Embedding NLP into Member Portals to Improve Plan Selection and CHIP Re-enrollment
Keywords:
Natural Language Processing (NLP), CHIP re-enrollment, health insurance plan selectionAbstract
For many families, choosing a suitable health insurance plan or handling CHIP (Children's Health Insurance Program) re-enrollment is a more significant obstacle mostly related to the complex nature of healthcare options & the technical vocabulary utilized. This paper investigates the use of Natural Language Processing (NLP) into member portals to solve these issues by simplifying the plan choosing process & also helping users with more precisely & more effectively re-enrollment. We include more complex language capabilities into the user interface using domain-specific transformer models from HuggingFace, therefore enabling contextual understanding and more customized help depending on specific user inquiry. These models are painstakingly improved using healthcare-specific information to ensure relevance and more accuracy in conversations on plan comparison. Since the system understands common language questions and offers succinct, tailored plan explanations, our implementation indicates that users have less cognitive load & more confidence in choosing coverage. Furthermore, the method helps to identify their eligibility and enrollment deadlines, thus promoting quick re-registration & so closing coverage gaps. By thus stressing state-specific legislation and paperwork requirements, the integration enhances policy compliance. Re-enrollment rates, user satisfaction & also decision accuracy show a significant increase after trial deployments. Including NLP into the digital healthcare experience modernizes the member journey & improves their healthcare accessibility goals, therefore allowing more people and families to get and maintain appropriate coverage with least possible barriers.
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References
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