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Classifying Electronic Consults for Triage Status and Question Type.

Xiyu Ding1,2, Michael L Barnett2,3, Ateev Mehrotra4

  • 1Boston Children's Hospital, Boston, MA.

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Summary
This summary is machine-generated.

Electronic consult (eConsult) systems improve specialist efficiency and healthcare access. Classifiers analyzing eConsult questions enhance triage and understanding of clinical queries, boosting system performance.

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Area of Science:

  • Health Informatics
  • Clinical Decision Support
  • Artificial Intelligence in Healthcare

Background:

  • Electronic consult (eConsult) systems enhance specialist efficiency and healthcare access, particularly in under-resourced settings.
  • Understanding eConsult usage patterns is crucial for optimizing specialist workflows and patient care.
  • Safety net systems benefit significantly from improved access to specialist consultations.

Purpose of the Study:

  • To develop and apply machine learning classifiers to categorize eConsult messages.
  • To analyze how specialist offices triage eConsult questions.
  • To identify the types of clinical questions primary care providers pose to specialists.

Main Methods:

  • Utilized a dataset of eConsult questions from primary care providers.
  • Developed and applied classification models to triage eConsult messages.
  • Evaluated the performance of pre-trained transformer models with domain-specific training.

Main Results:

  • Pre-trained transformer models demonstrated strong baseline performance for classifying eConsults.
  • Domain-specific training and shared representations further improved classifier accuracy.
  • Classifiers effectively categorized triage methods and clinical question types.

Conclusions:

  • Machine learning classifiers can effectively analyze and categorize eConsult data.
  • Optimizing eConsult systems through data analysis can enhance specialist efficiency.
  • This approach holds promise for improving healthcare delivery in diverse settings.