Approaches to Reducing the Burden on High-Cost Healthcare System Segments through Predictive Analytics
TL;DR
The paper argues that the high-cost burden declines when payers and providers use claims, electronic health record (EHR), registry, facility, and quality-measure data to identify high-risk patients, select safer alternatives to costly settings, and monitor outcomes after intervention.
Abstract
Predictive analytics can reduce pressure on high-cost healthcare segments when care teams connect risk estimates with concrete intervention pathways. The paper analyzes prospective risk stratification, skilled nursing facility transition management, readmission surveillance, and automated quality reporting as linked mechanisms of cost control. The aim is to define approaches for moving from retrospective expenditure review to prospective care-routing decisions. The materials include nine peer-reviewed studies and one official CMS measurement report published during the last five years. Comparative source analysis, conceptual synthesis, typologization, and analytical generalization guide the review. The paper argues that the high-cost burden declines when payers and providers use claims, electronic health record (EHR), registry, facility, and quality-measure data to identify high-risk patients, select safer alternatives to costly settings, and monitor outcomes after intervention. The proposed logic suits payer, provider, and value-based care environments where skilled nursing facility (SNF) placement, readmission exposure, and reporting workload shape financial risk.