AI-powered Predictive Analytics for Hospital Resource Allocation and Revenue Cycle Management
DOI:
https://doi.org/10.56472/ICCSAIML25-105Keywords:
AI in healthcare, predictive analytics, hospital resource allocation, revenue cycle management, machine learning, EHR, big data, cost optimization, patient care, workflow automationAbstract
AI-powered predictive analytics is altering hospital revenue cycle management (RCM) & resource allocation. It accomplishes this by improving patient care, streamlining operations, & simplifying financial processes. This paper investigates how AI -powered models employ real-time & historical data to forecast trends in patient admissions, manage staffing levels, guarantee completely occupied beds, & enhance supply chain flow of products. By means of effective billing, turn down claim rejections, & simplified cash flow prediction, AI also enhances RCM. Using data analytics & Machine learning techniques, AI combines electronic health records (EHR), patient demographic & financial data to identify usable insights. Important findings show that by matching supply with demand, predictive analytics not only reduces costs but also enhances patient outcomes. AI-driven RCM systems also increase total financial performance, reduce lost income, & help to make refund policies effective. AI is becoming increasingly useful as issues in healthcare systems get worse in helping hospitals be more proactive, patient-centered, & financially stable. Including technology into hospital operations is not only a fresh concept but also necessary to improve decisions, increase efficiency, & ensure long-term survival of the institution
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