Core Unit eHealth and Interoperability (CEI)

The increasing availability of digital health data—in hospitals, medical practices, research laboratories, genome databases or mobile health applications—holds great potential for medicine: modern statistical methods, artificial intelligence (AI) and big data analytics promise better diagnostics, personalized therapies and early disease prevention. However, to use these technologies to their full capacity, digital health data have to be processed across different systems and institutions. This requires the use of consistent standards and medical terminologies.

In the core unit "eHealth and Interoperability" headed by Prof. Dr. Sylvia Thun, we develop strategies and concepts for the fast, secure and reliable usage of medical data across different systems. In cooperation with hospitals, research institutions, industry and politics, we develop interoperability solutions using international IT standards and terminologies.

  • HL7 V2 und FHIR 
  • ISO 13606 (openEHR)
  • GS1
  • SNOMED 
  • LOINC
  • ICD-11
  • IDMP 
  • OMOP/OHDSI
  • CDISC
  • DICOM
  • HPO und andere OMICS-Terminologien

We do research and develop structures for semantic interoperability in the supply and research space and are significantly involved in the specification of the research data infrastructure. Our goal is a unified digital ecosystem that enables innovative medicine with more accurate predictive tools, personalized therapies, more patient participation and higher patient safety. Our vision is an interconnected digital health infrastructure that enables new technologies to predict disease more accurately, develop better, personalized treatments, promote active patient participation and ensure high data security.

Our scientific work is based on the standards of THE JOINT INTITIATIVE COUNCIL, HL7, GA4GH, ISO TC215

Publications

Publications

Khvastova M, Witt M, Essenwanger A, Sass J, Thun S, Krefting D. Towards Interoperability in Clinical Research - Enabling FHIR on the Open-Source Research Platform XNAT. J Med Syst. 2020 Jul 9;44(8):137. https://doi:10.1007/s10916-020-01600-y

Vogelsang L, Lehne M, Schoppmann P, Prasser F, Thun S, Scheuermann B, Schepers J. A Secure Multi-Party Computation Protocol for Time-To-Event Analyses. Stud Health Technol Inform . 2020 Jun 16;270:8-12

Radke TF, Patton SJ, Pantazoglou E, Sass J, Thun S. Evaluation of current genetic testing reports in German-speaking countries with regard to secondary use and future electronic implementation. Eur J Hum Genet. 2020 May;28(5):558-566. https://doi:10.1038/s41431-020-0586-z

Schaefer J, Lehne M, Schepers J, Prasser F, Thun S. The use of machine learning in rare diseases: a scoping review. Orphanet Journal of Rare Diseases Volume 15, Article number: 145 (2020)

Dewey M, Bosserdt M, Dodd JD, Thun S, Kressel HY (2019). Clinical Imaging Research: Higher Evidence, Global Collaboration, Improved Reporting, and Data Sharing Are the Grand Challenges. Radiology291(3), 547–552. https://doi.org/10.1148/radiol.2019181796

Lehne M, Luijten S, Vom Felde Genannt Imbusch P, Thun S, (2019). The Use of FHIR in Digital Health—A Review of the Scientific Literature. Studies in Health Technology and Informatics267, 52-58. https://doi.org/10.3233/SHTI190805

Lehne M, Sass J, Essenwanger A, Schepers J, Thun S. (2019) Why digital medicine depends on interoperability. NPJ Digital Medicine2(79). https://doi.org/10.1038/s41746-019-0158-1

Miñarro-Giménez JA, Cornet R, Jaulent MC, Dewenter H, Thun S, Gøeg KR, Karlsson D, Schulz S. (2019). Quantitative analysis of manual annotation of clinical text samples. International Journal of Medical Informatics123, 37–48. https://doi.org/10.1016/j.ijmedinf.2018.12.011

Otten H, Thun S, Rathmer A. (2019). Grundlage des Einkaufs 4.0: Interoperabilität von Supply Chain und Medizin durch valide Stammdaten. Klinik Einkauf01(01), 42–44. https://doi.org/10.1055/s-0036-1595717

Sass J, Essenwanger A, Luijten S, Vom Felde Genannt Imbusch, P, Thun S. (2019). Standardizing Germany’s Electronic Disease Management Program for Bronchial Asthma. Studies in Health Technology and Informatics267, 81-85. https://doi.org/10.3233/SHTI190809

Sass J, Lehne M, Thun S. (2019). Comparing Two Standardized Value Sets of Infectious Agents: Implications for Semantic Interoperability. Studies in Health Technology and Informatics264, 1574–1575. https://doi.org/10.3233/SHTI190541

Schepers J. Thun S. (2019). (Un-)Konzertierte Digitalisierung im Gesundheitswesen – Möglichkeiten und Herausforderungen im Kontext der sozialen Sicherung. Soziale Sicherheit8-9, 318-327.

Thun S, Lehne M. (2019). Interoperabilität – Voraussetzung für künstliche Intelligenz und Big Data in der Medizin. E-HEALTH-COM1, 47-49. https://e-health-com.de/details-news/interoperabilitaet-voraussetzung-fuer-kuenstliche-intelligenz-und-big-data-in-der-medizin/

Thun S, Lehne M. (2019). Intelligent dank Interoperabilität. das Krankenhaus4, 286-287. https://www.daskrankenhaus.de/de/archive/topic-of-the-month/74

 

Projects

EUCANCAN European-Canadian Cancer Network

European-Canadian Cancer Network 

EUCANCan is a European Canadian cooperation funded by the European Union’s research and innovation program Horizon 2020 and the Canadian Institutes of Health Research.  The four-year project aims at enhancing modern oncology by implementing a culturally, technologically, and legally integrated framework across Europe and Canada, to enable and facilitate the efficient analysis, management, and sharing of cancer genomic data.  

https://eucancan.com

HiGHmed

HiGHmed is working on novel, interoperable solutions in medical informatics with the aim to make medical patient data accessible for clinical research and education which will, in turn, improve patient care. Building safe data integration centres, the project aims to establish a technology platform enabling clinicians to take data-based and patient-centric decisions.

The HiGHmed Use Case Infection Control

The infection control use case will develop a software system to analyse various data sources from hospitals, with the aim to detect potentially dangerous germs as early as possible. This automated early warning system will help to protect patients from new infections, but also to understand their causes and how infectious diseases spread. At first, HiGHmed will target multidrug-resistant germs within and across university and non-university hospitals. These organisms are dangerous and may lead to life-threatening situations.

 

https://www.highmed.org

 

 

NFDI4Health National Research Data Infrastructure for Personal Health Data

NFDI4Health is a national research data infrastructure for personal health data. NFDI4Health aims to apply FAIR principles to make data from epidemiology, clinical studies, and healthcare internationally accessible. Our Core Unit supports the project with the use of international terminologies and (meta-)data standards for interoperable data exchange. 

www.nfdi4health.de

NFDI4Health Task Force COVID-19

The NFDI4Health Task Force COVID-19 aims to make medical and epidemiological research on COVID-19 swiftly available to the research community using harmonized data models. The Core Unit supports the project by using international standards to harmonize COVID-19 research data. 

www.nfdi4health.de/de/task-force-covid-19

AIQNET AI for Clinical Studies

KIKS is part of the innovation competition “Artificial Intelligence (AI) as a driver for economically-relevant ecosystems” and funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The project intends to automize medical data acquisition and analyze with the help of AI, and thereby attempts to dissolve the bottleneck hindering/delaying progress in medical engineering: non-standardized data/data variability. Both, medical device manufacturers and the healthcare sector desperately need clinical data for their further developments, but still have to overcome several obstacles to do so. KIKS therefore aims to develop a platform for the AI-based data extraction, data protection compliant data exchange, as well as a digital ecosystem to bring data-based solutions to common practice.  

 

https://aiqnet.eu

 

 

CORD Collaboration on Rare Diseases

The use case “Collaboration on Rare Diseases” (CORD-MI) is a collaboration project based on four consortia, in which numerous university clinics and cooperating partners institutions are involved. Its purpose is the improvement of clinical services and research on rare diseases, based on the innovation-funds projects TRANSLATE-NAMSE and ZSE-DUO, as well as on the national DIMDI project “Coding of Rare Diseases”, CORD makes use of the developments across the consortia of the Medical Informatics Initiative.  

 

https://www.medizininformatik-initiative.de/de/CORD

NAPKON National Pandemic Cohort Network

The NAPKON project sets the foundation for a deeper understanding of the course of a Covid-19 infection and the scientific investigation of possible therapies, by combining research studies, clinical data, biological samples, and imaging data. NAPKON is closely related to the structure of the National Research Data Platform and cooperates with the COVIM project. The studies made possible by NAPKON could for example provide information about the long-term effects of Covid-19, even if the affected patients change from a hospital to a doctor’s office while still being treated.  

CODEX | COVID-19 Data Exchange Platform

Establishment of a nationwide infrastructure for the storage and provision of Covid-19 research data sets. A comprehensive database, data collection tools, use & access procedures and a trustee office are planned.
The infrastructure will be able to map complex Covid-19 research data sets, including clinical data, image data and data on biosamples, in a multicenter, patient-related and pseudonymized manner and make them available to researchers.

 

ORCHESTRA Connecting European cohorts to increase common and effective response to SARS-CoV-2 pandemic

ORCHESTRA offers an innovative approach to learn from the pandemic SARS-CoV –2-crisis and derive recommendations to be better prepared in case of a second infection wave or for a future pandemic. The project’s objective is to deliver well-founded scientific insights for the prevention and treatment of the infection caused by SARS-CoV-2, thereby evaluating epidemiological, clinical, micro-biological, and genotypical aspects of the population, the environment, and the socio-economical characters. The project leans onto existing and new large-scale population cohorts in Europe (France, Germany, Spain, Italy, Belgium, Romania, Netherlands, Portugal, Luxembourg and Slovakia), as well as in further countries outside of Europe (India, Peru, Ecuador, Colombia, Venezuela, Argentina, Brazil, Kongo and Gabon), including SARS-CoV-2-infected individuals, as well as non-infected ones of multiple age groups and backgrounds.  

 

 

SPOCK Prognosis of intensive-care COVID-19-capacities

A set of dynamic adaptive prognosis models for the prediction of COVID-19 case numbers in intensive care is developed.

COMPASS Coordination on mobile pandemic apps best practice and solution sharing

IN COMPASS, a platform will be established to coordinate and provide concrete methods and tools for pandemic apps. Partners from science and industry are joining forces nationwide and are pursuing an open source approach. Together they coordinate and evaluate the pandemic apps and create recommendations. This also creates a basis for digital solutions to be better digitally prepared for future pandemics.

 

SCREEN4CARE Ecosystem for rare NMDs

Shortening the path to diagnosis of rare diseases through the use of genetic newborn screening and digital technologies

 

CAEHR Cardiovascular Diseases - Enhancing Healthcare through cross-Sectoral Data Integration