Skip to content

Feasibility Assessment of Large Language Models in Automated Generation of Anesthesia Handover Documentation

Feasibility Assessment of Large Language Models in Automated Generation of Anesthesia Handover Documentation

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600127227
Enrollment
Unknown
Registered
2026-06-26
Start date
2026-07-01
Completion date
Unknown
Last updated
2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

Observation group:none

Sponsors

West China Hospital of Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Age >=18 years old; 2. Elective surgery; 3. General anesthesia or regional anesthesia (including spinal anesthesia, nerve block combined with general anesthesia); 4. The operation duration is >=1 hour; 5. After the operation, transfer to the PACU or ICU for formal anesthesia handover. 6. The medical records are complete, including admission records and examination and test results, preoperative anesthesia visit assessment forms, anesthesia record forms (including vital sign time series data), and surgical operation records. 7. Surgical time range: May 2023 to May 2025.

Exclusion criteria

Exclusion criteria: 1. Emergency or time-limited surgery; 2. Age <18 years old; 3. Surgeries performed under local anesthesia or monitored anesthesia (MAC); 4. The operation duration is less than 1 hour; 5. Return to the ward directly without a PACU/ICU after the operation (without a formal anesthesia handover scene); 6. Missing key modules of the medical record (any missing item is excluded); 7. For the same patient who has undergone multiple surgeries, only the medical record of the first surgery will be included.

Design outcomes

Primary

MeasureTime frame
The structural fidelity of medical documents generated by large language models;The content accuracy of medical documents generated by large language models;The expressive capability of unstructured text generated by large language models.;

Secondary

MeasureTime frame
Output time of large language models.;

Countries

China

Contacts

Public ContactZhu Tao

West China Hospital of Sichuan University

739501155@qq.com+86 28 85423593

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jul 3, 2026