Anesthesia Complication, Pain, Postoperative, Surgery-Complications
Conditions
Keywords
anesthesiology, ambulatory surgery, artificial intelligence, machine learning, post operative complication
Brief 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.
Interventions
OTHERMathematical 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
HUmani
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* Patient undergoing anesthesia for a therapeutic or diagnostic procedure
Exclusion criteria
* Incomplete informatic data * Error in the encoding system
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Rate of patient reorientation | On the day of the operation | Rate of unforeseen hospital admission after an ambulatory surgery and rate of discharge after an hospitalised surgery |
Countries
Belgium
Outcome results
None listed