IDEA4RC FHIR Implementation Guide
0.1.0 - CI Build 150

IDEA4RC FHIR Implementation Guide, published by IDEA4RC Project. This guide is not an authorized publication; it is the continuous build for version 0.1.0 built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/hl7-eu/idea4rc/ and changes regularly. See the Directory of published versions

IDEA4RC FHIR IG - Home Page

Official URL: http://hl7.eu/fhir/ig/idea4rc/ImplementationGuide/hl7.eu.fhir.idea4rc Version: 0.1.0
Draft as of 2024-07-11 Computable Name: IDEA4RCImplementationGuide

Scope

Specify HL7 FHIR logical models and profiles to be used within the European project IDEA4RC. This project studies the implementation of an intelligent ecosystem to improve the governance, the sharing, and the re-use of health data for rare cancers.

The IDEA4RC project

Overview

IDEA4RC is a EU funded Research and Innovation action aiming to study the scale-up and implementation of an ecosystem for the re-use of health data related to that portion of population affected by rare cancers.

Building on the principles of the European Data strategy, the Rare Cancer Data Ecosystem concept proposed by IDEA4RC will unite available data sources and equip them with strong data governance controls to lay down the basis for an embryonic data spece for rare cancers. This because despite their relevance, rare cancers in general get a few scientific consideration and financial support. Carrying out clinical studies is difficult, because of the small number of sample populations. Therefore, clinical evidence is more difficult to build, clinical management is more complex, and shortage of accesible cancer registries and data is a fundamental obstacle. These can be overcome only through large collaborations exploiting networks specializing in rare cancers, that pool knowledge and data together.

Objectives

The overall aim of IDEA4RC is to leverage the digital transformation of healthcare to improve people-centred survivorship care by scaling up and implementing an intelligent ecosystem for improving the sharing of health data for rare cancers.

Preparation for Implementation
  1. exploit the potential of EURACAN and other existing ERNs for RC data sharing across Europe, involving stakeholders for rare cancer data economy scenarios co-creation and valuation supported by sound data governance policies and a resilient and privacy preserving IT architecture.

  2. extend HL7 FHIR concepts to a distributed data sharing enviroment and realize a set of AI-assisted tools ensuring interoperability and access to structured data from different sources, as a contribution towards the implementation of a European Health Data Space initiative.

  3. establish a federated Virtual Repository Layer, deployed on a secure distributed data space for FAIR data management and sharing, and to facilitate distributed data search and inform on data quality.

Implementation and evaluation
  1. leverage on novel AI approaches for achieving multi-lingual NPL to allow data extraction and standardization of unstructured data from different European centers, and data interpretation and integration with structured datasets.

  2. realize an intelligent data navigator exploiting multimodality, virtual assistance, and smart visualization technologies to support users in finding data and in fast and accurate assessment of data quality, as well as trust acceptability.

European Future Implementation & Achieving Impact
  1. implement a data governance framework for distributed data sharing, facilitating the negotiation of relevant data sharing agreements, the collection of patient consent, the enforcement of accountability, through the application of certification and trust-building methods based on blockchain technology.

  2. implement the realized ecosystem in pilot EURACAN sites, to assess user experience and value creation on the basis of real-world clinical research and public health questions, in different data use and reuse cases conducted on top of a “first-in-the-field” technical deployment of the IDEA4RC Data Ecosystem.

  3. investigate and assess translation potential of pilot experiences toward wider stakeholders’ audiences, beyond the centers involved in the project consortium, and creating a sustainable “Community of interest” around a continously expanding Data Ecosystem.

  4. build robust knowledge and information sharing channels to disseminate scientific and technology results, prepare business planning for exploitable components, inform rare cancer patients and health care providers of the opportunities created by the project, and inform the general public of the effort deployed by IDEA4RC, addressing the urgent needs of rare cancer patients, caregivers and families, with the key support received from the European Commission.

Dependencies

IGPackageFHIRComment
.. IDEA4RC FHIR Implementation Guidehl7.eu.fhir.idea4rc#0.1.0R4
... HL7 Terminology (THO)hl7.terminology.r4#5.5.0R4Automatically added as a dependency - all IGs depend on HL7 Terminology
... FHIR Extensions Packhl7.fhir.uv.extensions.r4#5.1.0R4Automatically added as a dependency - all IGs depend on the HL7 Extension Pack
... minimal Common Oncology Data Elements (mCODE) Implementation Guidehl7.fhir.us.mcode#3.0.0R4
.... HL7 Terminology (THO)hl7.terminology.r4#5.3.0R4
.... US Core Implementation Guidehl7.fhir.us.core#5.0.1R4
..... HL7 Terminology (THO)hl7.terminology.r4#3.1.0R4
..... Bulk Data Access IGhl7.fhir.uv.bulkdata#2.0.0R4
..... SMART App Launchhl7.fhir.uv.smart-app-launch#2.0.0R4
..... us.nlm.vsac#0.7.0R4
..... Structured Data Capturehl7.fhir.uv.sdc#3.0.0R4
...... FHIR R4 package : Exampleshl7.fhir.r4.examples#4.0.1R4
.... Genomics Reporting Implementation Guidehl7.fhir.uv.genomics-reporting#2.0.0R4
.... FHIR Extensions Packhl7.fhir.uv.extensions#currentR5
..... HL7 Terminology (THO)hl7.terminology.r5#5.5.0R5
... International Patient Summary Implementation Guidehl7.fhir.uv.ips#1.1.0R4
.... HL7 Terminology (THO)hl7.terminology.r4#5.0.0R4
.... fhir.dicom#2022.4.20221006R4

Package hl7.fhir.uv.extensions.r4#5.1.0

This IG defines the global extensions - the ones defined for everyone. These extensions are always in scope wherever FHIR is being used (built Sat, Apr 27, 2024 18:39+1000+10:00)

Package hl7.fhir.uv.bulkdata#2.0.0

FHIR based approach for exporting large data sets from a FHIR server to a client application (built Fri, Nov 26, 2021 05:56+1100+11:00)

Package hl7.fhir.r4.examples#4.0.1

Example resources in the R4 version of the FHIR standard

Package hl7.fhir.uv.sdc#3.0.0

The SDC specification provides an infrastructure to standardize the capture and expanded use of patient-level data collected within an EHR.
This includes two components:
* Support more sophisticated questionnaire/form use-cases such as those needed for research, oncology, pathology and other clinical domains.
*Support pre-population and auto-population of EHR data into forms/questionnaires for uses outside direct clinical care (patient safety, adverse event reporting, public health reporting, etc.). (built Tue, Mar 8, 2022 18:32+0000+00:00)

Package hl7.fhir.us.core#5.0.1

The US Core Implementation Guide is based on FHIR Version R4 and defines the minimum conformance requirements for accessing patient data. The Argonaut pilot implementations, ONC 2015 Edition Common Clinical Data Set (CCDS), and ONC U.S. Core Data for Interoperability (USCDI) v1 provided the requirements for this guide. The prior Argonaut search and vocabulary requirements, based on FHIR DSTU2, are updated in this guide to support FHIR Version R4. This guide was used as the basis for further testing and guidance by the Argonaut Project Team to provide additional content and guidance specific to Data Query Access for purpose of ONC Certification testing. These profiles are the foundation for future US Realm FHIR implementation guides. In addition to Argonaut, they are used by DAF-Research, QI-Core, and CIMI. Under the guidance of HL7 and the HL7 US Realm Steering Committee, the content will expand in future versions to meet the needs specific to the US Realm. These requirements were originally developed, balloted, and published in FHIR DSTU2 as part of the Office of the National Coordinator for Health Information Technology (ONC) sponsored Data Access Framework (DAF) project. For more information on how DAF became US Core see the US Core change notes. (built Wed, Jun 22, 2022 19:44+0000+00:00)

Package hl7.fhir.uv.genomics-reporting#2.0.0

Guidelines for reporting of clinical genomics results using HL7 FHIR. (built Mon, May 9, 2022 16:52+0000+00:00)

Package hl7.fhir.uv.extensions#current

This IG defines the global extensions - the ones defined for everyone. These extensions are always in scope wherever FHIR is being used (built Sun, Apr 21, 2024 19:45+0000+00:00)

Package hl7.fhir.us.mcode#3.0.0

mCODE™ (short for Minimal Common Oncology Data Elements) is an initiative intended to assemble a core set of structured data elements for oncology electronic health records. (built Wed, Oct 25, 2023 23:16+0000+00:00)

Package hl7.fhir.uv.ips#1.1.0

International Patient Summary (IPS) FHIR Implementation Guide (built Tue, Nov 22, 2022 03:24+0000+00:00)

Cross Version Analysis

This is an R4 IG. None of the features it uses are changed in R4B, so it can be used as is with R4B systems. Packages for both R4 (hl7.eu.fhir.idea4rc.r4) and R4B (hl7.eu.fhir.idea4rc.r4b) are available.

Global Profiles

There are no Global profiles defined

Authors and Contributors

Roles Name Organization Contact
Autore Giorgio Cangioli HL7 Europe giorgio.cangioli_at_gmail.com
Contributor
Contributor