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打击医疗保健欺诈和滥用:区块链反欺诈框架的概念化和原型研究。

Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework.

机构信息

UC San Diego - School of Medicine, Department of Anesthesiology and Division of Infectious Diseases and Global Public Health, La Jolla, CA, United States.

San Diego Supercomputer Center, BlockLAB, La Jolla, CA, United States.

出版信息

J Med Internet Res. 2020 Sep 10;22(9):e18623. doi: 10.2196/18623.

DOI:10.2196/18623
PMID:32909952
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7516680/
Abstract

BACKGROUND

An estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse.

OBJECTIVE

The aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse.

METHODS

We developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation).

RESULTS

Our technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network.

CONCLUSIONS

The proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.

摘要

背景

据估计,医疗保健欺诈和滥用造成了 26 亿美元的损失。在传统的医疗保健索赔验证和报销中,医疗服务提供者在向患者提供服务后提交索赔,然后由付款人进行验证和报销。然而,这个过程遗漏了一个关键的利益相关者:实际接受服务的患者。这种缺乏患者参与的情况会带来欺诈和滥用的风险。区块链技术可以实现安全的数据管理和透明度,从而降低医疗保健欺诈和滥用的风险。

目的

本研究旨在开发一个使用区块链以不可变格式记录索赔数据和交易的框架,并使患者能够充当验证节点,以帮助发现和预防医疗保健欺诈和滥用。

方法

我们使用关键的区块链工具和应用层(包括共识算法、智能合约、代币和基于以太坊平台上的数字身份的治理)开发了一个医疗保健欺诈和滥用区块链技术框架和原型。

结果

我们的技术框架与理赔裁决流程相对应,重点是医疗保险理赔,美国医疗保险和医疗补助服务中心(CMS)作为中央权力机构。使用区块链平台以太坊(以太坊基金会)开发了框架系统的原型,概述了其设计特点、工作流程、智能合约功能、系统架构和软件实现。用于构建系统的软件堆栈包括前端用户界面框架、后端处理服务器和区块链网络。React 用于用户界面框架,NodeJS 和 Express 服务器用于后端处理服务器;Solidity 是用于与本地以太坊区块链网络交互的智能合约语言。

结论

所提出的框架和初始原型有可能通过使用区块链技术进行安全的数据存储和共识机制来改善医疗保健理赔流程,从而使理赔裁决过程更加以患者为中心,以识别和预防医疗保健欺诈和滥用。未来的工作将集中在使用合成或历史 CMS 理赔数据来评估框架的实际可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/23463a6a8318/jmir_v22i9e18623_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/a387d2731d58/jmir_v22i9e18623_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/e7814d78a9ed/jmir_v22i9e18623_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/a15a27a8dfec/jmir_v22i9e18623_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/486a168fde07/jmir_v22i9e18623_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/23463a6a8318/jmir_v22i9e18623_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/a387d2731d58/jmir_v22i9e18623_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/e7814d78a9ed/jmir_v22i9e18623_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/a15a27a8dfec/jmir_v22i9e18623_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/486a168fde07/jmir_v22i9e18623_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7abe/7516680/23463a6a8318/jmir_v22i9e18623_fig5.jpg

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