Key result
Integrating blockchain and knowledge graphs successfully detects substance use disorder data in synthetic patient encounters.
Why the study?
Proactively identifying HIPAA Part 2 compliance issues is challenging when exchanging sensitive health records, creating a need to audit substance use disorder data shared without patient consent.
A novel blockchain and knowledge graph-based methodology can automate the detection and secure logging of unauthorized sharing of sensitive substance use disorder data (HIPAA Part 2 compliance).
Should not yet change clinical data practices; leaves open real-world validation of blockchain-knowledge graph detection in live cohorts.
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.
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Clavin et al. (2023) studied Substance use disorder data compliance (n=1,000). Blockchain integrated with knowledge graphs was evaluated on Detection of Part 2 data exchanged between organizations. A novel methodology integrating blockchain technologies with knowledge graphs successfully detected substance use disorder data in synthetic patient encounters for cohorts of 1,000 patients.
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