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Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE

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This study introduces "literacy profiles," an automated method using natural language processing to assess patient health literacy. This approach offers an economical and non-intrusive way to identify individuals with limited health literacy and associated health risks.

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Area of Science:

  • Health Informatics
  • Natural Language Processing in Healthcare
  • Patient Health Literacy Assessment

Background:

  • Limited health literacy significantly hinders effective healthcare delivery and patient outcomes.
  • Current methods for assessing health literacy, relying on self-reporting, are often time-consuming and perceived as intrusive, complicating widespread classification.
  • There is a need for automated, non-intrusive, and economical methods to characterize patient health literacy across large populations.

Purpose of the Study:

  • To develop and validate novel

Main Methods:

  • Generated three literacy profiles using natural language processing (NLP) techniques, combining computational linguistics and machine learning.
  • Analyzed a large dataset of 283,216 secure patient-physician messages from 6,941 participants in the Kaiser Permanente Northern California DISTANCE Study.
  • Validated literacy profiles against a gold standard of patient self-reported health literacy and analyzed associations with demographics, health outcomes, and healthcare utilization using t-tests and chi-square tests.

Main Results:

  • Literacy profiles demonstrated varying test characteristics, with C-statistics ranging from 0.61 to 0.74.
  • Patients identified with limited health literacy via these profiles were generally older, of minority status, and exhibited poorer medication adherence, glycemic control, and higher rates of comorbidities, hypoglycemia, and healthcare utilization.
  • The findings align with previous research linking limited health literacy to adverse health outcomes.

Conclusions:

  • This study successfully employed NLP to estimate patient health literacy, introducing 'literacy profiles' as a novel assessment tool.
  • Literacy profiles provide an automated, economical, and non-intrusive method for identifying patients with limited health literacy.
  • This approach facilitates the identification of vulnerable patient populations, enabling targeted interventions to improve healthcare delivery and outcomes.