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Automation in healthcare claims processing: Enhancing efficiency and accuracy
Senior software Developer, Kforce INC, USA.
Research Article
International Journal of Science and Research Archive, 2023, 09(01), 825-834.
Article DOI: 10.30574/ijsra.2023.9.1.0435
Publication history:
Received on 16 April 2023; revised on 11 June 2023; accepted on 14 June 2023
Abstract:
Healthcare claims processing is a vital but complicated part of the medical industry, plagued by inefficiencies and manual errors, and drawn-out approval cycles. The use of Automation, powered by Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) is revolutionizing eh claims management paradigm through smarter, accurate and faster processes. This article focuses on using automation to ease healthcare claims automation, and reduce manual processing, potential for submitting fraudulent claims and adhering with industry standards. We focus on the core technologies that will facilitate this shift Natural Language Processing (NLP) for data extraction, predictive analytics for fraud detection and blockchain for secure transactions. Additionally, we assess the influence of automation on operational expenses, claims processing times, and patient satisfaction. This paper uses case studies and data-driven findings to demonstrate how automation not only significantly increases the efficiency of claims processing but can also help streamline regulatory compliance processes. We also identify some key challenges that must be addressed in order to realize this potential, including the risk of data privacy breaches, and integration difficulties that could hinder the effective implementation of this new technology. The results indicate that implementing automated healthcare claims processing is an essential evolution in redesigning the healthcare system.
Keywords:
Automation; Healthcare Claims Processing; Artificial Intelligence; Machine Learning; Robotic Process Automation; Fraud Detection; Blockchain; Efficiency; Accuracy
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Copyright information:
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
