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Background: Digital health technologies and artificial intelligence (AI) are shifting healthcare from reactive treatment toward predictive and personalized care. The 10P-Health framework extends earlier P-models through ten principles: Predictive, Preventive, Personalized/Precision, Participatory, Pertinent, Proactive, Privacy-preserving, Pervasive, Permanent, and Platform-based. Public readiness for such a framework in Bavaria has not been established. Objective: This study assessed public readiness for the 10P-Health framework in Bavaria and examined whether operational readiness (OR), legal and regulatory requirements (LRR), stakeholder engagement (SE), and technological infrastructure (TI) predict its perceived feasibility. Methods: A cross-sectional survey of 200 adult residents of Bavaria was conducted using an online questionnaire. All constructs were measured with five-point Likert scales. Data were analyzed with descriptive statistics, reliability analysis, principal component analysis, Pearson correlations, and multiple linear regression. Results: Respondents were predominantly young and well-educated, and 77.5% had previously used a digital health platform, medical device, or health-related application. All constructs showed acceptable internal consistency (Cronbach's alpha = .706–.768). Perceived feasibility correlated significantly with all four predictors. The regression model explained 57.7% of the variance in perceived feasibility. TI (β = .402, p < .001), OR (β = .330, p < .001), and SE (β = .207, p = .001) were significant predictors, whereas LRR was not (β = .029, p = .614). Resource adequacy and provider preparedness received the lowest ratings. Conclusions: The findings indicate moderate readiness among a predominantly young, highly educated, and digitally experienced sample. Perceived feasibility was associated primarily with technological infrastructure, operational readiness, and stakeholder engagement, whereas legal requirements appeared to function as a background condition rather than a direct driver.
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Al-Sardy et al. (2026) studied this question.
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