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Toward a Model for Personal Health Record Interoperability.

Alex Roehrs, Cristiano Andre da Costa, Rodrigo da Rosa Righi

    IEEE Journal of Biomedical and Health Informatics
    |July 12, 2018
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    Summary

    This study introduces OmniPHR, a model enhancing personal health record (PHR) interoperability. It integrates diverse health data standards, enabling a unified patient health view for better healthcare delivery.

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    Area of Science:

    • Health Informatics
    • Medical Informatics
    • Computer Science

    Background:

    • Electronic health record (EHR) adoption is hindered by numerous data standards, impeding healthcare provider communication.
    • Patients frequently re-submit health information due to fragmented data across different healthcare locations, delaying personal health record (PHR) adoption.
    • Lack of a unified, patient-controlled health record system limits comprehensive care and proactive health management.

    Purpose of the Study:

    • To propose and evaluate an interoperability model for personal health record (PHR) systems.
    • To demonstrate the feasibility of integrating diverse health data standards (openEHR, HL7 FHIR, MIMIC-III) into a single, accessible format.
    • To enhance semantic interoperability for a unified patient health record vision.

    Main Methods:

    • Prototyping the OmniPHR application model for evaluating semantic interoperability.
    • Utilizing a real-world anonymized patient database (38,645 records) processed with multiple standards.
    • Employing a standard ontology, artificial intelligence, and natural language processing (NLP) for data integration.

    Main Results:

    • OmniPHR successfully demonstrated interoperability across openEHR, HL7 FHIR, and MIMIC-III reference models.
    • Achieved an 87.9% F1-score after model retraining, improving upon an initial 76.39% score.
    • The model provides a unified, structural, semantic, and up-to-date vision of PHR data for patients and providers.

    Conclusions:

    • The OmniPHR model proves the feasibility of achieving semantic interoperability for PHRs.
    • This approach facilitates the consolidation of disparate health data into a single, coherent format.
    • Results suggest potential for developing inference rules to predict or prevent patient health issues.