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Validation and Refinement of a Pain Information Model from EHR Flowsheet Data.

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    |March 15, 2018
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    A standardized pain information model (IM) was developed using electronic health record (EHR) data from over 6 million patients. This model enhances data consistency for research and quality reporting, improving pain assessment and management across healthcare systems.

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    Area of Science:

    • Health Informatics
    • Clinical Data Standards
    • Pain Management Research

    Background:

    • Electronic health record (EHR) data offers cost-saving potential for research and quality reporting.
    • Standardization of EHR flowsheet data is crucial for consistency within and across organizations.
    • Customized EHR content leads to duplicative and non-comparable data, hindering interoperability.

    Purpose of the Study:

    • To validate and refine a pain information model (IM) using EHR flowsheet data.
    • To standardize pain concepts, definitions, and value sets for assessments, goals, interventions, and outcomes.
    • To improve the generalizability of pain data across different healthcare organizations.

    Main Methods:

    • A retrospective observational study utilizing an iterative consensus-based approach.
    • Mapping, analysis, and evaluation of EHR flowsheet data from 10 participating healthcare organizations.
    • Leveraging aggregated metadata from 6.6 million patients and 27 million encounters.

    Main Results:

    • Development of a final pain IM comprising 30 concepts, 4 panels, and 396 value set items.
    • The model is built upon Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms.
    • Results indicate a need for additional terms to fully support interoperability.

    Conclusions:

    • The pain IM is a consensus model derived from actual EHR documentation across multiple health systems.
    • The model effectively captures critical concepts related to pain assessment and management.
    • This standardized approach enhances the utility of EHR data for pain research and clinical practice.