A Dataset and Resources for Identifying Patient Health Literacy Information from Clinical Notes
This provides a resource for researchers and clinicians to improve health literacy screening, but it is incremental as it focuses on dataset creation rather than novel detection methods.
The authors tackled the problem of automated detection of patient health literacy from clinical notes by introducing HEALIX, the first publicly available annotated dataset with 589 notes across 9 types, and benchmarked it using zero-shot and few-shot prompting across four LLMs.
Health literacy is a critical determinant of patient outcomes, yet current screening tools are not always feasible and differ considerably in the number of items, question format, and dimensions of health literacy they capture, making documentation in structured electronic health records difficult to achieve. Automated detection from unstructured clinical notes offers a promising alternative, as these notes often contain richer, more contextual health literacy information, but progress has been limited by the lack of annotated resources. We introduce HEALIX, the first publicly available annotated health literacy dataset derived from real clinical notes, curated through a combination of social worker note sampling, keyword-based filtering, and LLM-based active learning. HEALIX contains 589 notes across 9 note types, annotated with three health literacy labels: low, normal, and high. To demonstrate its utility, we benchmarked zero-shot and few-shot prompting strategies across four open source large language models (LLMs).