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Recognizing Questions and Answers in EMR Templates Using Natural Language Processing.

Guy Divita1, Shuying Shen1, Marjorie E Carter1

  • 1VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.

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Adapting natural language processing (NLP) pipelines to recognize templates in Veterans Affairs (VA) medical records improved information extraction (IE) accuracy. This enhanced NLP system better identifies psychosocial concepts by handling negation and question-answer structures within templates.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Informatics

Background:

  • Templated structures in electronic health records (EHRs) present significant challenges for natural language processing (NLP) tools in information extraction (IE).
  • Routine error analysis of Veterans Affairs (VA) medical records identified template-related false positives as a key issue in IE tasks.
  • Existing NLP pipelines often struggle to accurately interpret data within standardized or boilerplate sections of clinical notes.

Purpose of the Study:

  • To adapt an existing NLP pipeline (V3NLP) to effectively recognize and process templated structures within VA medical records.
  • To improve the accuracy of information extraction by specifically addressing challenges posed by negation, questions, and answers within templates.
  • To evaluate the feasibility and impact of template-aware NLP adaptation on the extraction of psychosocial concepts.

Main Methods:

  • The baseline NLP pipeline (V3NLP) was enhanced by incorporating a negation and slot:value identification annotator.
  • The adapted system was trained on a curated corpus of 975 VA medical documents serving as a reference standard for psychosocial concept extraction.
  • Iterative processing and evaluation were performed using both the baseline and the adapted NLP pipeline on the training corpus and a large-scale sample of 318,000 notes.

Main Results:

  • The adapted NLP pipeline demonstrated improved handling of negation, questions, and answers within various template types.
  • While some concept recall was lost, there was a modest increase in true positives across several psychosocial concept categories.
  • Processing a large dataset of 318,000 notes with the adapted V3NLP showed similar improvements in information extraction accuracy.

Conclusions:

  • Adapting NLP pipelines to recognize and interpret templated structures in clinical notes is feasible and beneficial for IE tasks.
  • The enhanced NLP system shows promise for more accurate extraction of specific concepts, such as psychosocial factors, from VA medical records.
  • This work highlights a practical approach to overcoming limitations of NLP in processing structured or semi-structured clinical text data.