| 1 |
Precision communication: Physicians’ linguistic adaptation to patients’ health literacy
|
|
|
|
In: Sci Adv (2021)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Multi-document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links
|
|
|
|
In: Artificial Intelligence in Education (2020)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study
|
|
|
|
In: Health Serv Res (2020)
|
|
Abstract:
OBJECTIVE: To 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 DESIGN: Retrospective 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 FINDINGS: LP_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. CONCLUSIONS: Since 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.
|
|
Keyword:
Evaluation Tools
|
|
URL: https://doi.org/10.1111/1475-6773.13560
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839650/
http://www.ncbi.nlm.nih.gov/pubmed/32966630
|
|
BASE
|
|
Hide details
|
|
|
| 4 |
Extended Multi-document Cohesion Network Analysis Centered on Comprehension Prediction
|
|
|
|
In: Artificial Intelligence in Education (2020)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Sequence-to-Sequence Models for Automated Text Simplification
|
|
|
|
In: Artificial Intelligence in Education (2020)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Challenges and solutions to employing natural language processing and machine learning to measure patients’ health literacy and physician writing complexity: The ECLIPPSE study
|
|
|
|
In: J Biomed Inform (2020)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Secure Messaging with Physicians by Proxies for Patients with Diabetes: Findings from the ECLIPPSE Study
|
|
|
|
In: J Gen Intern Med (2019)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Incorporating Learning Characteristics into Automatic Essay Scoring Models: What Individual Differences and Linguistic Features Tell Us about Writing Quality ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
To Aggregate or Not? Linguistic Features in Automatic Essay Scoring and Feedback Systems
|
|
|
|
In: Journal of Writing Assessment, vol 8, iss 1 (2015)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Linguistic microfeatures to predict L2 writing proficiency: A case study in Automated Writing Evaluation
|
|
|
|
In: Journal of Writing Assessment, vol 7, iss 1 (2014)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
What's so simple about simplified texts? A computational and psycholinguistic investigation of text comprehension and text processing
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
