Evaluation of patient safety culture using a random forest algorithm

Mecit Can Emre Simsekler, Abroon Qazi, Mohammad Amjad Alalami, Samer Ellahham, Al Ozonoff

    Research output: Contribution to journalArticlepeer-review

    43 Scopus citations

    Abstract

    Safety culture is a multidimensional concept that may be associated with medical errors and patient safety events in healthcare delivery systems. However, limited evidence is available regarding which safety culture dimensions drive overall patient safety. Moreover, the use of advanced statistical analysis has been limited in past studies of safety culture data. To address these issues, we use hospital-level aggregate survey data from U.S. hospitals to analyze the relationship between the defined safety culture dimensions and the patient safety grade. We use a tree-based machine learning algorithm, random forests, to estimate accurate and stable associations. The results of our analysis show that safety perception, management support, and supervisor/manager expectations are the leading drivers of patient safety grade. More specifically, safety problems in the work unit and work climate provided by hospital management are specific drivers of patient safety outcomes. The random forest model sheds new light on the most important cultural features relevant to patient safety.

    Original languageBritish English
    Article number107186
    JournalReliability Engineering and System Safety
    Volume204
    DOIs
    StatePublished - Dec 2020

    Keywords

    • Healthcare operations
    • Machine learning
    • Patient safety
    • Random forest algorithm
    • Safety culture

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