Enhancing Member Risk Profiling Using Data-Driven Architectures in Health Care

Abstract

This research looks at how using data-driven technology improves the identification of risks for medical members. The primary objective is to assess whether using electronic health records (EHRs), claims data, social determinants of health, and AI analytics improves the precision of identifying patient risks. The research aims to explain by using different case studies, along with selected secondary qualitative and quantitative data. According to the findings, data-driven systems make it easier to identify risks early, create customised treatment plans, improve patient outcomes and lower healthcare costs. Nonetheless, the challenges were listed as difficulties in data sharing, worries about privacy and ethical issues. Real-world use reveals that having protected and linked systems is vital for delivering active healthcare. It recommends supporting universal data standards, better privacy controls and involving different sectors to tackle operational threats. Because of these upgrades, healthcare can provide more anticipatory services that are both better for society and more efficient.