Overview: Why a New Inpatient eHealth Literacy Tool?
Digital health tools and generative AI are reshaping modern care, especially for inpatients who must quickly access and interpret health information. Traditional eHealth literacy (eHL) scales often fall short in the hospital setting or fail to reflect the demands of Web 3.0, where data security, personalization, and advanced digital competencies matter. The Adult Inpatient eHealth Literacy Scale (AIPeHLS) was developed to fill this gap, offering a psychometrically robust instrument grounded in the Lily model of six interrelated literacies.
Foundations: The Lily Model and Web 3.0 Demands
The Lily model defines 6 literacies—traditional, information, media, health, computer, and scientific literacy—that together describe how adults engage with digital health information. The AIPeHLS translates these literacies into practical inpatient scenarios across Web 1.0 to Web 3.0. It integrates foundational abilities (such as numeracy and information retrieval) with advanced tasks like assessing data privacy risks, recognizing AI-generated content, and adapting to new eHealth tools. This approach ensures the instrument measures both core skills and emerging competencies essential for hospitalized patients navigating digital care pathways.
Development: From Item Pool to a 44-Item Scale
The item pool began with a comprehensive review of 934 articles, yielding 53 candidate items. A two-round Delphi panel of 18 experts from 12 regions in China refined these items, focusing on relevance, clarity, and coverage of Web 3.0 considerations. The final instrument comprises 44 items spanning six dimensions and uses a 5-point Likert scale.
Key development steps included:
– Systematic literature review across Chinese and English databases (2013–2023).
– Delphi rounds to establish content validity and expert consensus.
– Pilot testing with 100 adult inpatients to assess item clarity and reliability.
Validation: Strong Psychometric Properties
A cross-sectional validation study recruited 532 adult inpatients across nine wards in a Grade A tertiary hospital in Hunan, China. The AIPeHLS demonstrated:
- Construct validity supported by both exploratory and confirmatory factor analyses; six distinct dimensions with good factor loadings.
- Convergent validity with AVE > 0.50 and CR > 0.70; discriminant validity confirmed via inter-dimension comparisons.
- Content validity with a high scale- and item-level CVI (>0.90 and >0.78, respectively).
- Criterion validity showing strong correlation with the Chinese version of eHEALS, the widely used standard.
- Reliability metrics indicating excellent internal consistency (Cronbach’s alpha up to 0.965; McDonald’s omega up to 0.971) and acceptable split-half reliability.
Model fit indices from CFA indicated a satisfactory fit, supporting the scale’s structural validity for clinical and research use in inpatient settings.
Why This Matters for Clinicians and Researchers
With the rising prevalence of internet hospitals, AI-driven diagnostics, and personalized digital health strategies, accurately assessing an inpatients’ eHL is crucial. The AIPeHLS enables clinicians to identify specific gaps—whether in basic information seeking, privacy awareness, or data interpretation—and to tailor support, education, and digital tool design accordingly. For researchers and policymakers, the instrument offers a reliable way to gauge eHL as a moderator or outcome in studies evaluating digital health interventions, patient engagement strategies, and AI-assisted care workflows.
Limitations and Future Directions
The study acknowledges limitations, including validation in a single hospital and the absence of test-retest reliability due to short hospital stays. Future work should explore multicenter applicability, longitudinal stability, and invariance across diverse populations. Additional efforts could examine how AIPeHLS scores relate to concrete health outcomes, adherence, and patient satisfaction in various clinical contexts.
Conclusion: A Practical Tool for Today and Tomorrow
The AIPeHLS offers a psychometrically robust, multidimensional measure of eHealth literacy tailored to adult inpatients in the Web 3.0 era. By capturing both foundational and advanced competencies, it supports targeted interventions that empower patients, enhance patient–provider communication, and optimize the integration of digital health tools in inpatient care.
