Renu Balyan|Scott A. Crossley|William Brown|Andrew J. Karter|Danielle S. McNamara|Jennifer Y. Liu|Courtney R. Lyles|Dean Schillinger
Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study
Arizona State University|Georgia State University|University of California, San Francisco|Kaiser Permanente|Arizona State University|Kaiser Permanente|San Francisco General Hospital|University of California, San Francisco|Kaiser Permanente|University of California, San Francisco|Kaiser Permanente|San Francisco General Hospital
limited health literacy barrier optimal healthcare delivery outcomes current measures requiring patients selfreport limitations timeconsuming may considered intrusive makes widespread classification patient health literacy challenging objective study develop validate literacy profiles automated indicators patients health literacy facilitate nonintrusive economic comprehensive characterization health literacy among health care delivery systems membership end three literacy profiles generated based natural language processing combining computational linguistics machine learning using sample 283216 secure messages sent 6941 patients primary care physicians patients participants kaiser permanente northern californias distance study performance three literacy profiles compared gold standard patient selfreported health literacy associations analyzed literacy profile patient demographics health outcomes healthcare utilization ttests used numeric data a1c charlson comorbidity index healthcare utilization rates chisquare tests categorical data sex race poor adherence severe hypoglycemia literacy profiles varied test characteristics cstatistics ranging 061074 relations literacy profiles health outcomes revealed patterns consistent previous health literacy research patients identified via literacy profiles indicative limited health literacy older likely minority status b poorer medication adherence glycemic control c exhibited higher rates hypoglycemia comorbidities healthcare utilization represents first successful attempt employ natural language processing estimate health literacy literacy profiles offer automated economical way identify patients limited health literacy greater vulnerability poor health outcomes