Engineering Intelligent Health Systems: AI-Powered Business Analytics for Informed Clinical Decision-Making

Abstract

The science of intelligent health systems (IHS) is crucial to the fundamental transformation of modern healthcare by embedding artificial intelligence (AI), robust data analytics, and resilient system architecture. IHS can handle unlimited clinical, imaging, and biomarker data through scalable architecture and interoperable platforms. The main components are acquisition devices, linear extraction and transformation lines, a computational engine driven by AI, and interfaces for clinical decision making, all of which require tight integration of engineering principles. Between 2018 and 2024, hospitals implementing IHS reported a 60% faster turnaround time for diagnostics, a 25% to 30% decrease in operating costs, and a 40% increase in efficiency for clinical workflows. The engineering architecture includes containerized environments, edge-computing devices, micro services architecture, and standardized health communications protocols. In this review, we examined how engineering principles facilitate data integrity, fault tolerance, scalability, and system performance. We also studied the systems that support the AI models that provide predictive diagnostics, risk stratification, and real-time treatment plan recommendations. The examples from various healthcare systems, additional to rural deployments and academic hospitals, demonstrate that an IHS is adaptable and scalable if it is properly engineered and, consequently, used appropriately. For example, business analytics platforms can be incorporated into the IHS and maximize financial planning, optimization of resources, and forecasting return-on-investment. The long-standing challenges of interoperability constraints, cybersecurity, infrastructure costs, and clinician adoption are discussed from an engineering perspective.