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Data Mining CMMSs: How to Convert Data into Knowledge.

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    Inferential statistics can analyze medical device maintenance data. This study found ventilator age, manufacturer, and preventive maintenance hours significantly impact corrective maintenance, but explain only 16% of the variability.

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    Area of Science:

    • Biomedical Engineering
    • Healthcare Technology Management
    • Statistical Analysis

    Background:

    • Healthcare technology management (HTM) possesses extensive medical device maintenance data with limited knowledge extraction.
    • Inferential statistics offer a powerful, yet underutilized, tool for analyzing this data within HTM.
    • Understanding device maintenance drivers is crucial for optimizing healthcare operations.

    Purpose of the Study:

    • To introduce inferential statistics to the HTM field.
    • To investigate the relationship between ventilator preventive maintenance (PM) and corrective maintenance (CM) labor hours.
    • To examine the influence of device age and manufacturer on maintenance needs.

    Main Methods:

    • A multiple regression analysis was performed on over 21,000 work orders for 2,045 ventilators (26 manufacturers, 2012-2016).
    • Key variables included ventilator age, manufacturer, PM labor hours, and CM labor hours.
    • Statistical significance was assessed using P-values.

    Main Results:

    • Ventilator age, manufacturer, and accumulated PM labor hours significantly influenced CM labor hours (P < 0.001).
    • Increased PM labor hours correlated with increased CM labor hours.
    • These factors collectively explained only 16% of the variability in CM labor hours.

    Conclusions:

    • While PM labor, age, and manufacturer are significant predictors, they do not adequately predict ventilator CM labor.
    • The majority (84%) of CM labor variability stems from unmeasured factors.
    • The developed regression model is unsuitable for precise ventilator CM labor prediction.