ArticleHealth services research2021
Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.
Article in Health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 19 citations in OpenAlex.
- Computational Use of Patient-Provider Secure Messaging Data to Achieve Better Clinical Efficiency and Quality of Communication: A Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Pooled it
- Challenges and Opportunities for Professional Medical Publications Writers to Contribute to Plain Language Summaries (PLS) in an AI/ML Environment - A Consumer Health Informatics Systematic Review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Pooled it
- A scoping review of studies on secure messaging through patient portals: persistent challenges and potential solutions.npj health systems · 2026Article
- A Dataset and Resources for Identifying Patient Health Literacy Information from Clinical Notes.ArXiv · 2026Article
- Improving Emergency Department Visit Risk Prediction: Exploring the Operational Utility of Applied Patient Portal Messages.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Helping patient educators meet health literacy needs: End-user testing and iterative development of an innovative health literacy editing tool.PEC innovation · 2023Article
- Automated Strategy Feedback Can Improve the Readability of Physicians' Electronic Communications to Simulated Patients.International journal of human-computer studies · 2023Article
- Multiple Automated Health Literacy Assessments of Written Health Information: Development of the SHeLL (Sydney Health Literacy Lab) Health Literacy Editor v1.JMIR formative research · 2023Article
- Precision communication: Physicians' linguistic adaptation to patients' health literacy.Science advances · 2021Article
- Validity of a Computational Linguistics-Derived Automated Health Literacy Measure Across Race/Ethnicity: Findings from The ECLIPPSE Project.Journal of health care for the poor and underserved · 2021Article
- Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study.Health services research · 2021Article
- Challenges and solutions to employing natural language processing and machine learning to measure patients' health literacy and physician writing complexity: The ECLIPPSE study.Journal of biomedical informatics · 2021Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
- A Framework of AI-Based Approaches to Improving eHealth Literacy and Combating Infodemic.Frontiers in public health · 2021Review
- The evolving landscape of artificial intelligence in patient education: A bibliometric knowledge mapping study.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing. DATA SOURCE: With respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system's electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records. STUDY
designRetrospective study used natural language processing and machine learning to generate five unique "Literacy Profiles" by employing various sets of linguistic indices: Flesch-Kincaid (LP_FK); basic indices of writing complexity, including lexical diversity (LP_LD) and writing quality (LP_WQ); and advanced indices related to syntactic complexity, lexical sophistication, and diversity, modeled from self-reported (LP_SR), and expert-rated (LP_Exp) health literacy. We first determined the performance of each literacy profile relative to self-reported and expert-rated health literacy to discriminate between high and low health literacy and then assessed Literacy Profiles' relationships with known correlates of health literacy, such as patient sociodemographics and a range of health-related outcomes, including ratings of physician communication, medication adherence, diabetes control, comorbidities, and utilization. PRINCIPAL
findingsLP_SR and LP_Exp performed best in discriminating between high and low self-reported (C-statistics: 0.86 and 0.58, respectively) and expert-rated health literacy (C-statistics: 0.71 and 0.87, respectively) and were significantly associated with educational attainment, race/ethnicity, Consumer Assessment of Provider and Systems (CAHPS) scores, adherence, glycemia, comorbidities, and emergency department visits.
conclusionsSince health literacy is a potentially remediable explanatory factor in health care disparities, the development of automated health literacy indicators represents a significant accomplishment with broad clinical and population health applications. Health systems could apply literacy profiles to efficiently determine whether quality of care and outcomes vary by patient health literacy; identify at-risk populations for targeting tailored health communications and self-management support interventions; and inform clinicians to promote improvements in individual-level care.
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What OpenQuestion holds
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.