Artificial Intelligence(AI) in the Emergency Department (ED) in Cologne

NCT07725965 | Active Not Recruiting

• 2 other identifiers: KINA-CO26-1064
• Study Type: observational
• Target: 100,000
• Locations: 1 country
• Sites: 1

Brief Summary

This retrospective, non-interventional study evaluates the prognostic performance of open-weight Large Language Models (LLMs) in the setting of a German academic emergency department. Using a full census of all consecutive emergency department cases at University Hospital Cologne between 01 January 2023 and 31 December 2025 (approximately 100,000 cases), the study assesses whether LLMs can make reliable prognostic predictions (e.g., hospital admission, imaging, diagnosis, placement) based on the initial history, vital signs, and triage category. In addition, it quantifies how strongly automated anonymization and perturbation procedures affect the models' diagnostic accuracy. This is an Investigator-Initiated Trial (IIT) with no intervention on patients.

Conditions

Emergency Care; Clinical Decision Support; Triage

Keywords

Emergency Care; Medical AI; Large Language Models; Triage; Anonymization; De-Identification; Clinical Decision Support; AI Real World Data Validation

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy of the LLM predictions (AUROC, F1 score) compared with the clinical gold standard

  assessment at the level of the individual emergency department encounter).\]

  From enrollment to the end of retrospective observation period at 1 year

Secondary Outcomes (2)

• Relative performance loss of the models between original data (Arm A) and anonymized/perturbed data (Arm B); hypothesis < 5%.

  From enrollment to the end of retrospective observation period at 1 year

• Sensitivity, specificity, positive predictive value(PPV)/negative predictive value (NPV) for binary endpoints and agreement of the triage assessment (Cohen's kappa / Krippendorff's alpha).

  From enrollment to the end of retrospective observation period at 1 year

Study Arms (2)

Analytic Arm A

Original data will be used for the analysis

Analytic Arm B

Anonymized/perturbed data will be used for the analysis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All consecutive treatment cases at the Central Emergency Department of University Hospital Cologne during 01 January 2023 - 31 December 2025 (full census, approximately 100,000 cases).

You may not qualify if:

• Documented objection to the scientific use of the data pursuant to Art. 21 General data protection Regulation (GDPR).
• Cases lacking the minimum data required for analysis (triage/history and documented outcome).

Sponsors & Collaborators

• University of Cologne: lead

Study Sites (1)

Department of Internal Medicine II, University Hospital Cologne

Cologne, 50937, Germany

Location

Study Officials

• Volker Burst, Prof.

  Department of Internal Medicine II, University Hospital Cologne

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof. Dr.

Study Record Dates

• First Submitted: July 21, 2026
• First Posted: July 24, 2026
• Study Start: July 1, 2026
• Primary Completion (Estimated): July 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: July 28, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

DE (1)