Predictive Risk Stratification in Health Insurance
Keywords:
: Population Health Analytics, Risk Stratification Models, Predictive Healthcare Analytics, Hospital Readmission Prediction, Emergency Visit Prediction, Claims Data Analytics, Provider Network Analytics, Health Risk Modeling, Care Management Optimization, Social Determinants of Health, Predictive Equity Analysis, Healthcare Resource Allocation, Clinical Risk Prediction, Outcome Optimization Models, Precision-Recall Metrics, ROC Analysis, F1 Score Evaluation, Healthcare Data Science, Insurance Network Analytics, Data-Driven Care Management.Abstract
Population health risk stratification identifies clusters of individuals who share common health risk characteristics, enabling health systems to prioritize care actions and target interventions where they are most needed. Care management efforts are typically directed at individuals in specific need categories — those at risk of hospitalization for acute or chronic conditions, recently discharged patients, individuals facing social or economic barriers to health, those receiving end-of-life support, or patients managing multiple comorbidities. Understanding the overall distribution and burden of illness across these categories can guide the allocation of resources across care pathways.
Predictive analytics extends this effort by identifying future risks and opportunities at both the individual and group level. Within a population health risk stratification framework, accurately identifying subpopulations at elevated risk creates the potential for improved outcomes and reduced costs. This study proposes an approach to improve predictive analytics for hospital admission, readmission, and emergency department (ED) visit risk stratification within an insurance network, using advanced modeling methodologies. Primary data sources — including claims and provider network data — form the foundation for prediction and inform the design of use cases supporting the predictive engine. Sensitivity analyses evaluate predictive equity across social determinant lines to ensure fairness in model outcomes. Model performance is assessed using receiver operating characteristic (ROC) analysis, precision-recall, F1 score, area under the precision-recall curve (AUPRC), and Matthews correlation coefficient (MCC). The resulting insights are intended to inform resource allocation, care management prioritization, contracting decisions, and network design.
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