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

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