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

HUmani68,683 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

68,683

Conditions

Anesthesia Complication Surgery-Complications Pain, Postoperative

Interventions

Mathematical Prediction of unforseen patient reorientationOTHER

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

Eligibility

Sex: ALL

Medical Language ↔ Plain English

Inclusion Criteria: * Patient undergoing anesthesia for a therapeutic or diagnostic procedure Exclusion Criteria: * Incomplete informatic data * Error in the encoding system

Outcomes

Primary Outcomes

Rate of patient reorientation

Rate of unforeseen hospital admission after an ambulatory surgery and rate of discharge after an hospitalised surgery

Time frame: On the day of the operation