@inproceedings{1407bd5efd4a40858363fed23bd56428,
title = "Evaluating Patient Safety Drivers using Decision Trees",
abstract = "This study aims to identify the drivers of patient safety challenges in healthcare. Hospital-level aggregate survey data from the UK hospital is used to identify what drives the number of reported incidents affecting patient safety. Leveraging a decision tree algorithm, our results suggest that the role of teamwork and safety culture in incident reporting and investigation are the most critical dimensions influencing the number of reported patient safety challenges. The decision tree algorithm can be useful for hospitals to enhance patient safety through data-driven approaches and direct resources towards service improvements.",
keywords = "data analytics, decision trees, machine learning, medical errors, patient safety, safety culture",
author = "Bakhita Alderei and Ragheb Nammari and Alalami, {Mohammad A.} and Clarence Rodrigues and Abroon Qazi and {Emre Simsekler}, {Mecit Can}",
note = "Funding Information: This publication is based upon work supported by the Khalifa University of Science and Technology under Award No. RCII-2019-002, Center for Digital Supply Chain and Operations Management. The funding body had no direct involvement in the design, data collection, analysis, and interpretationorinwritingthemanuscript. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 ; Conference date: 21-02-2022 Through 24-02-2022",
year = "2022",
doi = "10.1109/ASET53988.2022.9735129",
language = "British English",
series = "2022 Advances in Science and Engineering Technology International Conferences, ASET 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 Advances in Science and Engineering Technology International Conferences, ASET 2022",
address = "United States",
}