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Automated Healthcare Claim Processing with AWS AI

Author(s) Goutham Bilakanti
Country India
Abstract The integration of artificial intelligence (AI) and cloud-based solutions is revolutionizing healthcare claims processing by enhancing efficiency, accuracy, and security. This study explores an AWS-powered system that leverages Amazon Textract, AWS Step Functions, and Amazon Recognition to automate document extraction, validation, and fraud detection. By eliminating manual processes, the AI-driven system significantly reduces claim processing time, minimizes errors, and ensures compliance with regulatory standards. Machine learning algorithms improve data classification and enhance decision-making, leading to faster reimbursements and reduced administrative burdens for healthcare payers and providers. The incorporation of AWS Step Functions optimizes workflow automation, while Amazon Rekognition strengthens fraud detection by identifying inconsistencies in submitted claims. The cloud-based architecture ensures scalability, reliability, and secure data management, facilitating seamless claim approvals. Furthermore, AI models continuously adapt to evolving fraud patterns, reinforcing system integrity and trust. This study highlights the impact of AI in streamlining claims processing, reducing costs, and enhancing operational efficiency in healthcare. The findings contribute to the ongoing transformation of healthcare administration through intelligent automation, positioning AI as a key enabler of future-ready healthcare systems.
Keywords AI-driven claims processing, AWS cloud solutions, Amazon Textract, fraud detection, healthcare automation, workflow optimization, machine learning in healthcare, digital transformation in insurance.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 5, Issue 1, January 2024
Published On 2024-01-05
Cite This Automated Healthcare Claim Processing with AWS AI - Goutham Bilakanti - IJLRP Volume 5, Issue 1, January 2024. DOI 10.5281/zenodo.15196969
DOI https://doi.org/10.5281/zenodo.15196969
Short DOI https://doi.org/g9d68g
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