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

NCT06582407 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 68683

Last updated 2024-10-18

No results posted yet for this study

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

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

Sponsors & Collaborators

  • HUmani

    lead NETWORK

Principal Investigators

  • Rémi Florquin, MD · Université de Mons, Belgium

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-01-01
Primary Completion
2024-06-30
Completion
2024-07-30

Countries

  • Belgium

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06582407 on ClinicalTrials.gov