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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

68683 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-01-01

Study Completion Date

2024-07-30

Brief Summary

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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.

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Detailed Description

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Conditions

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Anesthesia Complication Surgery-Complications Pain, Postoperative

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Ambulatory Patients

Patient undergoing anesthesia in an ambulatory setting.

Mathematical Prediction of unforseen patient reorientation

Intervention Type OTHER

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

Hospitalised Patients

Patient undergoing anesthesia in a hospitalisation setting.

Mathematical Prediction of unforseen patient reorientation

Intervention Type OTHER

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

Interventions

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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

Intervention Type OTHER