Identifying fraud in medical insurance based on blockchain and deep learning

With the rapid growth of medical costs, the control of medical expenses has been becoming an important task of Health Insurance Department. Traditional medical insurance settlement is paid on a per-service basis, which leads to lots of unreasonable expenses. To cope with this problem, the single-disease payment mechanism has been widely used in recent years. However, the single-disease payment also has a risk of fraud. In this work, we propose a framework to identify fraud of medical insurance based on consortium blockchain and deep learning, which can recognize suspicious medical records automatically to ensure valid implementation on single-disease payment and lighten the work of medical insurance auditors. An explainable model BERT-LE is designed to evaluate the reasonability of ICD disease code for Medicare reimbursement by predicting the probability of a disease according to the chief complaint of a patient. We also put forward a storage and management process of medical records based on consortium blockchain to ensure the security, immutability, traceability, and auditability of the data. The experiments on two real datasets from two 3A hospitals demonstrate that the proposed solution can identify fraud effectively and greatly improve the efficiency in medical insurance reviews. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

With the rapid growth of medical costs, controlling medical expenses has become an important task for the Health Insurance Department. Traditional per-service payment methods in medical insurance lead to many unreasonable expenses. To address this issue, the use of single-disease payment mechanisms has become popular, but it also carries the risk of fraud. This study proposes a framework based on consortium blockchain and deep learning to identify fraud in medical insurance, automating the recognition of suspicious medical records and ensuring valid implementation of single-disease payments, while reducing the workload of medical insurance auditors.