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.