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A Cross-Sectional Study to Evaluate the Accuracy and Reliability of Large Language Models in Generating Nursing Diagnoses for Patients with Cardiovascular Diseases

A Cross-Sectional Study to Evaluate the Accuracy and Reliability of Large Language Models in Generating Nursing Diagnoses for Patients with Cardiovascular Diseases

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500114698
Enrollment
Unknown
Registered
2025-12-16
Start date
2025-12-16
Completion date
Unknown
Last updated
2026-01-05

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

Conditions

Cardiovascular diseases

Interventions

Nurse:None

Sponsors

Xiamen Cardiovascular Hospital of Xiamen University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. The research subjects must be registered nurses, and have worked for at least one year in clinical wards such as cardiovascular disease wards where nursing diagnoses need to be routinely written before the start of this study; 2. Hold a valid nurse practice certificate; 3. Voluntarily participate in this study and sign the informed consent form.

Exclusion criteria

Exclusion criteria: 1. Nurses on further study or rotation (non - formally established and not working in the designated ward for a long - term); 2. Currently on maternity leave, sick leave or long - term leave.

Design outcomes

Primary

MeasureTime frame
Accuracy of nursing diagnoses generated by large language models;Reliability of Nursing Diagnoses Generated by Large Language Models;—

Countries

China

Contacts

Public ContactChen Yuan

Xiamen Cardiovascular Hospital of Xiamen University

28837445@qq.com+86 592 299 3237

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026