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Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records:

Tom M Seinen1, Jan A Kors1, Erik M van Mulligen1

  • 1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.

Journal of Medical Internet Research
|February 13, 2025
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Summary
This summary is machine-generated.

Unstructured clinical notes in electronic health records (EHRs) contain significant information not present in structured data. This study validates that unstructured EHR data offers crucial, comprehensive insights across diverse patient populations.

Keywords:
EHRclinical concept extractionclinical concept similaritycodedataelectronic health recordselectronic recordframeworkfree-textinformationmachine learningnamed entity recognitionnatural language processingpatient recordsstructured datatext miningunstructured dataword embeddings

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Area of Science:

  • Health Informatics
  • Clinical Data Science
  • Natural Language Processing in Healthcare

Background:

  • Electronic health records (EHRs) integrate structured (e.g., diagnostic codes) and unstructured (e.g., clinical notes) data.
  • Unstructured clinical narratives are presumed to offer richer patient information, but lack large-scale validation.
  • Existing validation methods for EHR data completeness are limited.

Purpose of the Study:

  • To quantitatively compare information content between structured and unstructured EHR data.
  • To validate the hypothesis that unstructured EHR data provides more extensive patient information.
  • To assess data overlap across diverse patient populations and clinical contexts.

Main Methods:

  • Analysis of structured and unstructured data from a large Dutch primary care EHR database (2021-2024).
  • Utilized a Dutch-tailored extraction framework to identify clinical concepts from free-text notes.
  • Employed cosine similarity of concept embeddings to measure semantic similarity between structured and unstructured data.
  • Quantified concept overlap across domains and patient subpopulations.

Main Results:

  • Only 13% of concepts from patient records and 7% from visits had similar structured counterparts.
  • Conversely, 42% of structured concepts in records and 25% in visits were found in unstructured data.
  • Condition, measurement, and drug concepts showed varying overlap; subpopulations exhibited distinct data reliance patterns.

Conclusions:

  • Unstructured EHR data significantly contributes additional, valuable information beyond structured data.
  • The study validates the feasibility of quantifying information differences and highlights the utility of unstructured data.
  • The developed methodology is versatile for robust clinical research, with annotated matches shared publicly.