Early Adoption of Predictive Analytics for Preventive Care Opportunities Using Claims and Encounter Data
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I4P110Keywords:
Predictive Analytics, Preventive Care, Claims Data, Encounter Data, Chronic Disease, Healthcare AI, Risk Stratification, Population HealthAbstract
The integration of predictive analytics into health- care has emerged as a transformative tool in identifying preventive care opportunities. Leveraging claims and encounter data, this research explores early adoption strategies for predictive analytics models to proactively manage chronic conditions, reduce avoidable hospitalizations, and optimize care delivery. We present a scalable framework incorporating machine learning pipelines trained on structured insurance claims and encounter records from over 500,000 patients. The study emphasizes clinical relevance, cost-efficiency, and population health outcomes through model validation, risk stratification, and deployment case studies
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