Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study

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.

Validity of a Computational Linguistics-Derived Automated Health Literacy Measure Across Race/Ethnicity: Findings from The ECLIPPSE Project

“…This study is part of the NLM-funded ECLIPPSE Project (Employing Computational Linguistics to Improve Patient-Physician email Exchange), and a detailed review of methods used to develop the Literacy Profile and results and implications of this work can be found in prior reports. [14][15][18][19] Briefly, our sampling frame included over one million secure messages (SMs) exchanged between diabetes patients and providers between 2006 and 2015 at Kaiser Permanente Northern California (KPNC), a fully integrated health system that provides care to ~4.4 million patients and supports a well-developed and mature patient portal (kp .org). We selected diabetes patients for our study because more than 30 million U.S adults are living with diabetes, 20 and one quarter to one third of them has limited HL skills.…”

“…Furthermore, we have shown that the Literacy Profile has high criterion validity with respect to a reference standard of health literacy, as well as significant predictive validity with respect to health outcomes. [18][19] Our current objective is to determine whether the Literacy Profile has sufficient validity across races/ ethnicities to justify applying and scaling it in practice at a scale that could improve clinical care and population health among diverse populations.…”

Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

“…(2.1%) [89][90][91][92][93]. Other significant SBDH factors include geographic location [24,31,44,70], health literacy [43,47,88,94], social patterns (sexual health, adverse experiences and behavioral attitudes) [33,52,73,95], social environment [41,56,77], health access [21,54,88], living condition [20,31,35], social behavior [54,63,82], and financial insecurity [81,86].…”

A Fuzzy MCDM Approach for Structured Comparison of the Health Literacy Level of Hospitals

“…Studying and understanding the health literacy from a systemic perspective is based on an understanding of the inputs, processes, relationships, and outputs of many relevant components considered one of the recent research and application directions for developing solutions that limit the effects of this phenomenon [14,15]. The influential relationship between the individual's competencies, system's demands, and its complexities, where health literacy decisions and actions are taken, and the primary health literacy of an individual (health literacy abilities) in this system, is one of the fundamental justifications for this research and its applied directions [10,16]. The health literacy concept is used as a tool to [10]: simplify and understand the health systems, which are often complicated,, improve the service provided; structure services in ways that maximize simplicity of systems and reduce challenges that limit access for services, ensure meeting the necessary health literacy requirements and preferences of all individuals that the healthcare organization serves; and improve patient outcomes and healthcare quality.…”

“…MAPPING BETWEEN THE ANALYTIC HIERARCHY PROCESS SCORE AND EVALUATION GAPS www.ijacsa.thesai.org TABLE XIV. MAPPING BETWEEN THE ANALYTIC HIERARCHY PROCESS SCORE AND FIVE POINT EVALUATION GAPS[16] …”