Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance

Lua Perimal-Lewis, David Teubner, Paul Hakendorf, Chris Horwood

    Research output: Contribution to journalArticle

    12 Citations (Scopus)

    Abstract

    Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues.

    Original languageEnglish
    Pages (from-to)1017-1029
    Number of pages13
    JournalHEALTH INFORMATICS JOURNAL
    Volume22
    Issue number4
    Early online date11 Oct 2015
    DOIs
    Publication statusPublished - 1 Dec 2016

    Keywords

    • data quality
    • electronic health record
    • electronic medical record
    • health analytics
    • key performance indicators
    • patient journey
    • process mining
    • time-based performance metrics

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