Introduction: The Need for a tailored inpatient eHealth literacy tool
As digital health technologies expand, patients must effectively engage with eHealth tools to manage health in today’s Web 3.0 environment. Traditional instruments often miss the unique demands of adult inpatients, who navigate complex medical information, privacy considerations, and rapidly evolving AI-assisted health services. The Adult Inpatient eHealth Literacy Scale (AIPeHLS) responds to this gap by offering a psychometrically robust, multidimensional measure grounded in the Lily model of eHealth literacy. This article summarizes the scale’s development and validation, its theoretical basis, and implications for clinical practice and research.
Background: From eHealth literacy to inpatient contexts
eHealth literacy (eHL) describes the ability to seek, understand, appraise, and apply health information from digital sources. Inpatients face heightened vulnerability and time-sensitive decision-making, yet face barriers such as variable digital proficiency, information credibility concerns, and data privacy risks. Existing tools like eHEALS, e-HLS, eHLQ, and DHLI have advanced measurement, but they do not fully capture the Web 3.0 competencies now demanded by AI-enabled care, data tracking, and personalized health technology in hospital settings. The AIPeHLS adapts the Lily model—encompassing six literacies: traditional, information, media, health, computer, and scientific literacy—to the inpatient digital health landscape, ensuring coverage of both foundational skills and advanced competencies such as data security, AI-assisted decision support, and protective health information practices.
Methods: Building and validating a hospital-ready tool
Step 1 — Development
The initial item pool (53 items) was generated from a comprehensive literature review across Chinese and English sources (2013–2023) and refined via a two-round Delphi process with 18 experts from multiple regions and specialties. Experts evaluated item importance, relevance, and clarity, with a high authority coefficient (Cr ≈ 0.86) and strong consensus (Kendall W, P<.001). Item reduction and refinement led to a 44-item, six-dimension instrument aligned with the Lily model and designed for adult inpatients.
Step 2 — Validation
A cross-sectional validation study recruited 532 adult inpatients from a Grade A tertiary hospital in Hunan, China, using randomized cluster sampling across nine wards. We employed confirmatory factor analysis (CFA) to test the factor structure, alongside convergent and discriminant validity assessments (AVE, CR), content validity (CVI), and criterion validity via correlation with a Chinese version of eHEALS. Reliability was examined using Cronbach’s alpha, McDonald’s omega, and split-half methods.
Results: A robust, reliable, and valid instrument
The finalized AIPeHLS includes 44 items across six dimensions, with strong factor loadings and minimal cross-loadings. CFA indices indicate excellent construct validity (GFI ≈ 0.854, CFI/IFI > 0.95, RMSEA ≈ 0.048). Convergent validity is supported by AVE values above 0.50 and CR values between 0.94 and 0.97; discriminant validity is established by the square roots of AVE exceeding inter-dimension correlations. Content validity achieved high CVI scores (I-CVI 0.889–1.000; S-CVI 0.961). Criterion validity showed a strong correlation with eHEALS, endorsing its relevance as a measure of inpatient eHL in clinical contexts.
Reliability is excellent: total Cronbach’s alpha ~0.965 and omega ~0.962, with subscale alphas ranging 0.948–0.971. Split-half reliability exceeds 0.79 overall. These results support the AIPeHLS as a stable, trustworthy tool for both clinical assessment and research use.
Discussion: Implications for practice and research
The AIPeHLS provides a clinically meaningful, comprehensive assessment of inpatient eHL within the Web 3.0 health ecosystem. By capturing six literacies and their application to real-world hospital scenarios, the scale helps clinicians identify gaps, tailor digital health education, and design patient-centered eHealth interventions. For researchers, AIPeHLS enables exploration of links between eHL and health outcomes, self-management, and engagement with AI-enabled care, paving the way for precision digital health strategies in inpatient settings.
Limitations and future directions
Key limitations include the single-site validation in China and the absence of test-retest data. Future work should aim for multicenter validation across diverse populations, inclusion of longitudinal measurements, and potential calibration for cross-cultural contexts. As hospital digital ecosystems evolve, ongoing updates to AIPeHLS may be needed to capture new AI tools, privacy considerations, and patient data ownership concerns.
Conclusion
The AIPeHLS emerges as a psychometrically robust, multidimensional instrument tailored to adult inpatients in the Web 3.0 era. By delivering reliable measures of six interrelated literacies, it supports better understanding of patients’ digital health competencies and informs targeted interventions to improve engagement, safety, and outcomes in hospital care.
