Intelligent ERP framework for financial optimization in smart healthcare institutions

Smart healthcare institutions are increasingly dependent on IoT-enabled diagnostics, AI-enabled clinical systems and cloud-based hospital infrastructures, however, monetary management systems in Enterprise Resource Planning (ERP) systems are still mostly transactional and reactive. Traditional ERP platforms are void of adaptive financial intelligence to deal with dynamic patient inflow, reimbursement variance and varying operational costs. Based on this work, an Intelligent ERP Framework combining predictive and constrained financial optimization algorithms is proposed to improve cost minimization, revenue forecasting accuracy and long-term financial sustainability in smart healthcare environments. A hybrid architecture that combines machine learning-based revenue prediction, multi-variable cost modelling and real-time ERP financial data streams was built and tested using synthetic 24-month-long hospital financial datasets that contained 50,000 simulated patient records. Simulation results show an increase in Revenue Growth Rate by 17.9% and in Cost Reduction Index by 15.4% and increase in the Financial Efficiency Ratio by 1.36 from 1.10 with respect to conventional ERP systems. Operational cost variability decreased by 22% and billing cycle delay decreased by 18% and the accuracy of revenue forecast rose to 94.6%. The proposed framework resulted in a Composite Optimization Score gain from 0.62 to 0.89 implying significant financial stability gains. As part of the ERP architectures with predictive intelligence, the Intelligent ERP Framework facilitates adaptive financial decision making and budget allocation strategies and helps maintain a better revenue sustainability in a digitally transformed smart healthcare institution.