Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission

NCT07760051 | Recruiting

• 1 other identifier: 2026-0977
• Study Type: interventional
• Target: 180
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

Clinical Decision Support Systems; Diagnostic Reasoning; Artificial Intelligence (AI)

Keywords

AI agent; Large Language Model; Clinical Decision Support; Admission Diagnosis; Diagnostic Accuracy; Diagnostic Reasoning; Randomized Controlled Trial; Management Planning

Outcome Measures

Primary Outcomes (1)

• 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.

  Within one-hour study

Secondary Outcomes (2)

• Active Response Time per Case

  Within one-hour study

• Degree of Adherence to AI-Generated Recommendations

  Within one-hour study

Study Arms (3)

Agent-assisted group

EXPERIMENTAL

Other: Agent-assisted workflow

LLM-assisted group

EXPERIMENTAL

Other: LLM-assisted workflow

Traditional group

ACTIVE COMPARATOR

Other: Traditional Workflow

Interventions

Traditional Workflow OTHER

Conventional resources only: the pre-admission clinical record, conventional search engines, permitted conventional non-generative clinical information resources. No AI assistance.

Traditional group

Agent-assisted workflow OTHER

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.

Agent-assisted group

LLM-assisted workflow OTHER

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.

LLM-assisted group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Second Affiliated Hospital, Zhejiang University, School of Medicine: lead
• Shangrao People's Hospital: collaborator

Study Sites (1)

2nd Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, 310009, China

RECRUITING

Study Officials

• Yuan Ding

  Second Affiliated Hospital, Zhejiang University, School of Medicine

  STUDY CHAIR

Central Study Contacts

Yixin Zhang

CONTACT

+86 19157950225; 12518552@zju.edu.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Masking Details: Participants and investigators cannot be masked, as the intervention is the workflow itself. Outcome assessors scoring the structured answers are blinded to group allocation; submitted answers are anonymized and stripped of workflow-identifying information before scoring.
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: 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.

Study Record Dates

• First Submitted: July 29, 2026
• First Posted: August 12, 2026
• Study Start: September 6, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: September 28, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)