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    Network analysis of opioid prescribing data reveals prescribing behaviors. This method offers insights into patient-prescriber interactions to reduce prescription drug harm.

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    Area of Science:

    • Health Informatics
    • Network Science
    • Pharmacovigilance

    Background:

    • Opioid analgesic prescribing is increasing globally and in Australia, leading to significant patient harm, including overdose and death.
    • Queensland Health manages a state-wide database of opioid analgesic prescriptions, vital for monitoring drug use trends.

    Purpose of the Study:

    • To propose and demonstrate network visualization and analysis as a tool for understanding complex opioid prescribing data.
    • To analyze Fentanyl patch prescribing patterns in Queensland, Australia (2011-2018) using network analysis to identify prescribing behaviors.

    Main Methods:

    • Prescribing data were modeled as a dynamic, bipartite graph representing patient-prescriber interactions over time.
    • Network analysis techniques were applied to provide both global and local views of the prescribing dataset.
    • Network statistics were used to quantify structural and dynamic properties, revealing trends in drug-seeking and prescribing behaviors.

    Main Results:

    • The network analysis approach provided a comprehensive overview of state-wide prescribing data, including subgraph distributions.
    • The technique revealed dynamic changes in the graph structure over time, offering clinical utility.
    • Key characteristics and trends in patient-prescriber interactions related to opioid prescribing were identified.

    Conclusions:

    • Network visualization and analysis offer novel insights into patient-prescriber relationships within large-scale prescribing datasets.
    • This methodology supports healthcare professionals in their advisory roles to mitigate patient harm from prescription drug misuse.
    • The approach is recognized by Queensland Health professionals for its ability to generate useful intelligence on prescribing patterns.