KISIK, AI-based prediction and optimization methods in assistance systems for the effective and efficient management of intensive care capacity in German hospitals: Which model is superior in predicting Length of Stay and Mortality: Decision-making with artificial intelligence vs. clinical decision-making at the bedside

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00037851

Enrollment

300

Registered

2025-09-25

Start date

2025-09-23

Completion date

Unknown

Last updated

2025-10-06

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

Conditions

Length of Stay prognose

Interventions

Group 1: Senior physicians estimate the patients’ length of stay in the ICU based on their clinical experience. Group 2: Resident physicians estimate the patients’ length of stay in the ICU based on t

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: Patientdata from the ICU

Exclusion criteria

Exclusion criteria: Length of Stay in the ICU under 24 std

Design outcomes

Primary

Measure	Time frame
Comparison of the predictions of the two groups	—

Secondary

Measure	Time frame
Impact of the tool on medical decision-making	—

Countries

Germany

Contacts

Public ContactAlexander Althammer

Universitätsklinikum Augsburg

alexander.althammer@uk-augsburg.de+49 821 400-01

Outcome results

None listed

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