AI transformation in healthcare claims processing: Technical overview

The healthcare claims processing domain is experiencing a significant transformation through the integration of artificial intelligence technologies. This transformation encompasses several key components: machine learning frameworks for predictive adjudication, natural language processing implementations for document analysis, robotic process automation architectures for workflow optimization, and sophisticated fraud detection systems. These technologies collectively enhance accuracy, accelerate processing times, reduce fraudulent activities, and ensure compliance with evolving regulatory frameworks. While offering substantial benefits, AI implementation presents challenges, including data standardization across disparate sources, computational infrastructure requirements, model explainability concerns, system interoperability issues, continuous model retraining needs, and privacy considerations for sensitive patient data. The integration of these technologies represents a paradigm shift in claims processing, establishing new standards for operational efficiency while navigating complex implementation barriers.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17760420
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

AI transformation in healthcare claims processing: Technical overview

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

AI transformation in healthcare claims processing: Technical overview

article en

Abstract

The healthcare claims processing domain is experiencing a significant transformation through the integration of artificial intelligence technologies. This transformation encompasses several key components: machine learning frameworks for predictive adjudication, natural language processing implementations for document analysis, robotic process automation architectures for workflow optimization, and sophisticated fraud detection systems. These technologies collectively enhance accuracy, accelerate processing times, reduce fraudulent activities, and ensure compliance with evolving regulatory frameworks. While offering substantial benefits, AI implementation presents challenges, including data standardization across disparate sources, computational infrastructure requirements, model explainability concerns, system interoperability issues, continuous model retraining needs, and privacy considerations for sensitive patient data. The integration of these technologies represents a paradigm shift in claims processing, establishing new standards for operational efficiency while navigating complex implementation barriers.

Zenodo (CERN European Organization for Nuclear Research)
Jain University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 48%
Artificial Intelligence in Healthcare and Education
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