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Analyzing Patient Secure Messages Using a Fast Health Care Interoperability Resources (FIHR)-Based Data Model:

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This summary is machine-generated.

This study developed a data model for patient secure messages using FHIR standards, improving information extraction from patient communications. The model helps identify key health concepts, paving the way for better patient-centered care.

Keywords:
FHIRannotated corpusdata modelpatient portalpatient secure messagestopic modeling

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Medical Data Modeling

Background:

  • Patient portals linked to EHRs have grown since the Medicare Access and Children's Health Insurance Program Reauthorization Act and Meaningful Use program.
  • Secure messaging via patient portals has increased, offering research opportunities in patient-centered care.
  • Analyzing patient secure messages is crucial for understanding patient needs and improving healthcare delivery.

Purpose of the Study:

  • To develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard.
  • To identify and extract significant information from patient secure messages.
  • To facilitate natural language processing (NLP) analysis of patient communications.

Main Methods:

  • Iterative development and annotation of a data model using FHIR standards.
  • Manual review and annotation of over 2 million patient secure messages.
  • Application of topic modeling to identify hidden themes in patient messages.

Main Results:

  • A 3-level hierarchical data model with 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts was created.
  • Clinical concepts formed the largest category (64.38%) in the annotated corpus.
  • Topic modeling successfully identified key concepts like fatigue, prednisone, and patient visits, with 89.2% of keywords aligning with the data model.

Conclusions:

  • The developed data model and annotated corpus enable better identification and understanding of medical concepts in patient messages.
  • The model can be extended to analyze other patient narratives from social media and forums.
  • Future work includes developing NLP solutions for automated triaging and enhanced patient-centered care analysis.