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

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 phy

SUBMITTER: Balyan R 

PROVIDER: S-EPMC6386302 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature