A Dialogue-based Information Extraction System for Medical Insurance Assessment

TL;DR

This paper presents a dialogue-based NLP system designed to streamline medical insurance assessments in China, significantly reducing time and resource costs during online claim processing.

Contribution

The paper introduces a novel dialogue-based information extraction system that enhances efficiency and accuracy in online medical insurance assessments, especially for junior assessors.

Findings

Reduced assessment time from 55 to 35 minutes

Saved 30% of human resources costs

Served thousands of online claim cases

Abstract

In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant. This is a highly professional job that involves many parts, such as identifying personal information, collecting related evidence, and making a final insurance report. Due to the coronavirus (COVID-19) pandemic, the previous offline insurance assessment has to be conducted online. However, for the junior assessor often lacking practical experience, it is not easy to quickly handle such a complex online procedure, yet this is important as the insurance company needs to decide how much compensation the claimant should receive based on the assessor's feedback. In order to promote assessors' work efficiency and speed up the overall procedure, in this paper, we propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. With the assistance of our system, the average time cost of the procedure is reduced from 55 minutes to 35 minutes, and the total human resources cost is saved 30% compared with the previous offline procedure. Until now, the system has already served thousands of online claim cases.