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

Effects of Agent-assisted, LLM-assisted and Traditional Workflows on Diagnosis and Management Planning at Admission: A Randomized Controlled Study

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.

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
• Complete 6 anonymized simulated HIS admission cases within one hour
• 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

Study Overview

• Status: Not yet recruiting
• Conditions: Clinical Decision Support Systems; Artificial Intelligence (AI); Diagnostic Reasoning
• Intervention / Treatment: Other: Agent-assisted workflow; Other: LLM-assisted workflow; Other: Traditional Workflow

• Study Type: Interventional
• Enrollment (Estimated): 180
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Yixin Zhang; Phone Number: +86 19157950225; Email: 12518552@zju.edu.cn

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310009
      • 2nd Affiliated Hospital, School of Medicine, Zhejiang University
      • Contact: Human Subject Research Ethics Committee; Phone Number: +86 0571 87783759; Email: keyanlunli_zheer@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria

• 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.

Exclusion Criteria

• 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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Active Comparator: Traditional group	Other: Traditional Workflow; Conventional resources only: the pre-admission clinical record, standard search engines. No AI assistance.
Experimental: Agent-assisted group	Other: Agent-assisted workflow; Conventional resources (pre-admission clinical record, search engines) 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.
Experimental: LLM-assisted group	Other: LLM-assisted workflow; Conventional 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Degree of Adherence to AI-Generated Recommendations	Degree to which the submitted answer incorporates AI output. Assessed in the Agent and LLM arms only.	Within one-hour study
Active Response Time per Case	Active response time in seconds for each case, recorded automatically by the platform.Time is counted per case while that case's response page is active.	Within one-hour study

Collaborators and Investigators

• Sponsor: Second Affiliated Hospital, Zhejiang University, School of Medicine
• Collaborators: Shangrao People's Hospital
• Investigators: Study Chair: Yuan Ding, Second Affiliated Hospital, Zhejiang University, School of Medicine

Study record dates

Study Major Dates

• Study Start (Estimated): August 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

• First Submitted: July 29, 2026
• First Submitted That Met QC Criteria: August 9, 2026
• First Posted (Actual): August 12, 2026

Study Record Updates

• Last Update Posted (Actual): August 18, 2026
• Last Update Submitted That Met QC Criteria: August 15, 2026
• Last Verified: August 1, 2026

More Information

Terms related to this study

• Keywords: Clinical Decision Support; Randomized Controlled Trial; Diagnostic Accuracy; Large Language Model; AI agent; Admission Diagnosis; Diagnostic Reasoning; Management Planning
• Other Study ID Numbers: 2026-0977

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No