AI-Powered Predictive and Prescriptive Analytics for Intelligent Healthcare Resource Optimization

Abstract

This study has examined how artificial-intelligence-powered predictive and prescriptive analytics affects the optimization of healthcare resources and, through that mechanism, the performance of care delivery, and it has developed and tested a framework linking analytic capability to resource outcomes across inpatient beds, operating theatres, critical care, diagnostics, and the clinical workforce. The central problem has been that hospital resource management remains largely reactive and calendar-driven, so that capacity pressure is recognised only when it manifests as a full ward, a boarded emergency patient, a cancelled operation, or an unplanned overtime shift, by which point the available responses are costly, disruptive, and clinically consequential, while the substantial forecasting capability now present in many provider organisations is seldom converted into the allocation, scheduling, and rostering decisions that would relieve the pressure. Guided by five research questions, concerning how predictive and prescriptive analytics affects resource optimization performance, which resource domains are most effectively optimised, how forecast usefulness varies with decision horizon, what risks reactive capacity management carries, and whether resource optimization effectiveness transmits analytic capability into care delivery performance, the study has proposed a framework comprising five contributing constructs: clinical and operational data integration; predictive demand and acuity forecasting; prescriptive capacity and scheduling optimisation; clinical governance, explainability and equity assurance; and operational flexibility and workforce adaptability, with resource optimization effectiveness as the proximal outcome and care delivery performance as the distal outcome. A quantitative, cross-sectional design supplemented by a discrete-event simulation has been used, and data have been collected from hospital operations and capacity managers, clinical leads, nursing leadership, health informatics and data science staff, and finance and planning managers across academic medical centres, community hospitals, integrated delivery networks, specialty hospitals, and ambulatory networks. Out of 386 distributed questionnaires, 312 valid responses have been retained, producing an 80.8% valid response rate. The analysis has included descriptive statistics, reliability and validity testing, correlation analysis, hierarchical multiple regression, bootstrapped mediation analysis, and a simulation evaluation of census forecasting, bed-day utilisation, and the cost–service frontier. The findings have shown that all five constructs were significantly and positively associated with resource optimization effectiveness, that the model was significant, F(5, 306) = 81.44, p < .001, explaining 57.1% of the variance, and that prescriptive capacity and scheduling optimisation and clinical and operational data integration were the strongest predictors. Resource optimization effectiveness in turn predicted care delivery performance and partially mediated the relationship between prescriptive capability and performance, carrying 62.2% of the total effect. The simulation showed that prescriptive allocation reduced bed-day consumption by 11.6% per thousand admissions and shifted the institution onto a materially better cost–service frontier, but also that predictive usefulness decays sharply beyond a forty-eight-hour horizon.