Policy Integrated Blockchain to Automate HIPAA Part 2 Compliance

James R. Clavin, K. Joshi
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Abstract

Healthcare organizations exchange sensitive health records, including behavioral health data, across peer-to-peer networks, and it is challenging to find and fix compliance issues proactively. The Healthcare industry anticipates a growing need to audit substance use disorder patient data, commonly referred to as Part 2 data, having been shared without a release of information signed by the patient. To address this need, we developed and evaluated a novel methodology to detect Part 2 data exchanged between organizations that integrates Blockchain technologies with knowledge graphs. We detect substance use disorder data in patient encounters exchanged using clinical terminology based upon the value sets provided by the National Institutes of Health for the Substance Abuse and Mental Health Services Administration. Generally, we consider sharing Part 2 data without consent as Byzantine medical faults, as they represent data shared between known and trusted network participants, that is valid, but is not relevant, and sharing it causes a breach. In this paper, we present our methodology in detail along with the experiment results. We model a medical network of hospitals based upon the most recent healthcare legislation, TEFCA, and generate synthetic patient encounter data dynamically in HL7 format. We convert exchanged encounter data into a knowledge graph data model so that we can use SNOMED-CT for identifying Part 2 data. For cohorts of 1,000 patients, we detect Part 2 data in a subset of their encounter data shared between organizations and log that securely on an Ethereum-based blockchain.
政策集成区块链自动化HIPAA第2部分合规性
医疗保健组织跨点对点网络交换敏感的健康记录(包括行为健康数据),主动发现和修复遵从性问题具有挑战性。医疗保健行业预计,对物质使用障碍患者数据(通常称为第2部分数据)的审计需求将不断增长,这些数据是在没有患者签名的情况下共享的。为了满足这一需求,我们开发并评估了一种新的方法来检测将区块链技术与知识图集成在一起的组织之间交换的第2部分数据。我们根据美国国立卫生研究院为药物滥用和精神卫生服务管理局提供的值集,在使用临床术语交换的患者接触中检测物质使用障碍数据。一般来说,我们将未经同意共享第2部分数据视为拜占庭医疗故障,因为它们代表已知和可信网络参与者之间共享的数据,这是有效的,但不相关,并且共享它会导致违规。在本文中,我们详细介绍了我们的方法以及实验结果。我们基于最新的医疗保健立法TEFCA对医院医疗网络进行建模,并以HL7格式动态生成合成的患者遭遇数据。我们将交换的遭遇数据转换为知识图数据模型,以便我们可以使用somed - ct来标识第2部分数据。对于1000名患者的队列,我们在组织之间共享的遭遇数据的子集中检测第2部分数据,并将其安全地记录在基于以太坊的区块链上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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