IntelliCare: A Pareto-Efficient Multi-Objective Model for Balancing Cost and Quality in Smart Healthcare Systems

Modern healthcare systems operate under increasing pressure to deliver superior patient outcomes while constrained by limited financial and operational resources. Balancing cost efficiency with service quality remains a central challenge for healthcare administrators. This study introduces IntelliCare, a Pareto-efficient hybrid multi-objective optimization framework that models healthcare resource allocation as a bi-objective problem—minimizing Total Operational Cost (ToC) and maximizing Total Quality of Service (TQoS). The proposed Hybrid Multi-Objective Evolutionary Algorithm (Hybrid MOEA) integrates elitist selection and diversity preservation mechanisms from classical evolutionary paradigms to ensure reliable convergence and wide Pareto-front coverage. A nonlinear formulation captures diminishing returns in resource deployment, realistically reflecting hospital interdependencies across multiple departments. Experimental evaluations on synthetic healthcare datasets reveal that the Hybrid MOEA consistently surpasses baseline algorithms, yielding higher Hypervolume (HV) and lower Generational Distance (GD), Inverted GD (IGD), and Spread (Δ) values. Overall, the IntelliCare framework establishes a scalable, data-driven decision-support model that enables administrators to achieve cost-effective, quality-centered, and operationally sustainable healthcare management.