Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.

ABSTRACT:

Objective

To develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing.

Data source

With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records.

Study design

Retrospective study used natural language processing and machine learning to generate five unique "Literacy Profiles" by employing various sets of linguistic indices: Flesch-Kincaid (LP_FK); basic indices of writing complexity, including lexical diversity (LP_LD) and writing quality (LP_WQ); and advanced indices

SUBMITTER: Schillinger D 

PROVIDER: S-EPMC7839650 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature