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Effect of Agent-assisted, LLM-assisted and Traditional Workflows on Physician Admission Diagnosis

NCT07760051 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 180

Last updated 2026-08-12

No results posted yet for this study

Summary

The goal of this clinical trial is to evaluate whether AI-assisted workflows improve physicians' admission diagnosis 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 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-only group, Agent 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

Conditions

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

Interventions

OTHER

Agent 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.

OTHER

LLM 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.

OTHER

traditional workflow

Conventional resources only: the pre-admission clinical record, standard search engines. No AI assistance.

Sponsors & Collaborators

  • Shangrao People's Hospital

    collaborator UNKNOWN

  • Second Affiliated Hospital, Zhejiang University, School of Medicine

    lead OTHER

Principal Investigators

  • Yuan Ding · Second Affiliated Hospital, Zhejiang University, School of Medicine

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-09-30
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07760051 on ClinicalTrials.gov