Author: K. Donsa, K. Kreiner, D. Hayn, A. Rzepka, S. Ovejero, M. Topolnik, A. Ziegl, B. Pfeifer, S. Neururer, S. Kaltenbrunner, E. Klager, K. Zatloukal, B. Zatloukal, T. Schabetsberger, M. L. Garcia, N. Tanjga, G. Schreier

DOI: 10.3233/SHTI240351

Publication Date: August 2024

Abstract:
Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensiveanalysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patient-driven donations of health data for clinical research. Leveraging the groundwork, laid by the national Electronic Health Record implementation in Austria (called ELGA), Smart FOX aims to harness structured datasets from ELGA for research purposes through an opt-in approach. With funding secured from the Austrian Research Promotion Agency, the project embarks on innovative solutions encompassing governance frameworks, community engagement, and technical infrastructure. The Smart FOX consortium, comprising key stakeholders across various healthcare-associated domains, will evaluate these efforts through demonstrators focusing on clinical registries, patient-generated data, and recruitment services. The project targets to accompany the development of future data donation infrastructure while ultimately advancing clinical research efficiency and bolstering Austria’s preparedness for the European Health Data Space.This paper presents the first systematic evaluation of the technical concept and proposal for the federated system architecture of the Austrian Health Data Donation Space, which is the socio-technical goal of Smart FOX.

Author: K. Donsa, P. Mangesius, A. Lauschensky, M. Baumgartner, N. Tanjga, S. Beyer, G, Schreier, K. Kreiner

DOI: 10.3233/SHTI250187

Publication Date: April 2025

Abstract:
Efficient secondary use of real-world data (RWD) is a cornerstone for advancing data-driven medical research and personalised healthcare. However, significant challenges persist, including data fragmentation in silos, the lack of record linkage, and legal constraints that often hinder data utilisation. Especially Electronic Health Records (EHRs) represent a valuable data source, yet their potential remains largely untapped due to these barriers. Especially modern data space solutions promise to address these challenges, focusing on standardisation and harmonisation efforts, data governance aspects, as well as federated data-sharing approaches. A significant push in this area represents the European Health Data Space (EHDS) Act, focusing on an opt-out based approach for secondary use of health data. An additional consent-based approach (opt-in) represents data donation, which empowers individuals to contribute their data to research while maintaining trust and privacy under the current legal situation. The flagship project Smart FOX lays the foundations for making citizen-based data donations of EHR-standardised information usable in clinical research in Austria. As part of the architecture of the Austrian Health Data Donation Space (AHDDS), data donation boxes - Federated Open data eXchange Boxes (FOX BOXes) - present the fundamental decentralised building blocks for sharing EHR-standardised data. This paper outlines the architecture, functionality, and governance of FOX BOXes, highlighting its role in overcoming key barriers to health data sharing and its potential to accelerate data-driven research.

Author: S. Ovejero, C. Bezzi, S. huber, J. Harrer, A. Lauschensky, K. Donsa

DOI: 10.3233/SHTI250195

Publication Date: April 2025

Abstract:
The secondary use of healthcare data can play a crucial role in enhancing health care systems, patient care, and clinical research; however, it is challenged by privacy, governance and regulatory challenges. The aim of the Smart FOX project is to address these challenges to facilitate citizen-driven donation of ELGA (Austria Electronic Health Records) - standardized. The project consortium comprises stakeholders across several healthcare-related areas developing concepts and architectures for patient/citizen driven data-donation. This paper specifically introduces two distinct services for recruiting patients and citizens to enable secondary data use within the project. Service A, a researcher-focused service integrated in the hospital information system, within the Smart FOX project focuses on two primary use cases: enabling the study of new trial cohorts for data consumers (e.g., CROs), and contacting data donors (e.g., citizens) for trial recruitment. Service B, a web-based patient/citizen focused service, addresses three key use cases: identifying recruitment potential, facilitating contact with the recruitment pool, and presenting data donation opportunities via digital advertising and on its internet platform. By leveraging innovative digital platforms and federated data approaches, the Smart FOX project aims to overcome barriers in secondary data use, ultimately driving more efficient, inclusive, and secure clinical research that benefits both healthcare systems and patients/citizens.

Author: P. Mangesius, T. Schabetsberger, K. Kreiner, K. Donsa

DOI:10.3233/SHTI250983

Publication Date: August 2025

Abstract:
Introduction: Real-world data from Electronic Health Record (EHR) systems is crucial in medical research, particularly for understanding diseases, treatment efficiency, patient safety, personalized medicine, drug research, and public health surveillance. Despite its significance, collecting and utilizing this data for secondary use is challenging due to bottlenecks such as recruitment of study subjects and difficulties in linking high-quality data. The concept of data donation by patients is gaining importance also in the context of the European Health Data Space (EHDS) framework. The paper aims to propose a scalable, open architecture for collecting real-world data in EHRs for secondary use. Methods: Current EHR technologies, operational readiness and interoperability standards are assessed based on existing EHRs in the European Union. Using a literature search related work has been identified to derive an open architecture for collecting structured data for secondary used. Results: An open architecture based on IHE and FHIR interoperability standards is proposed to integrate into existing, standard based EHR systems respecting consent requirements and data zones regarding privacy levels.

Author: S. Beyer, N. Tanjga, G. Kleinoscheg, K.Donsa, K. kreiner, G. Schreier

DOI: 10.3389/fmed.2025.1661091

Publication Date: September 2025

Abstract:
Introduction: Healthcare systems generate vast amounts of data in diverse and often incompatible formats. Efficient conversion between these formats is essential to ensure interoperability and enable secondary data use, particularly in the context of the European Health Data Space (EHDS) and the proposed Austrian Health Data Donation Space (AHDDS). While standards such as HL7 FHIR aim to facilitate interoperability, inconsistencies in implementation persist. Electronic health record (EHR) providers, including Austria’s ELGA, continue to face challenges in this area. The FHIR mapping language (FML) offers a promising solution for format translation, but current tools for executing FML mappings are limited, especially in terms of processing speed. To address this gap, there is a pressing need for a compiler that translates FML mappings into efficient, executable code.

Materials and methods: We developed the Mapping Language Compiler for Health Data (MaLaC-HD), which compiles FML code into Python. To assess performance, we benchmarked the compiler using a large ELGA document on a typical end-user device, comparing execution speed with existing FML tools. Baseline overhead was measured using an empty mapping. Conformance was manually evaluated by comparing the output of a wide range of example mappings and input data against the Java reference implementation. Additionally, we analyzed the structure and correctness of the generated Python code to assess functional completeness.

Results: After adjusting for overhead, MaLaC-HD achieved execution speeds nearly 100 times faster than existing tools. The output closely matched that of the reference implementation, with only minor discrepancies. The generated Python code met all functional requirements and demonstrated the compiler’s ability to support complex transformations. MaLaC-HD is publicly available under the LGPL license.

Conclusion: MaLaC-HD can serve a wide array of use cases and has the potential to integrate with existing platforms for secondary data use to support large-scale health data research across Europe and beyond. MaLaC-HD could provide the EHR community with a powerful, efficient tool for accelerating data transformation, an essential capability for the success of the EHDS initiative.