Analysis of patient flows at a maximum care provider, using the University Hospital RWTH Aachen as an example, divided into a statistical, retrospective evaluation of relevant emergency department key performance indicators from 2019 and 2022, and subsequent development of a prognostic tool for the depth of care for specific tracer diagnoses using machine learning.

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00040138

Enrollment

100000

Registered

2026-05-13

Start date

2023-07-31

Completion date

Unknown

Last updated

2026-06-01

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

Conditions

Depth of care defined by ICD10, case mix index, length of stay, etc.

Interventions

Group 1: The intensity of care—specifically length of stay in the emergency department, hospital length of stay, hours of mechanical ventilation, case mix index, DRG-Group, Manchester Triage Score, an

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: During the observation period, the patient was in the emergency department of the Aachen University Hospital.

Exclusion criteria

Exclusion criteria: Incomplete dataset

Design outcomes

Primary

Measure	Time frame
The aim is therefore, for the first time, to depict the treatment urgency and depth of care of patients presented to an emergency room by the ambulance service, taking into account the discharge data.	—

Countries

Germany

Contacts

Public ContactJörb Brokmann

Uniklinik RWTH Aachen

jbrokmann@ukaachen.de+492418088421

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Jun 11, 2026