AI-Driven Analysis of Device Duration in Anesthesiology
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Abstract
Lines, drains, and airway devices are commonly used in surgical and inpatient care to support treatment and patient monitoring. While these devices are essential, prolonged device retention is associated with complications such as infection, tissue injury, thrombosis, and extended hospital stay. Understanding how long devices remain in place and how duration varies across device types is important for improving patient safety and guiding device management practices. This study analyzes device duration patterns using Patient Lines, Drains, and Airways (LDA) data containing information on device type, placement time, and removal time across multiple patient encounters. Exploratory data analysis and machine learning approaches are used to examine duration distributions, distinguish short and prolonged device retention, and identify structural patterns across device categories. The findings indicate notable variation in device duration patterns across categories, with identifiable differences in device retention behavior across device groups. This study highlights the value of observational electronic health record data for characterizing device exposure patterns and provides a foundation for future datadriven device management and risk assessment.
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BibTeX
@misc{sutar2026aidriven,
title = {{AI-Driven Analysis of Device Duration in Anesthesiology}},
author = {A. Sutar and B. Tabman and H. Fatima and P. Norouzzadeh and Y. Martins and M. Yazdi and B. Rahmani},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.25.26364018},
url = {https://www.medrxiv.org/content/10.64898/2026.09.25.26364018v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - AI-Driven Analysis of Device Duration in Anesthesiology
AU - A. Sutar
AU - B. Tabman
AU - H. Fatima
AU - P. Norouzzadeh
AU - Y. Martins
AU - M. Yazdi
AU - B. Rahmani
PY - 2026
DA - 2026/09/25
PB - medRxiv
DO - 10.64898/2026.09.25.26364018
UR - https://www.medrxiv.org/content/10.64898/2026.09.25.26364018v1
AB - Lines, drains, and airway devices are commonly used in surgical and inpatient care to support treatment and patient monitoring. While these devices are essential, prolonged device retention is associated with complications such as infection, tissue injury, thrombosis, and extended hospital stay. Understanding how long devices remain in place and how duration varies across device types is important for improving patient safety and guiding device management practices. This study analyzes device duration patterns using Patient Lines, Drains, and Airways (LDA) data containing information on device type, placement time, and removal time across multiple patient encounters. Exploratory data analysis and machine learning approaches are used to examine duration distributions, distinguish short and prolonged device retention, and identify structural patterns across device categories. The findings indicate notable variation in device duration patterns across categories, with identifiable differences in device retention behavior across device groups. This study highlights the value of observational electronic health record data for characterizing device exposure patterns and provides a foundation for future datadriven device management and risk assessment.
ER -