Paper
A Dataset and Resources for Identifying Patient Health Literacy Information from Clinical Notes
Authors
Madeline Bittner, Dina Demner-Fushman, Yasmeen Shabazz, Davis Bartels, Dukyong Yoon, Brad Quitadamo, Rajiv Menghrajani, Leo Celi, Sarvesh Soni
Abstract
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).
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.19082v1</id>\n <title>A Dataset and Resources for Identifying Patient Health Literacy Information from Clinical Notes</title>\n <updated>2026-03-19T16:09:40Z</updated>\n <link href='https://arxiv.org/abs/2603.19082v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.19082v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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).</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n <published>2026-03-19T16:09:40Z</published>\n <arxiv:primary_category term='cs.CL'/>\n <author>\n <name>Madeline Bittner</name>\n </author>\n <author>\n <name>Dina Demner-Fushman</name>\n </author>\n <author>\n <name>Yasmeen Shabazz</name>\n </author>\n <author>\n <name>Davis Bartels</name>\n </author>\n <author>\n <name>Dukyong Yoon</name>\n </author>\n <author>\n <name>Brad Quitadamo</name>\n </author>\n <author>\n <name>Rajiv Menghrajani</name>\n </author>\n <author>\n <name>Leo Celi</name>\n </author>\n <author>\n <name>Sarvesh Soni</name>\n </author>\n </entry>"
}