AI-Optimized Symmetry Episode Analytics for Early Detection of High-Utilizers: A Claims-Based Predictive Modelling Framework Using Advanced Machine Learning Models
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I1P147Keywords:
Symmetry episode analytics, AI, ML, Cyber issues, Population health, Persistent threat, High utilizers, ETG(Episode Treatment Groups), ERG (Episode Risk Groups), PEG(procedure-based analytics groupsAbstract
The continuously increasing medical expenses are now considered as priority for national economy and organizational finance management for strategic emphasis on finances. The increasing concern about economic risks and compounded influence on population health needs to be managed suitably. Managing issues like increasing costs and staff limitations, along with cyber issues require insightful analysis. Perceptions regarding the continuously changing healthcare challenge is critical for supporting claim processing with compliance and effective care. Supporting patients with high-quality healthcare emphasizes on persistent threats. They require practical processes to support in managing associated complexities. Increasing the strength of healthcare claim management and risk handling requires using strategies to manage the challenges. This study involves development of a predictive modeling structure and framework, unifying symmetry episode analytics and ETG/ERG/PEG with supervised learning models. These models enhance the strength to manage compliance and address shrinking margins according to revenue generated. The paper discusses information regarding Symmetry episode analytics combination with intelligence (AI) and learning (ML), for effective therapy delivery and cost reduction. The outcomes involve demonstration of AI-streamlined episode analytics for identification of high-utilizers proactively and empower healthcare management for targeted interventions therapeutically.
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