A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms

Mecit Can Emre Simsekler, Clarence Rodrigues, Abroon Qazi, Samer Ellahham, Al Ozonoff

    Research output: Contribution to journalArticlepeer-review

    18 Scopus citations

    Abstract

    Medical errors constitute a significant challenge affecting patient and staff safety in complex and dynamic healthcare systems. While vv v various organizational factors may contribute to such errors, limited studies have addressed patient and staff safety issues simultaneously in the same study setting. To evaluate this, we conduct an exploratory analysis using two types of tree-based machine learning algorithms, random forests and gradient boosting, and the hospital-level aggregate staff experience survey data from UK hospitals. Based on staff views and priorities, the results from both algorithms suggest that “health and wellbeing” is the leading theme associated with the number of reported errors and near misses harming patient and staff safety. Specifically, “work-related stress” is the most important survey item associated with safety outcomes. With respect to prediction accuracy, both algorithms provide similar results with comparable values in error metrics. Based on the analytical results, healthcare risk managers and decision-makers can develop and implement policies and practices that address staff experience and prioritize resources effectively to improve patient and staff safety.

    Original languageBritish English
    Article number107416
    JournalReliability Engineering and System Safety
    Volume208
    DOIs
    StatePublished - Apr 2021

    Keywords

    • Data analytics
    • Gradient boosting
    • Healthcare operations
    • Machine learning
    • Medical errors
    • Patient safety
    • Random forest
    • Staff safety

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