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The SMART Text2FHIR Pipeline.

Timothy A Miller1, Andrew J McMurry1, James Jones2

  • 1Computational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA.

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Summary
This summary is machine-generated.

This study introduces SMART Text2FHIR, an open-source natural language processing (NLP) tool that converts clinical notes into Fast Healthcare Interoperability Resources (FHIR). This enables seamless data exchange for research and public health initiatives.

Keywords:
InteroperabilityNatural language processingelectronic health records

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Clinical notes contain valuable unstructured data crucial for research and public health.
  • Existing methods for extracting and structuring this data can be complex and lack interoperability.
  • The need for standardized data formats like Fast Healthcare Interoperability Resources (FHIR) is increasing due to new interoperability regulations.

Approach:

  • Developed an open-source, high-throughput natural language processing (NLP) module using Apache cTAKES.
  • Created FHIR resources with modifier extensions for negation, NLP sourcing, and concept provenance.
  • Implemented the SMART Text2FHIR Pipeline, released via standard package managers and container images.

Key Points:

  • The SMART Text2FHIR Pipeline facilitates the conversion of clinical text into FHIR resources.
  • Utilizes modifier extensions within FHIR to represent complex clinical concepts like negation and data provenance.
  • Provides an easily deployable and free solution for NLP-driven healthcare data extraction.

Conclusions:

  • The developed framework enables a common language for transporting structured and unstructured clinical data.
  • This NLP to FHIR conversion is valuable for critical public health and clinical research use cases.
  • Future work includes expanding mappings for more NLP-extracted information categories and integrating additional NLP tools.