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

PloS One
Renu BalyanDean Schillinger

Abstract

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate "literacy profiles" as automated indicators of patients' health literacy to facilitate a non-intrusive, economic and more comprehensive characterization of health literacy among a health care delivery system's membership. To this end, three literacy profiles were generated based on natural language processing (combining computational linguistics and machine learning) using a sample of 283,216 secure messages sent from 6,941 patients to their primary care physicians. All patients were participants in Kaiser Permanente Northern California's DISTANCE Study. Performance of the three literacy profiles were compared against a gold standard of patient self-reported health literacy. Associations were analyzed between each literacy profile and patient demographics, health outcomes and healthcare utilization. T-tests were used for numeric data such as A1C, Charlson comorbidity index and healthcare utilizat...Continue Reading

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Citations

Jul 30, 2019·JAMA Pediatrics·Tracy A Lieu, Gary L Freed
Nov 11, 2020·Health Literacy Research and Practice·Elaine C Khoong, Dean Schillinger
Nov 9, 2021·Computers, Informatics, Nursing : CIN·Mary Anne SchultzAlvin D Jeffery
Dec 18, 2021·Science Advances·Dean SchillingerAndrew J Karter

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Software Mentioned

Wordnet
ECLIPPSE
SVM
LinKBase
TerMine
SEntiment ANalysis and Cognition Engine ( SÉANCE ) Writing Ass...
Weka
medical language extraction and encoding system ( MedLee ) NLP...
HLCOMB
TAASSC

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