Building Intelligent Claims and Data Infrastructure with AI
DOI:
https://doi.org/10.5281/zenodo.22111252Keywords:
AI-Enabled Payer Intelligence, Healthcare Claims Processing, Automated Claims Management, Claims Adjudication Systems, Fraud Waste and Abuse Detection, Healthcare Data Pipelines, Enterprise Data Governance, Predictive Claims Analytics, NLP in Healthcare, Natural Language Understanding, Claims Lifecycle Automation, Human-in-the-Loop AI, Healthcare Cost Optimization, Intelligent Claims Routing, Data Integration in Healthcare, AI in Health Insurance, Operational Efficiency in Payers, Healthcare Analytics Systems, Decision Support in Claims, AI-Driven Healthcare Operations.Abstract
An abstract encapsulates the essence of a research work. This piece presents an overview of AI-Enabled Payer Intelligence in healthcare. Schmitt and Gamasai explore the processing of payer claims—an activity that defines each health insurance company in the United States—through an automated claims and data pipeline. A data integration strategy, stakeholder data-sourcing choice, supportive enterprise data governance, and the automation of end-to-end operations in a controlled manner allow for greater efficiency and lower costs. Automated management of fraud, waste, and abuse; natural language understanding of adjudication; and predictive modeling applied upstream, upstream, midpoint, and downstream in supplementing human judgment throughout the operations chain are pursued.
Automated processing of claims constitutes an essential component of the operation of any North American healthcare payer. Claims-management autoroutes; a natural-language-understanding model dedicated to adjudication; and prompt-creation, natural-language-generation, and predictive-modeling approaches applied throughout the claims lifecycle down a human-in-the-loop pathway support greater efficiency, lower costs, enhanced quality, and improved experiences.
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