De-identified, Anonymized FHIR Profiles Library
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De-identified, Anonymized FHIR Profiles Library, published by HL7 International / Cross-Group Projects. This guide is not an authorized publication; it is the continuous build for version 1.0.0-ballot built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/fhir-dapl/ and changes regularly. See the Directory of published versions

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DAPL Home Page

Official URL: http://hl7.org/fhir/us/dapl/ImplementationGuide/hl7.fhir.us.dapl Version: 1.0.0-ballot
IG Standards status: Trial-use Maturity Level: 2 Computable Name: DAPL

Contents:

Introduction

The health care industry has embraced FHIR as the standard for data exchange and has been implementing FHIR in the real-world as part of the various accelerators such as Argonaut, DaVinci, and Helios. The adoption of FHIR has been further expedited by the Assistant Secretary for Technology Policy (ASTP) and Centers for Medicare and Medicaid Services (CMS) regulations that require the implementation of FHIR for multiple use cases. One of the competing requirements that is emerging in the industry is the need for data that does not contain protected health information (PHI) or personally identifiable information (PII). These requirements are common among federal reporting use cases such as Health Resources and Services Administration (HRSA) reporting, public health reporting to Centers for Disease Control and Prevention (CDC), data needs for training artificial intelligence (AI) models, and data needs for research programs.

This Implementation Guide (IG) defines the data structures (profiles) which federal agencies such as HRSA, SAMHSA, and others can use to receive line-level de-identified and/or anonymized information. The IG defines profiles to represent and exchange

  • De-identified Patient data using FHIR Resources
  • Anonymized Patient data using FHIR Resources

The main sections of this IG are:

  • Background - The page provides an introduction and definitions for de-identification, pseudonymization and anonymization.
  • Use Cases - Defines the use case, workflows, actors and systems that are used as part of the IG.
  • FHIR Artifacts - Defines the FHIR artifacts for the IG.
  • Downloads - Allows you to download a copy of this implementation guide and other useful information

Relationship to other Implementation Guides

This section elaborates on the relationship of this IG to other implementation guides

Relationship to US Core Implementation Guide

This implementation guide, leverages terminology from US Core. The profiles are not derived from US Core because they eliminate many of the data elements that are mandatory in US Core profiles. Therefore this IG is aligned with US core in terms of terminology only.

Relationship to De-identification, Anonymization and Redaction Toolkit Services (DARTS) Implementation Guide

This implementation guide complements the the DARTS IG by defining the data structures (profiles) used to represent de-identified and anonymized data. DARTS services consume identifiable information and produce de-identified or anonymized information and use the DAPL profiles to represent the output produced by each DARTS service.

Relationship to Data Segmentation for Privacy (DS4P) Implmentation Guide

The DS4P IG specifies the tags,labels and obligations to be used to protect patient privacy of patients' data. The DAPL IG on the other hand aims to remove PHI/PII from the data structures. Implementers of this IG can leverage any existing DS4P mechanisms if needed as part of the DAPL profiles and data exchanges containing DAPL profiles.