AI-Driven Cloud-Based System for Automated Data Extraction from Healthcare Claim Forms

This article presents an AI-driven cloud-based system for automating data extraction from healthcare claim forms. The solution integrates Optical Character Recognition (OCR), Natural Language Processing (NLP), and deep learning techniques to transform unstructured and semi-structured claim documents into validated structured data. Built on a modular architecture comprising document acquisition, pre-processing, AI-powered extraction, validation, and integration components, the system operates within a secure cloud infrastructure that ensures scalability and regulatory compliance. Evaluations demonstrate significant improvements in extraction accuracy across various document types, with particular effectiveness for typed and digital forms. The architecture substantially reduces processing time compared to manual methods while maintaining consistent performance during peak workloads. The technology addresses critical healthcare administrative challenges, enabling healthcare organizations to reduce operational costs, accelerate reimbursement cycles, and redirect resources toward patient-centered activities. The implementation demonstrates that AI automation in healthcare claims processing delivers tangible benefits in accuracy, efficiency, and cost reduction.

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Publication Details

Journal
Journal of Information Systems Engineering & Management
Published
2025-07-19
DOI
https://doi.org/10.52783/jisem.v10i57s.12535
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Driven Cloud-Based System for Automated Data Extraction from Healthcare Claim Forms

Soham Chakraborti
Journal of Information Systems Engineering & Management
Impact of AI and Big Data on Business and Society
article

AI-Driven Cloud-Based System for Automated Data Extraction from Healthcare Claim Forms

Soham Chakraborti
article en

Abstract

This article presents an AI-driven cloud-based system for automating data extraction from healthcare claim forms. The solution integrates Optical Character Recognition (OCR), Natural Language Processing (NLP), and deep learning techniques to transform unstructured and semi-structured claim documents into validated structured data. Built on a modular architecture comprising document acquisition, pre-processing, AI-powered extraction, validation, and integration components, the system operates within a secure cloud infrastructure that ensures scalability and regulatory compliance. Evaluations demonstrate significant improvements in extraction accuracy across various document types, with particular effectiveness for typed and digital forms. The architecture substantially reduces processing time compared to manual methods while maintaining consistent performance during peak workloads. The technology addresses critical healthcare administrative challenges, enabling healthcare organizations to reduce operational costs, accelerate reimbursement cycles, and redirect resources toward patient-centered activities. The implementation demonstrates that AI automation in healthcare claims processing delivers tangible benefits in accuracy, efficiency, and cost reduction.

Journal of Information Systems Engineering & ManagementVol. 10(57s)
Carnegie Mellon University (US)
Openalex Percentile: Top 75%
Impact of AI and Big Data on Business and Society
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AI-Driven Cloud-Based System for Automated Data Extraction from Healthcare Claim Forms — Soham Chakraborti · Journal of Information Systems Engineering & Management (2025) | TGRS Research Map | TGRS