FHIR Granular Sensitive Data Segmentation

Abstract

Background Due to fear of stigma, patients want more control over the sharing of sensitive medical records. The Substance Abuse and Mental Health Administration (SAMHSA) and the Office of the National Coordinator (ONC) supported the development of standards-compliant, consent-respecting medical record exchange technology using metadata labeling (e.g., substance use information). Existing technologies must be updated with newer standards and support more than binary-sensitive categorizations to better align with how physicians categorize sensitive medical records.

Objectives Our goal was to deploy, pilot test, and share open-source Fast Healthcare Interoperability Resources (FHIR)-based data segmentation technologies. We pilot-tested the technologies using real-world patient electronic health record data in the context of substance use information. We involved physicians in designing a novel decision engine that supports various confidence levels.

Results We deployed a web-based Patient Portal and Clinical Decision Support (CDS) granular data segmentation Engine to allow patients to make consent-based granular data choices (e.g., not sharing substance use medical records). Compared with previous solutions, the Engine innovates by using the latest Health Level 7 (HL7) standards to support data sensitivity labeling and redaction: FHIR R5 and its Consent resource type and CDS Hooks. It also supports configurable floating point confidence threshold cutoffs as opposed to binary medical record categorizations. Multiple engineering choices were made to simplify software development and maintenance and to improve technology adaptability, reusability, and scalability.

Conclusion The resulting data segmentation technologies update SAMHSA and ONC software with the newest HL7 standards and better mimic how physicians categorize sensitive medical information with various confidence levels. To support reusability, we shared the resulting open-source code through the HL7 FHIR Foundry.

Keywords

standards adoption - testing and evaluation - knowledge management - confidentiality - electronic health records and systems - Fast Healthcare Interoperability Resources

Protection of Human and Animal Subjects

We obtained approval (approval no.: 00006227) from the Arizona State University Institutional Review Board to consent patients to share their de-identified medical records.

Publication History

Received: 11 August 2024

Accepted: 11 November 2024

Article published online:
19 February 2025

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