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

HUmani

Status: Completed

Conditions: Surgery-Complications; Pain, Postoperative; Anesthesia Complication

Treatments: Other: Mathematical Prediction of unforseen patient reorientation

Study type: Observational

Funder types: NETWORK

Identifiers

NCT06582407

HUmani_ODanesth

Details and patient eligibility

About

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.

Enrollment: 68,683 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patient undergoing anesthesia for a therapeutic or diagnostic procedure

Exclusion criteria

• Incomplete informatic data
• Error in the encoding system

Trial design

68,683 participants in 2 patient groups

Ambulatory Patients

Description:

Patient undergoing anesthesia in an ambulatory setting.

Treatment:

Other: Mathematical Prediction of unforseen patient reorientation

Hospitalised Patients

Description:

Patient undergoing anesthesia in a hospitalisation setting.

Treatment:

Other: Mathematical Prediction of unforseen patient reorientation