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Probing Patient Messages Enhanced by Natural Language Processing: A Top-Down Message Corpus Analysis.

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Patient portal messages are often about active symptoms or logistics. Analyzing these messages using natural language processing (NLP) can help improve clinical efficiency by better understanding patient communication.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Communication

Background:

  • Patients increasingly use asynchronous communication platforms to interact with healthcare teams.
  • Automating the classification and triage of patient messages via NLP can significantly enhance clinical efficiency.
  • Understanding patient-generated text is crucial for developing advanced NLP applications.

Purpose of the Study:

  • To characterize the content of patient portal messages using NLP methods.
  • To provide descriptive analyses of patient text data.
  • To inform the development of sophisticated NLP applications for healthcare.

Main Methods:

  • Collected approximately 3,000 patient portal messages from cardiology, dermatology, and gastroenterology departments.
  • Classified messages into 'Active Symptom', 'Logistical', 'Prescription', or 'Update' categories.
  • Utilized Named Entity Recognition (NER) with the UMLS library to identify medical concepts and analyzed message distributions.

Main Results:

  • Active Symptom and Logistical messages constituted about 67% of the corpus.
  • 'Findings' was the most frequent medical concept across message types and departments.
  • Specific keywords like 'Anatomical Sites' and 'Disorders' were common in 'Active Symptom' messages, while 'Drugs' were prevalent in 'Prescription' messages.

Conclusions:

  • Descriptive analysis of patient portal messages reveals key content themes and variations.
  • Insights into message content differences can guide the creation of more effective NLP models.
  • This study provides a foundation for improving automated message triage and clinical workflow.