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Using Natural Language Processing and Machine Learning to Classify Health Literacy from Secure Messages: The ECLIPPSE Study
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Balyan, Renu; Crossley, Scott A.; Brown, William, III; Karter, Andrew J.; McNamara, Danielle S.; Liu, Jennifer Y.; Lyles, Courtney R.; Schillinger, Dean – Grantee Submission, 2019
Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patient health literacy challenging. The objective of this study was to develop and validate…
Descriptors: Patients, Literacy, Health Services, Profiles
Employing Computational Linguistics Technologies to Identify Limited Patient Health Literacy: Findings from the ECLIPPSE Study
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Schillinger, Dean; Balyan, Renu; Crossley, Scott A.; McNamara, Danielle S.; Liu, Jennifer Y.; Karter, Andrew J. – Grantee Submission, 2020
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.…
Descriptors: Literacy, Profiles, Computational Linguistics, Syntax