Employing Computational Linguistics Technologies to Identify Limited Patient Health Literacy: Findings from the ECLIPPSE Study

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Autor/inn/en	Schillinger, Dean; Balyan, Renu; Crossley, Scott A.; McNamara, Danielle S.; Liu, Jennifer Y.; Karter, Andrew J.
Titel	Employing Computational Linguistics Technologies to Identify Limited Patient Health Literacy: Findings from the ECLIPPSE Study
Quelle	(2020), (14 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	ORCID (Balyan, Renu) ORCID (Karter, Andrew J.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Literacy; Profiles; Computational Linguistics; Syntax; Patients; Health; Natural Language Processing; Information Technology; Medical Evaluation; Records (Forms); Readability Formulas; Correlation; Physician Patient Relationship; Pronunciation; Drug Therapy; Compliance (Psychology); Diabetes; Comorbidity; Specialists; Educational Attainment; Race; Ethnicity; Scores; Hospitals; At Risk Persons; Identification; Self Management; Health Services; Outcomes of Treatment; California; Flesch Reading Ease Formula + Suchen Sie Ihr Suchwort? Alphabetisierung; Schreib- und Lesefähigkeit; Charakterisierung; Profilanalyse; Linguistics; Computerlinguistik; Patient; Gesundheit; Natürliche Sprache; Informationstechnologie; Formularsammlung; Korrelation; Arzt-Patient-Beziehung; Aussprache; Bildungsabschluss; Bildungsgut; Rasse; Abstammung; Ethnizität; Krankengymnast; Krankenhaus; Risikogruppe; Identifikation; Identifizierung; Selbstmanagement; Health service; Gesundheitsdienst; Gesundheitswesen; Kalifornien
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. [This is the online version of an article published in "Health Services Research."] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01