Using NLP and AI to Automate Medical Coding and Insurance Claims on Cloud Systems
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V4I4P104Keywords:
Natural Language Processing, Medical Coding, Insurance Claims, Cloud Computing, Deep Learning, Electronic Health Records, ICD, Automation, CPT, Healthcare AIAbstract
Insurance claims processing and medical coding are both very important and, at the same time, consuming aspects of the healthcare administration system. As clinical data and insurance transactions increase daily, traditional manual code processes are becoming ineffective, inaccurate, and costly. The full framework developed in this paper incorporates the technology behind Natural Language Processing (NLP) and Artificial Intelligence (AI) functionality implemented on cloud-based systems to optimize and automate medical coding and insurance claims. The offered system is based on deep learning models trained on annotated Electronic Health Records (EHRs) to retrieve pertinent clinical data, encode it into standard medical code systems (ICD-10-CM, CPT-4), and submit claims to insurance companies through secure cloud connections. It has a modular architecture, making data privacy, regulatory compliance (e.g., HIPAA) and scalability a fact of life. As described in our research, we have various pipelines of NLP to recognize entities, context disambiguation, and mapping the code. Moreover, we also demonstrate how cloud infrastructure enables real-time claim validation, auditing, and feedback loops, thereby improving accuracy. The experimental results indicate that our system can cut down the time of claim processing by 70 percent, positively impact accuracy by 23 percent and cut the administration expenses extremely well. It is finished by discussing limitations, ethical issues and future work in the study
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