Open AccessMachine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center
Author(s) -
Rodney A. Gabriel,
Bhavya Harjai,
Sierra Simpson,
Nicole H. Goldhaber,
Brian P. Curran,
Ruth S. Waterman
Publication year - 2022
Publication title -
anesthesia and analgesia/anesthesia and analgesia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.404
H-Index - 201
eISSN - 1526-7598
pISSN - 0003-2999
DOI - 10.1213/ane.0000000000006015
Subject(s) - medicine , ambulatory , logistic regression , pacu , outpatient surgery , receiver operating characteristic , random forest , surgery , emergency medicine , machine learning , computer science
Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression.