NCT06582407

Machine Learning Models for Predicting Unforeseen Hospital Admissions or Discharges After Anesthesia

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 68,683 (actual)
• Sponsor: HUmani · Network
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

Unexpected hospital admissions after ambulatory surgery not only bring discomfort to patients but also causes a decrease in the efficiency of the healthcare system. In addition, unanticipated patient's orientation carry the risk of unsuitable post operative orders. The hypothesis of this project is that artificial intelligence models will outperform traditional models in predicting which patients will require hospital admission after ambulatory surgery or unforeseen hospital discharge after surgery.

Conditions

• Anesthesia Complication
• Surgery-Complications
• Pain, Postoperative

Interventions

Type	Name	Description
OTHER	Mathematical Prediction of unforseen patient reorientation	The goal of this project is to develop models to predict in the preoperative period which patients will require hospital admission after ambulatory surgery or unforeseen hospital discharge after surgery

Timeline

• Start date: 2020-01-01
• Primary completion: 2024-06-30
• Completion: 2024-07-30
• First posted: 2024-09-03
• Last updated: 2024-10-18

Locations

1 site across 1 country: Belgium