Developing and Testing Automatic Models of Patient Communicative Health Literacy Using Linguistic Features: Findings from the ECLIPPSE study

Patients with diabetes and limited health literacy (HL) may have suboptimal communication exchange with their health care providers and be at elevated risk of adverse health outcomes. These difficulties are generally attributed to patients’ reduced ability to both communicate and understand health-related ideas as well as physicians’ lack of skill in identifying those with limited HL. Understanding and identifying patients with barriers posed by lower HL to improve healthcare delivery and outcomes is an important research avenue. However, doing so using traditional methods has proven difficult and infeasible to scale. This study using corpus analyses, expert human ratings of HL, and natural language processing (NLP) approaches to estimate HL at the individual patient level. The goal of the study is to better understand HL from a linguistic perspective and to open new research areas to enhance population management and individualized care. Specifically, this study examines HL as a function of patients’ demonstrated ability to communicate health-related information to their providers via secure messages. The study develops an NLP-based HL model and validates the model by predicting patient-related events such as medical outcomes and hospitalizations. Results indicate that the developed model predicts human ratings of HL with ~80% accuracy. Validation indicates that lower HL patients are more likely to be nonwhite and have lower educational attainment. In addition, patients with lower HL suffered more negative health outcomes and had higher healthcare service utilization.