FHIR Granular Sensitive Data Segmentation.

IF 2.1 2区医学 Q4 MEDICAL INFORMATICS

Applied Clinical Informatics Pub Date : 2025-01-01 Epub Date: 2025-02-19 DOI:10.1055/a-2466-4371

Preston Lee, Daniel Mendoza, Martha Kaiser, Eric Lott, Gagandeep Singh, Adela Grando

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引用次数: 0

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.

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来源期刊

Applied Clinical Informatics MEDICAL INFORMATICS-

CiteScore

4.60

自引率

24.10%

发文量

132

期刊介绍： ACI is the third Schattauer journal dealing with biomedical and health informatics. It perfectly complements our other journals Öffnet internen Link im aktuellen FensterMethods of Information in Medicine and the Öffnet internen Link im aktuellen FensterYearbook of Medical Informatics. The Yearbook of Medical Informatics being the “Milestone” or state-of-the-art journal and Methods of Information in Medicine being the “Science and Research” journal of IMIA, ACI intends to be the “Practical” journal of IMIA.