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Using Real-World Data on Depression from EHR-based Research Networks: A Scoping Review.

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    This summary is machine-generated.

    Electronic health record (EHR) research networks offer valuable data for studying depression, but their use remains limited. This review highlights the underutilization of these powerful tools for depression epidemiology and management research.

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    Area of Science:

    • Public Health
    • Epidemiology
    • Health Informatics

    Background:

    • Depression is a major global public health concern requiring advanced research methods.
    • Electronic health record (EHR) research networks provide large-scale data but are underutilized for depression studies.

    Purpose of the Study:

    • To conduct a scoping review on the extent of depression research utilizing EHR networks.
    • To identify trends and gaps in the application of EHR networks for studying depression.

    Main Methods:

    • Systematic literature search using Arksey and O'Malley framework and PRISMA guidelines across multiple databases.
    • Included studies published in September 2024, focusing on depression research in EHR networks like TriNetX, All of Us, and Million Veteran Program (MVP).
    • Utilized Covidence with a custom large language model (LLM) plugin for screening and data extraction.

    Main Results:

    • A limited number of studies (166 from 1211 records) utilized EHR networks for depression research.
    • TriNetX (36 studies) and All of Us (24 studies) were the most common platforms.
    • Research predominantly focused on US populations, analyzing predictors such as age, gender/sex, and race/ethnicity.

    Conclusions:

    • EHR networks possess substantial real-world data crucial for advancing depression research.
    • The current underutilization of EHR networks indicates a need for improved accessibility to maximize their potential in future depression studies.