Revolutionizing Medical Bill Reviews with AI: Enhancing Claims Processing Accuracy and Efficiency
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P113Keywords:
Artificial Intelligence, Medical Billing, Claims Processing, NLP, Fraud Detection, OCR, Machine Learning, Healthcare AutomationAbstract
Medical billing is an important part of the healthcare reimbursement ecosystem, although it continues to be plagued by inefficiencies, errors, and fraud. The conventional methods of claim processing in the medical profession are that the verifications, cross-checks, and adjudication are carried manually, which consumes time and is prone to inconsistencies. The innovative solution provided by Artificial Intelligence (AI) is the automation of reviews accompanied by the use of Natural Language Processing (NLP) and Machine Learning (ML). In this paper, we will discuss what AI would bring to revolutionizing the medical bill reviews, in terms of accuracy, elimination of human errors, identification of outliers and fraud, and general speed increase in the process. We explore how AI can be applied through Optical Character Recognition (OCR) and deep learning models to automate claims processing and even predictive analytics. In an extensive literature review, we examine past interventions and their limitations. We then suggest a sound methodology that integrates both unsupervised and supervised learning in detecting anomalies of claims using rules based expert system in real-time decision. The results of the experiments indicate considerable increases in accuracy, a cost reduction, and increased speed. The paper concludes that AI-based technologies are not only applicable but also necessary for transforming the management of healthcare bills and claim verifications
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