Making the operating room schedule more efficient with artificial intelligence models

Operating room schedule optimisation using AI-driven techniques - oint of care fibrinogen measurement in trauma patients in the emergency department

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON57693

Enrollment

100000

Registered

2025-06-06

Start date

2025-07-01

Completion date

Unknown

Last updated

2025-09-08

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

Conditions

Operating Room (OR) Scheduling, Healthcare Efficiency, Hospital Resource Allocation, Capacity Planning, Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, Mathematical Modeling

Interventions

none

Eligibility

Age

2 Years to 99 Years

Inclusion criteria

Inclusion criteria: Patients that underwent surgery in the Erasmus MC in the last 10 years.

Exclusion criteria

Exclusion criteria: Patients that objected against the use of their data for research purposes.

Design outcomes

Primary

Measure	Time frame
Primary endpoint:Accuracy of prediction model for predicting OR time.	—

Secondary

Measure	Time frame
Efficiency of OR planning when using the prediction model compared to current standard of care	—

Countries

Netherlands

Contacts

Public ContactN.A. Ottenhof

Erasmus MC, Universitair Medisch Centrum Rotterdam

n.ottenhof@erasmusmc.nl+31 107041277

Outcome results

None listed

Source: NL-OMON (via WHO ICTRP)