimental | Not described Receives: Other: Agent-assisted workflow |
| LLM-assisted group | Experimental | Not described Receives: Other: LLM-assisted workflow |
| Traditional group | Active comparator | Not described Receives: Other: Traditional Workflow |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mean Normalized Structured Score
Mean of the rescaled case scores (each case rescaled to 100), divided by the number of cases completed; range 0 to 100.
Measured: Within one-hour study
Secondary outcomes (2)
Active Response Time per Case
Active response time in seconds for each case, recorded automatically by the platform.
Measured: Within one-hour study
Degree of Adherence to AI-Generated Recommendations
Degree to which the submitted answer incorporates AI output. Assessed in the Agent-assisted group and LLM-assisted group only.
Measured: Within one-hour study
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Aug 2026
- Study start6 Sep 2026
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Clinical Decision Support SystemsStudies of the same condition, open and under way.
- More trials run by Second Affiliated Hospital, Zhejiang University, School of MedicineThe sponsor's other registered studies.
- 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
- Parallel groups
- Model description
- Three-arm parallel-group design with 1:1:1 allocation. All arms share the same conventional resources (pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources); the arms differ only in the AI entry added on top (none / LLM / Agent). Randomization is stratified by hospital, specialty and seniority.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 180 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Shangrao People's Hospital
- Responsible party
- Sponsor
- Investigators
- Yuan Ding, Study chair, Second Affiliated Hospital, Zhejiang University, School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07760051
- Sponsor's protocol ID
- 2026-0977
- Status last verified
- Sep 2026
- First submitted
- 29 Jul 2026
- Last updated
- 28 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission: A Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07760051
- Acronym / sponsor ID
- 2026-0977
- Keywords
- AI agent, Large Language Model, Clinical Decision Support, Admission Diagnosis, Diagnostic Accuracy, Diagnostic Reasoning, Randomized Controlled Trial, Management Planning
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 180 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Collaborators
- Shangrao People's Hospital
- Study start
- 6 Sep 2026 (actual)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 12 Aug 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 evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system.
The main questions it aims to answer are:
- Does the Agent-assisted workflow yield better structured admission diagnosis and management planning scores than standalone LLM assistance?
- Does the Agent-assisted workflow outperform the traditional workflow without AI tools? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the Agent tool can improve diagnostic accuracy and efficiency.
The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis and management planning performance on standardized simulated inpatient cases, among practicing internal medicine and surgery physicians across all seniority levels and across three tiers of the Chinese healthcare system.
The main questions it aims to answer are:
- Does the Agent-assisted workflow yield better diagnosis and management planning scores than the LLM-assisted workflow?
- Does the Agent-assisted workflow yield better diagnosis and management planning scores than the conventional workflow without AI assistance? Researchers will compare three parallel groups (traditional workflow group, LLM-assisted group, Agent-assisted group) to determine whether the agent tool can improve diagnostic and management planning performance and efficiency.
Participants will:
- Be recruited from 15 hospitals in China and participate remotely under video proctoring
- Be randomly assigned to one of the three fixed workflows, with randomization stratified by hospital tier, specialty and seniority
- Work through 6 anonymized simulated HIS admission cases and complete as many as they can.
- Submit structured answers for each case covering principal diagnosis, secondary diagnoses, differential diagnoses, diagnostic justification, next diagnostic or therapeutic steps, consultation and referral decisions, and diagnostic confidence.
- Have their operation logs and time consumption recorded automatically by the study platform.
- Complete a post-session questionnaire after submitting all cases.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 3
- Model description
- Three-arm parallel-group design with 1:1:1 allocation. All arms share the same conventional resources (pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources); the arms differ only in the AI entry added on top (none / LLM / Agent). Randomization is stratified by hospital, specialty and seniority.
| Arm | Type | Receives |
|---|---|---|
| Agent-assisted group | Experimental | Other: Agent-assisted workflow |
| LLM-assisted group | Experimental | Other: LLM-assisted workflow |
| Traditional group | Active comparator | Other: Traditional Workflow |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Hold a Medical Practitioner Qualification Certificate and/or Medical License, or be a recognized standardized resident physician; able to independently read electronic medical records, laboratory and imaging reports on an HIS.
- Currently engaged in clinical work in internal medicine or surgery at one of the 15 participating hospitals.
- Able to complete the case assessment in one continuous hour without breaks.
- Able to participate remotely under video proctoring, with a stable internet connection and a working camera.
- Voluntarily agree to participate and sign the informed consent form, including the declaration not to use unauthorized AI tools during the assessment.
Key exclusion criteria (5 listed)
- Have previously accessed the official test cases or reference standard of this study.
- Unable to complete the training module, qualification test, or all experimental tasks.
- Have conflicts of interest, e.g. participation in developing core algorithms of the tested system.
- Unwilling to comply with remote proctoring, including keeping the camera on throughout.
- Judged unsuitable by the investigators.
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 |
|---|---|---|
| Agent-assisted workflowConventional resources (the pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources) plus an in-system Agent entry that automatically reads the full record and report images, produces a structured summary with source-text tracing, and supports multi-turn Q\&A and one-click editable drafts. | Other | Agent-assisted group |
| LLM-assisted workflowConventional resources (the pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources) plus an in-system multi-turn AI dialogue entry. The AI does not automatically read the record; participants paste text or send partial screenshots. | Other | LLM-assisted group |
| Traditional WorkflowConventional resources only: the pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources. No AI assistance. | Other | Traditional group |
Primary outcome
Mean Normalized Structured Score
Time frame: Within one-hour study
Mean of the rescaled case scores (each case rescaled to 100), divided by the number of cases completed; range 0 to 100.
2 secondary outcomes
Active Response Time per Case
Time frame: Within one-hour study
Degree of Adherence to AI-Generated Recommendations
Time frame: Within one-hour study
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 28 Sep 2026.
- Individual participant dataSharing planned: No
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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.
Yuan Ding study chair
Second Affiliated Hospital, Zhejiang University, School of Medicine
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Agent-assisted workflow, LLM-assisted workflow and Traditional Workflow in people with Clinical Decision Support Systems and Diagnostic Reasoning, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 1 site in China.
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 6 inclusion and 5 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: looking for participants now, as of the sponsor's last update on 28 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
Contact Yixin Zhang (+86 19157950225, 12518552@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean Normalized Structured Score (time frame: Within one-hour study). 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 28 Sep 2026.