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Area of Science:

  • Medical Informatics
  • Vascular Surgery
  • Public Health

Background:

  • Screening and surveillance are critical for preventing abdominal aortic aneurysm (AAA) rupture.
  • Natural language processing (NLP) offers a novel approach to assess surveillance efficacy.
  • This study evaluated NLP's role in identifying patients for AAA surveillance.

Purpose of the Study:

  • To assess the efficacy of natural language processing (NLP) software in evaluating abdominal aortic aneurysm (AAA) surveillance.
  • To identify patients with AAA and determine their compliance with surveillance protocols using NLP.

Main Methods:

  • Utilized NLP software to analyze 7 years of imaging reports from a single institution.
  • Identified a cohort of patients with abdominal aortic aneurysms (AAAs).
  • Reviewed electronic medical records to collect demographic data and assess surveillance compliance.

Main Results:

  • NLP identified 1424 patients with AAAs; 77.6% were male, mean age 74 ± 10 years.
  • Only 39% of patients were under active surveillance; 61% were not participating in recommended surveillance.
  • Surveillance compliance did not significantly differ by gender, race, or ethnicity.

Conclusions:

  • A significant proportion of patients with abdominal aortic aneurysms are not compliant with recommended surveillance.
  • NLP can enhance traditional screening and surveillance by identifying patients with gaps in care.
  • Integrating NLP can improve patient outcomes by ensuring adherence to AAA surveillance protocols.