A methodological framework for integrating model-guided medicine and multidimensional information management systems: application in anti-aging healthcare.

Purpose: This study introduces a methodological framework for integrating model-guided medicine (MGM) with multidimensional information management systems (MIMMS) to address healthcare's digital transformation challenges. Anti-aging medicine is used as a case study to illustrate the framework's adaptability and practical application in terms of data integration, workflow transparency, and AI auditability.

Methods: The methodology combines MGM and MIMMS to manage complex healthcare data through patient-specific, semantic, and syntactic models. Automated workflows streamline processes from data acquisition to decision-making. Integration is demonstrated with metabolic assessments and patient-specific modeling.

Results: The framework effectively integrates multi-domain data, enhancing interoperability, workflow transparency, and AI auditability. A case study in anti-aging medicine illustrates its practical utility and scalability, addressing limitations of existing systems and highlighting potential for broader applications.

Conclusion: This methodological framework offers a novel approach to advancing digital healthcare transformation by enabling integrated, patient-centric workflows. While not yet applied in a clinical setting, its conceptual application to anti-aging medicine illustrates the framework's adaptability and potential to enhance healthcare standards across various domains. Future work will focus on real-world validation and refinement to further demonstrate its practical impact.