ArticleJournal of health care for the poor and underserved2021
Validity of a Computational Linguistics-Derived Automated Health Literacy Measure Across Race/Ethnicity: Findings from The ECLIPPSE Project.
Article in Journal of health care for the poor and underserved, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- Equitable Discharge Teaching During COVID-19: Paediatric Emergency Nurses' Perspectives From Qualitative Interviews.Journal of advanced nursing · 2026Article
- Association of Health Literacy with Sociodemographic Factors and Medication Adherence Among Primary Health Care Users in Montenegro.Healthcare (Basel, Switzerland) · 2026Article
- Precision communication: Physicians' linguistic adaptation to patients' health literacy.Science advances · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Limited health literacy (HL) partially mediates health disparities. Measurement constraints, including lack of validity assessment across racial/ethnic groups and administration challenges, have undermined the field and impeded scaling of HL interventions. We employed computational linguistics to develop an automated and novel HL measure, analyzing >300,000 messages sent by >9,000 diabetes patients via a patient portal to create a Literacy Profiles. We carried out stratified analyses among White/non-Hispanics, Black/non-Hispanics, Hispanics, and Asian/Pacific Islanders to determine if the Literacy Profile has comparable criterion and predictive validities. We discovered that criterion validity was consistently high across all groups (c-statistics 0.82-0.89). We observed consistent relationships across racial/ethnic groups between HL and outcomes, including communication, adherence, hypoglycemia, diabetes control, and ED utilization. While concerns have arisen regarding bias in AI, the automated Literacy Profile appears sufficiently valid across race/ethnicity, enabling HL measurement at a scale that could improve clinical care and population health among diverse populations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.