Evaluating Patient Safety Drivers using Decision Trees

Bakhita Alderei, Ragheb Nammari, Mohammad A. Alalami, Clarence Rodrigues, Abroon Qazi, Mecit Can Emre Simsekler

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    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.

    Original languageBritish English
    Title of host publication2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781665418010
    DOIs
    StatePublished - 2022
    Event2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 - Dubai, United Arab Emirates
    Duration: 21 Feb 202224 Feb 2022

    Publication series

    Name2022 Advances in Science and Engineering Technology International Conferences, ASET 2022

    Conference

    Conference2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
    Country/TerritoryUnited Arab Emirates
    CityDubai
    Period21/02/2224/02/22

    Keywords

    • data analytics
    • decision trees
    • machine learning
    • medical errors
    • patient safety
    • safety culture

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