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Health Literacy

Health Literacy

Health literacy is an individual's or a community's capacity to comprehend, receive, read, and use relevant healthcare information and services. The World Health Organization (WHO, 2018) defines health literacy as the cognitive and social skills that determine the ability of individuals to gain access to, understand, and use information in ways that promote and maintain good health. As a result, the WHO helps individuals manage long-term health concerns, participate in preventative...

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Development of Machine Learning Algorithms for Identifying Patients With Limited Health Literacy.

Dylan Koole^1,2, Oscar Shen¹, Amanda Lans^1,3

¹Department of Orthopaedic Surgery, Orthopaedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Journal of Evaluation in Clinical Practice

|November 22, 2024

Summary

This summary is machine-generated.

Machine learning models can identify patients with limited health literacy (HL) in spine clinics. This approach aids early intervention without in-person screening, improving patient outcomes.

Keywords:

health literacy machine learning orthopaedic surgery social determinants of health spine

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Area of Science:

Health Informatics
Machine Learning in Healthcare
Patient Education

Background:

Limited health literacy (HL) is linked to adverse health outcomes and increased healthcare costs.
Identifying patients with limited HL in clinical settings is challenging.
Machine learning (ML) offers a potential solution for efficient HL screening.

Purpose of the Study:

To develop and evaluate ML algorithms for identifying patients at risk of limited HL.
Focus on patients within an urban academic outpatient spine clinic.

Main Methods:

A cross-sectional survey study included 753 English-speaking spine patients (age >18).
Health literacy was assessed using the Newest Vital Sign.
ML models, including Elastic-Net Penalized Logistic Regression, were trained using patient data (demographics, EHRs).

Main Results:

34.4% of patients had limited HL.
Key predictors included age, socioeconomic factors (Area Deprivation Index, Social Vulnerability Index), insurance, BMI, race, education, and employment.
Elastic-Net Penalized Logistic Regression demonstrated the best performance (c-statistic: 0.766).

Conclusions:

ML algorithms can effectively identify patients with limited HL in spine care settings.
The developed models offer a non-intrusive method for HL screening.
Early identification via ML can facilitate timely interventions to mitigate negative health consequences.