Health Insurance Document Simplifier Chatbot: A Transformer-Based Framework for Patient-Friendly Medical Text Understanding

Abstract

Most of the documents, such as agreement papers, claims, benefit descriptions, exemption clauses, etc., associated with healthcare insurance policies are written in a complicated manner with respect to technical jargon that is not understandable by the ordinary public. Owing to such difficulties, misinterpretation of the contract terms is possible, leading to claims denials, discrepancies in finance, loss of trust between the insurance firms and respective customers. The insurance firms do have solutions for customer service, but those services are not effective, customized, and correct for lengthy files uploaded by the customers themselves. This paper creates a Health Insurance Document Simplifier Chatbot using AI with transformer language models and domain-specific knowledge grounding for the purpose of a correct interpretation of health insurance documents in an understandable way. The chatbot will use a transformer Seq2Seq architecture combined with RAG for reducing hallucinations in the generated output while preserving factual correctness. Insurance, coupled with health terminology, will be grounded with structured knowledge sources in order to retain factual correctness from the chatbot. It also has the capacity to support end-to-end processing for query explanations to enable the user to raise additional queries pertaining to deductibles, benefits, exemptions, and claims. The outcome achieved from the experiment reflects an optimal improvement in terms of clarity, relevance, and satisfaction levels pertaining to overall processing of natural language queries, including the usual processing method for natural language queries. A system has been discussed in this work regarding the provision of an easy medium for documents such as healthcare insurance to become friend-like documents using a designed system intended for the chatbot.

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