This paper presents REACH, an advanced machine learning framework to deliver comprehensive decision support for older patient prioritisation. The framework employs a Mixture of Experts (MoE) architecture, integrating multiple specialised predictive models to simultaneously address four critical dimensions: complex care pathway classification, aged residential care prediction, early supported discharge assessment, and mortality risk evaluation. The MoE architecture features a context-aware attention-based gating mechanism that dynamically adjusts expert contributions based on patient characteristics and operational factors. The framework’s implements an automated model selection, and hyperparameter optimisation through a Combined Algorithm Selection and Hyperparameter-tuning methodology. This study is a conceptual theory extending on the fundamentals of REACH to create a multi-dimensional model. This work addresses a critical gap in healthcare delivery by providing a comprehensive, data-driven approach to optimising care pathways for older patients while considering resource constraints and operational efficiency.