Predictive Healthcare Administration Using Advanced Payer Analytics and Population Health Data Engineering
DOI:
https://doi.org/10.15662/IJARCST.2023.0602007Keywords:
Predictive Healthcare Administration, Population Health Analytics, Advanced Payer Intelligence, Healthcare Data Engineering Platforms, Risk-Stratified Population Modeling, Predictive Care Cost Optimization, Integrated Healthcare Data Ecosystems, AI-Driven Payer Analytics, High-Risk Patient Forecasting, Population-Centered Predictive ModelsAbstract
Healthcare systems around the world are struggling to control the costs of healthcare expenditures while maintaining or improving health outcomes for their constituents. Advanced analytics is essential to ascertain upcoming high-risk, high-cost populations. The incorporation of predictive information into payer management decisions presents exciting possibilities for improved budget allocation and patient care outcomes, yet the experimentation and adoption of these emerging techniques in a payer context remain limited.
Research gaps identified through a review of academic literature provide the foundation for the subsequent development and demonstration of a comprehensive set of predictive analytics for a fictional payer, MedicarePlus. Data sources are integrated and engineered within a dedicated population health data engineering platform and subsequently exploited via predictive healthcare administration techniques focusing on advanced payer analytics and population health-centered predictive data models. Results demonstrate significant potential for improved mission fulfillment through multiple predictive models, paving the way for expanded predictive capabilities and ensuing value delivery to the citizens of New Hampshire.
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