Risk Stratification Through Predictive Analytics
DOI:
https://doi.org/10.5281/zenodo.21509774Keywords:
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
Predictive analytics seeks to identify future risks and opportunities for individuals and groups. In a population health risk stratification context, addressing the higher level of risk associated with a subpopulation creates the potential for improved outcomes and reduced costs. The proposed study aims to improve predictive analytics for hospital admission, readmission, and emergency department (ED) visit risk stratification in an insurance network using advanced methodologies. Primary data sources from claims and provider networks provide the foundation for prediction and the design of use cases to support the predictive engine. Sensitivity analyses assess predictive equity along social determinant lines to ensure fairness. Model performance is measured in terms of the receiver operating characteristic, precision-recall, F1 score, area under the precision-recall curve, and Matthews correlation coefficient. Results could influence resource allocation, care management priorities, contracting, and network design.
Population health risk stratification identifies clusters of individuals with shared health risk characteristics for prioritizing health care actions and interventions. Care management interventions are generally devoted to individuals in specific need categories—those at risk of hospitalization for specific acute or chronic conditions, recently discharged from the hospital, facing social or economic barriers to achieving good health, receiving end-of-life support, or dealing with multiple medical conditions—and the overall levels and distribution of illness burden and acuity can inform the investment and allocation of resources across these care pathways. Predictive analytics seeks to identify future risks and opportunities for individuals and groups. In a population health context, addressing the higher level of risk associated with a subpopulation creates the potential for improved outcomes and reduced costs.
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