- ICH GCP
- Amerikanska kliniska prövningsregistret
- Klinisk prövning NCT07760051
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
Studieöversikt
Status
Intervention / Behandling
Studietyp
Inskrivning (Beräknad)
Fas
- Inte tillämpbar
Kontakter och platser
Studiekontakt
- Namn: Yixin Zhang
- Telefonnummer: +86 19157950225
- E-post: 12518552@zju.edu.cn
Studieorter
-
-
Zhejiang
-
Hangzhou, Zhejiang, Kina, 310009
- 2nd Affiliated Hospital, School of Medicine, Zhejiang University
-
Kontakt:
- Human Subject Research Ethics Committee
- Telefonnummer: +86 0571 87783759
- E-post: keyanlunli_zheer@163.com
-
-
Deltagandekriterier
Urvalskriterier
Åldrar som är berättigade till studier
- Vuxen
- Äldre vuxen
Tar emot friska volontärer
Beskrivning
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.
Studieplan
Hur är studien utformad?
Designdetaljer
- Primärt syfte: Hälsovårdsforskning
- Tilldelning: Randomiserad
- Interventionsmodell: Parallellt uppdrag
- Maskning: Enda
Vapen och interventioner
Deltagargrupp / Arm |
Intervention / Behandling |
|---|---|
|
Aktiv komparator: Traditionell grupp
|
Conventional resources only: the pre-admission clinical record, standard search engines.
No AI assistance.
|
|
Experimentell: Agent-assisted group
|
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.
|
|
Experimentell: LLM-assisted group
|
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.
|
Vad mäter studien?
Primära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Mean Normalized Structured Score
Tidsram: 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.
|
Within one-hour study
|
Sekundära resultatmått
Resultatmått |
Åtgärdsbeskrivning |
Tidsram |
|---|---|---|
|
Degree of Adherence to AI-Generated Recommendations
Tidsram: Within one-hour study
|
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
Tidsram: Within one-hour study
|
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
|
Samarbetspartners och utredare
Samarbetspartners
Utredare
- Studiestol: Yuan Ding, Second Affiliated Hospital, Zhejiang University, School of Medicine
Studieavstämningsdatum
Studera stora datum
Studiestart (Beräknad)
Primärt slutförande (Beräknad)
Avslutad studie (Beräknad)
Studieregistreringsdatum
Först inskickad
Först inskickad som uppfyllde QC-kriterierna
Första postat (Faktisk)
Uppdateringar av studier
Senaste uppdatering publicerad (Faktisk)
Senaste inskickade uppdateringen som uppfyllde QC-kriterierna
Senast verifierad
Mer information
Termer relaterade till denna studie
Nyckelord
Andra studie-ID-nummer
- 2026-0977
Plan för individuella deltagardata (IPD)
Planerar du att dela individuella deltagardata (IPD)?
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