Use Cases

Use Case 1: Alerting in Care – IT Support for Patient Recruitment

Clinical studies often fail due to the insufficient number of suitable study participants. In the use case “Alerting in Care – IT Support for Patient Recruitment” the consortium MIRACUM integrates recruitment platforms into the Hospital Information System environments at each of its participating university hospitals. This will support recruitment processes with innovative IT solutions applying existing routine data.

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New therapeutic options, whether drugs, treatment methods or other medical products, are subject to complex testing procedures, including the conduct of studies. For pursuing such studies those responsible for the studies often face one major challenge: identifying and recruiting sufficient participants in a timely manner. In a university hospital´s busy daily routine with many studies running at the same time, today efficient tools are missing, which would easily identify patients who are eligible for the respective studies. Therefore, Use Case 1 of the MIRACUM consortium is developing a digital recruitment platform that is designed to automatically check who is suitable for a study based on existing patient data.

As a first step, all MIRACUM sites have harmonized their study description criteria and consented on a joined information model for a FHIR-based study description. Based on this all MIRACUM sites have established local study registries and initiated processes to continuously enter and maintain study descriptions of all clinical trials pursued at their sites. As early as the second half of 2019, the local study registries at the MIRACUM sites began to enter study data in a standardized manner. The study information from these local implementations is also recorded in a MIRACUM-wide central study registry, in which information on the individual studies can be searched across all sites. The central registry uses a FHIR-based interface to receive study information from all local registries. A web interface allows keyword searches and filtering by participating centers and study category. The website of the central registry is available at Up-to-date information is guaranteed by automated exports from the local registries.

The FHIR standard is also used for the implementation of IT-supported patient recruitment: An FHIR server serves as a central integration and communication platform. A search module is based on the central databases of the data integration centers at the MIRACUM sites to determine which of the patients currently enrolled are potential subjects for a study. These patients are placed on a screening list, which doctors can view as a clinic-internal web application. At the same time, a notification tool informs them about current recruitment proposals by e-mail. The physician then has the opportunity to check the respective patient file and recommend the patient to participate in the respective study. The procedure is now to be examined in an evaluation study in order to derive further optimization options.

Use Case 1 Booklet


Gulden C, Blasini R, Nassirian A, Stein A, Altun F, Kirchner M, Prokosch H, Boeker M. Prototypical Clinical Trial Registry Based on Fast Healthcare Interoperability Resources (FHIR): Design and Implementation Study. JMIR Med Inform 2021; 9:e20470. Doi: 10.2196/20470.

Reinecke I, Gulden C, Kümmel M, Nassirian A, Blasini R, Sedlmayr M. Design for a Modular Clinical Trial Recruitment Support System Based on FHIR and OMOP. Stud Health Technol Inform. 2020;270:158-162. Doi: 10.3233/SHTI200142. PMID: 32570366.

Becker L, Ganslandt T, Prokosch HU, Newe A. Applied practice and possible leverage points for information technology support for the screening for clinical trials: A qualitative study. JMIR Med Inform. 2020;8:e15749. Doi: 10.2196/15749. PMID: 32442156.

Gulden C, Kirchner M, Schüttler C, Hinderer M, Kampf M, Prokosch HUP, Toddenroth D. Extractive summarization of clinical trial descriptions. Int J Med Inform. 2019;129:114-121. Doi: 10.1016/j.ijmedinf.2019.05.019. PMID: 31445245.

Gulden C, Landerer I, Nassirian A, Altun FB, Andrae J. Extraction and Prevalence of Structured Data Elements in Free-Text Clinical Trial Eligibility Criteria. Stud Health Technol Inform. 2019;258:226-230. Doi: 10.2196/15749. PMID: 30942751.

Hasselblatt H, Andrae J, Tassoni A, Fitzer K, Bahls T, Prokosch HU, Boeker M. Establishing an Interoperable Clinical Trial Information System Within MIRACUM. Stud Health Technol Inform. 2019;258:216-220. Doi: 10.3233/978-1-61499-959-1-216. PMID: 30942749.

Sommer M, Kirchner M, Gulden C, Egloffstein S, Lux MP, Beckmann MW, Mackensen A, Prokosch HU. Design and Implementation of a Single Source Multipurpose Hospital-Wide Clinical Trial Registry. Stud Health Technol Inform. 2019;258:164-168. Doi: 10.3233/978-1-61499-959-1-164. PMID: 30942738.

Gulden C, Mate S, Prokosch HU, Kraus S. Investigating the Capabilities of FHIR Search for Clinical Trial PhenotypingStud Health Technol Inform. 2018;253:3-7. Doi: 10.3233/978-1-61499-896-9-3. PMID: 30147028.

Use Case 2: From Data to Knowledge – Clinico-Molecular Predictive Knowledge Tool

In the MIRACUM consortium’s use case “From Data to Knowledge – Clinico-Molecular Predictive Knowledge Tool”, the aim is to develop and establish methods for the cross-site analysis of patient data in the participating university hospitals. The methods will be used to generate knowledge that can be directly applied in clinical practice.

MIRACUM – Gemeinsam gegen Asthma und COPD (in German; source: BMBF)

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The gradual expansion of the data integration centers at the medical university sites of the Medical Informatics Initiative will create a basis for identifying patient cohorts based on clinical parameters, biomarkers and molecular/genomic studies and dividing them into subgroups. In Use Case 2 of the MIRACUM consortium, predictive models are to be developed on this basis, which can contribute to medical knowledge and potentially support physicians in their diagnostic and therapeutic decisions. In the clinical area, the use case focuses on patients with lung diseases (asthma and COPD) and brain tumors.

A concrete example: Alpha-1-antitrypsin deficiency (AATM) is a hereditary disease in which the enzyme alpha-1-antitrypsin is missing in the body. As a result, tissue damage to the lungs and liver can occur, leading to chronic obstructive pulmonary disease (COPD) at a young age. Thus, COPD patients with and without AATM often differ fundamentally – both in age and in smoking history, the biggest risk factors for COPD. The problem is that COPD with AATM is rather rare, which is why prognostic factors for complications and emerging comorbidities have usually been established in COPD records of patients without AATM. The use case “From Data to Knowledge” now wants to investigate whether these factors can be used for COPD patients with AATM despite the fundamental differences.

The corresponding data in MIRACUM are to be regarded as particularly worth protecting from a data protection perspective. A centralized collection across all locations is potentially too great a risk. Therefore, the goal is not to bring the data to analysis, but to bring the analysis to the data. More precisely, only aggregated and anonymous data should leave the sites. This principle is implemented by the software DataSHIELD, which was developed at the University of Newcastle. The software is published under an open source license and can be used freely. DataSHIELD offers various procedures that are part of the statistical toolkit, ranging from the calculation of simple ratios, such as averages or frequencies, to more complex regression models that are used in the clinical application described above. In addition to these already implemented analysis procedures, DataSHIELD also offers a flexible and expandable infrastructure to develop new types of artificial intelligence methods and apply them to networked data. To this end, the MIRACUM consortium is in close exchange with the development team and the DataSHIELD community.

In addition to the use of anonymous aggregated data, the use of synthetic data is researched in use cases to meet data protection requirements. Synthetic data are data that do not contain real observations and patient information, but rather replicate general characteristics and statistical relationships of real data. For the use of data in research, this means that virtual patient data are created for each site, which are not bound to the data of an individual patient. Such data can then be shared and allow the use of different analysis concepts, such as standard statistical analyses or artificial intelligence techniques. Machine learning approaches are required to generate synthetic data from real data. Specifically, so-called generative models are used, which map the systematic and random variability of the original data. This is made possible by artificial intelligence techniques, in particular techniques from the field of deep learning. The generation of virtual patient data is distributed over different MIRACUM locations. The DataSHIELD infrastructure is also used for this purpose. In this way, the analysis of the data with established procedures and the development of new methods for the data protection-compliant analysis of distributed patient data can be jointly advanced.

Use Case 2 Booklet


Gruendner J, Wolf N, Tögel L, Haller F, Prokosch HU, Christoph J. Integrating Genomics and Clinical Data for Statistical Analysis by Using GEnome MINIng (GEMINI) and Fast Healthcare Interoperability Resources (FHIR): System Design and Implementation. JMIR 2020; 22:e19879. DOI: 10.2196/19879.

Gruendner J, Prokosch HU, Schindler S, Lenz S, Binder H. A Queue-Poll Extension and DataSHIELD: Standardised, Monitored, Indirect and Secure Access to Sensitive DataStud Health Technol Inform. 2019;258:115-119. Doi: 10.3233/978-1-61499-959-1-115. PMID: 30942726.

Use Case 3: From Knowledge to Action – Support for Molecular Tumor Boards

Precision medicine already plays a major role in the care of tumor patients. For many tumors, so-called “driver mutations” can now be identified by means of molecular biological characterization in order to treat them in a targeted manner if a suitable drug is available.
In Molecular Tumor Boards (MTB), clinical information and molecular/genetic test results converge for interdisciplinary decision-making. In order to support MTBs, the MIRACUM consortium aims to improve the complex processes of quality assurance, data preparation, data analysis, data integration and information retrieval between genetic high-throughput analysis and medical therapy decisions with innovative IT solutions. In addition, clinicians will be offered decision support through efficient data visualization.

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Use Case 3 of the MIRACUM consortium aims to support MTBs with IT solutions. An MTB is an interdisciplinary, cross-organ conference where clinical-pathological data and molecular findings of selected cancer patients are discussed. An interdisciplinary team of physicians and scientists from the fields of medicine, bioinformatics, medical informatics and biology votes on which therapy options promise the best chances of fighting the tumor on the basis of the available data. The application aims to identify patients without conventional or promising therapy options or with rare tumor diseases and to offer them potentially effective treatment options with a targeted therapy within the framework of precision medicine – in the context of clinical studies or individual healing trials.

In the daily clinical routine of medical centers, more and more high-throughput data of patients with the diagnosis of an advanced tumor is generated. In order to be able to process this enormous amount of data, standardized bioinformatics tools must be developed. These tools should support physicians in interpreting these complex data. Within the scope of the use case, a bioinformatics process for the analysis of the individual tumor DNA sequence was developed – the so-called MIRACUM Pipe. The pipeline was successfully distributed and installed at all MIRACUM sites in the second half of 2019. This allows the analysis of sequencing data to be performed in a uniform and standardized manner, starting with raw data and ending with the determination and annotation of tumor-specific mutations.
The automated pipeline delivers reliable and reproducible results with a single click as a PDF report that serves as the basis for therapy recommendations. In addition, a file is generated which can be imported into the cBioPortal software platform of the Memorial Sloan Kettering Cancer Center, a private cancer clinic and research facility in New York. In cBioPortal the results are visualized, the clinical and molecular biological data of the patient are combined and presented in a reduced form. This facilitates the interpretation of the data. Furthermore, the data can be viewed in the context of other cases.

In order to enhance cBioPortal with additional functionalities and adapt it to different data requirements, detailed stakeholder analyses with all MIRACUM sites were conducted and published in 2018. Since 2019, the University of Lübeck officially cooperates within MIRACUM Use Case 3 and implements it at the University Hospital Schleswig-Holstein, a HiGHmed site. Based on the stakeholder analyses, the process for creating a therapy recommendation is currently being implemented organizationally and technically for the local MTB. The cooperation with the University of Lübeck is the first cross-consortium collaboration within the German medical informatics initiative (MII) as a proof of principle for interoperability between IT architectures of data integration centers of two MII consortia.

Use Case 3 Booklet


Pugliese P, Knell C, Christoph J. Exchange of Clinical and Omics Data According to FAIR Principles: A Review of Open Source Solutions. Methods Inf Med. 2020;59:e13-e20. Doi:10.1055/s-0040-1712968. PMID: 32620018.

Gruendner J, Wolf N, Tögel L, Haller F, Prokosch HU, Christoph J. Integrating Genomics and Clinical Data for Statistical Analysis by Using GEnome MINIng (GEMINI) and Fast Healthcare Interoperability Resources (FHIR): System Design and Implementation. JMIR 2020; 22:e19879. DOI: 10.2196/19879.

Jaravine V, Balmford J, Metzger P, Boerries M, Binder H, Boeker M. Annotation of Human Exome Gene Variants with Consensus Pathogenicity. Genes 2020; 11 (9), 1076. DOI: 10.3390/genes11091076.

Fuchs M, Kreutzer FP, Kapsner L.A, Mitzka S, Just A, Perbellini F, Terracciano CM, Xiao K, Geffers R, Bogdan C, Prokosch, HU, Fiedler J, Thum T, Kunz M. Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies. Int J Mol Sci 2020; 21, 4727. DOI: 10.3390/ijms21134727.

Walther D, Paret C, Ritzel C, Büchner P, Unberath P, Maier W, Metzger P, Christoph J, Storf H, Boerries M, Wagner S. Definition of an annotation pipeline for a molecular tumor board focused on the processing of the result and oncological drugs in terms of usability and approval status. Doi: 10.3205/19gmds179.

Hinderer M, Boerries M, Boeker M, Neumaier M, Loubal FP, Acker T, Brunner M, Prokosch HU, Christoph J. Implementing Pharmacogenomic Clinical Decision Support into German Hospitals.
Stud Health Technol Inform. 2018;247:870-874. Doi: 10.3233/978-1-61499-852-5-870. PMID: 29678085.

Hinderer M, Boeker M, Wagner SA, Binder H, Ückert F, Newe S, Hülsemann JL, Neumaier M, Schade-Brittinger C, Acker T, Prokosch HU, Sedlmayr B. The experience of physicians in pharmacogenomic clinical decision support within eight German University Hospitals. Pharmacogenomics. 2017;18(8):773-785. Doi: 10.2217/pgs-2017-0027. PMID: 28593816.

Hinderer M, Boeker M, Wagner SA, Lablans M, Newe S, Hülsemann JL, Neumaier M, Binder H, Renz H, Acker T, Prokosch HU, Sedlmayr M. Integrating clinical decision support systems for pharmacogenomic testing into clinical routine – a scoping review of designs of user-system interactions in recent system development. BMC Med Inform Decis Mak. 2017;17:81. Doi: 10.1186/s12911-017-0480-y. PMID: 28587608.

Hinderer M, Boerries M, Haller F, Wagner S, Sollfrank S, Acker T, Prokosch HU, Christoph J. Supporting Molecular Tumor Boards in Molecular-guided Decision-making – the Current Status of Five German University Hospitals. Stud Health Technol Inform. 2017;236:48-54. Doi: 10.3233/978-1-61499-759-7-48. PMID: 28508778.

Use Case “RECUR”: Nationwide Registry for Recurrent Urolithiasis in the Upper Urinary Tract

Urinary stone disease is a widespread disease with a high recurrence rate. Covering  a period of 5 years, the German Federal Ministry of Education and Research (BMBF) will fund the establishment of a Nationwide Registry for Recurrent Urolithiasis in the Upper Urinary Tract (RECUR). The project led by urologist Prof. Martin Schoenthaler (Freiburg) is one of six projects that will receive financial support as part of the BMBF initiative to set up exemplary registers for health services research.

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The digital stone register was developed under the patronage of the German Society of Urology (Deutsche Gesellschaft für Urologie e.V., DGU). It is supported by DGU working groups on Urological Research (Arbeitskreis Urologische Forschung, AuF) and Urolithiasis (Arbeitskreis Harnsteine).

The registry will make use of the digital infrastructure of the BMBF’s Medical Informatics Initiative (MII). It will accomplish existing data from the hospital information systems together with patient derived data provided via a new patient app. This project will be implemented as an additional use case for the MII MIRACUM consortium. Urologists and medical informatics specialists from MIRACUM centres Dresden, Erlangen, Frankfurt, Freiburg, Giessen, Greifswald, Magdeburg, Mainz, Mannheim and Marburg have joined forces to improve the structural and procedural framework for patients with recurrent urolithiasis. This includes advanced diagnostic algorithms and treatment paths.


Schönthaler M, Praus F. Urolithiasisforschung – Big Data und künstliche Intelligenz. Urologe. 2019;58: 1298–1303. Doi:10.1007/s00120-019-01032-8. PMID: 31520098.

Schoenthaler M, Boeker M, Horki P. How to compete with Google and Co.: big data and artificial intelligence in stones. Curr Opin Urol. 2019;29:135–142. Doi: 10.1097/MOU.0000000000000578. PMID: 30531434.

Use Case “POLAR_MI”: Polypharmacy, Drug Interactions and Risks

The Use Case “Polypharmacy, Drug Interactions and Risks” (POLypharmazie, Arzneimittelwechselwirkungen und Risiken), which includes all four consortia of the Medical Informatics Initiative, aims to contribute to the detection of health risks in patients with polymedication by using methods and processes of the Medical Informatics Initiative.

Polymedication occurs particularly in older patients with multimorbidity. This can lead to drug interactions that either reduce or enhance the desired effect of individual active ingredients or lead to undesired effects due to pharmacological interactions. These can trigger additional clinical pictures and additional need for therapy, which, however, would be avoidable with better drug management.

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Objectives of the POLAR_MI Use Case

In the POLAR_MI Use Case, medical computer scientists, biometricians, epidemiologists, pharmacists, clinical pharmacologists and health researchers from 21 institutions, including 13 university hospitals, work together to

1) develop and implement methods to collect prospectively and retrospectively available personal data on prescribed drugs (e.g. medication plans) as well as on prescriptions and drug dispensations from pharmacies at several sites of the four MII consortia,
2) classify a selected range of polymedications according to available methods regarding Potentially Inadequate Medication (PIM) and a selected range of drugs as high-risk prescriptions,
3) electronically map score systems for identifying high-risk patients for relevant drug-related problems, and
4) identify the occurrence of adverse drug reactions and their consequences at an early stage or to avoid them completely (e.g. new diagnoses/interventions, intensive care, re-introduction, new (approved) medications)

Although a core program covering the above-mentioned objectives has been designed for all participating sites, additional specific sub-projects are planned to prepare future follow-up projects. One subproject deals with record linkage with 1-year mortality and, in cooperation with health insurance companies, with the linking of data on drug use and adverse drug events (ADE) in outpatient care. A further subproject is working on a text body for Natural Language Processing (NLP) with regard to adverse drug reactions.

The use case POLAR_MI will

  • obtain data on drugs in Germany in the vicinity of university hospitals,
  • demonstrate that effective use of these health data from MII centers in all four MII consortia can be made, and
  • provide and validate a set of algorithms for classifying high-risk drugs and PIMs that can be used prospectively to improve drug safety.
MIRACUM project partners
  • Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Universitätsklinikum Erlangen
  • Universitätsklinikum Freiburg
  • Universitätsklinikum Gießen
  • Universität Heidelberg, Medizinische Fakultät Mannheim
Further Information

MII Website – POLAR_MI

Use Case “CORD_MI”: Collaboration on Rare Diseases

The Use Case “Collaboration on Rare Diseases” (CORD-MI) is a collaborative project comprising the four consortia of the Medical Informatics Initiative, in which numerous German university hospitals and partner institutions are involved. The aim is to improve care and research in the field of rare diseases. It builds on the Innovation Fund projects TRANSLATE-NAMSE and ZSE-DUO as well as the national DIMDI project “Coding of Rare Diseases” and uses the development status of the Medical Informatics Initiative across consortia.

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It is estimated that around four million German citizens are affected by approximately 8,000 known rare diseases. Due to the rarity of each individual disease and the lack of consideration in hospital documentation, it is not yet possible to make concrete statements on the frequency, distribution and course of the disease, which has a negative impact on research, diagnosis and therapy. Based on the National Action Plan for People with Rare Diseases (NAMSE) from 2013, various measures have been implemented in Germany to support the coding of rare diseases (DIMDI-SE) and to create better care structures for patients in university hospitals (TRANSLATE-NAMSE, ZSE-Duo). In addition, there is a NAMSE strategy paper on digitisation needs for rare diseases. Despite these and other important care and research projects at national, European and international level, it has not yet been possible to establish sustainable structures for a digital network for data exchange for rare diseases.

CORD-MI wants to contribute to changing this situation and uses the organizational and technical solutions of the Medical Informatics Initiative to

1) improve the visibility of rare diseases
2) provide insights into the reality of supply,
3) improve the quality of patient care and
4) stimulate research in the field of rare diseases.

To this end, special concepts and solutions will be developed by 2022 to ensure that

  •  the documentation of rare diseases in university hospitals is improved
  • the collected data can be shared and
  • data protection is ensured in the nationwide data collection and data evaluation.
MIRACUM project partners
  • Technische Universität Dresden
  • Universitätsklinikum Frankfurt am Main
  • Universitätsklinikum Freiburg
  • Universitätsklinikum Gießen
  • Universitätsklinikum Magdeburg
  • Universitätsklinikum Mannheim
  • Universitätsklinikum Marburg
Further Information

MII Website – CORD_MI

Use Case “ABIDE_MI”: Aligning Biobanking and DIC Efficiently

The use case “Aligning Biobanking and DIC Efficiently” (ABIDE_MI) is a collaborative project involving the majority of German university hospitals in the four consortia of the Medical Informatics Initiative (MII). The aim of the project, which started in May 2021, is for the data integration centers (DIC) of the MII to be able to link patient data from routine care with data on biospecimens and make it usable for research. In particular, researchers will be able to submit feasibility queries via the MII’s future German Portal for Medical Research Data for Health.

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The project, funded by the German Federal Ministry of Education and Research, takes an interdisciplinary approach, combining the achievements and experience of the MII, the German Biobank Node (GBN) and the biobanks of the German Biobank Alliance (GBA) in a sustainable health IT infrastructure: the MII’s German Portal for Medical Research Data.

The project will enable future close collaboration between biobanks and DIC at 24 sites of German university medicine, both on a technical and regulatory level.

This is based on

  1. the development of methods that enable MII DIC to link patient data with selected information on associated biospecimens collected in the context of patient care (i.e., excluding clinical trials, research consortia, and cohorts) by university hospitals and available in the associated biobanks.
  2. the establishment of a central FHIR-based, MII-wide (feasibility) query and analysis tool of the German Research Data Portal for Health. This should
    • identify patient groups with appropriate biospecimens suitable for a given research project, and
    • distributed data analyses across all participating university hospitals.
  3. the development of software tools and their easy dissemination via software containers also to newly added MII sites, so that a fast and easy expansion of the network is possible in the future.
  4. The development of a scalable infrastructure that will allow seamless connection of the MII-DIC to the European BBMRI-ERIC Common Service IT portal for the management of healthcare biospecimens.
Further Information

MII Website – ABIDE_MI

Use Case “CODEX”: COVID-19 Data Exchange Platform

With the research data platform CODEX, a secure, expandable and interoperable platform for the provision of research data on Covid-19 is being established, connecting university hospitals nationwide. This is intended to make structured data of high quality available to the scientific community and to enable innovative evaluations.

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The basis is the preliminary work of the Medical Informatics Initiative (MII) of the Federal Ministry of Education and Research (BMBF). The already established decentralized nationwide research data infrastructure via the data integration centers (DIC) of the MII will be supplemented by a central component for CODEX. For this central research data platform, the aim is to create a database from different data sources that is available as quickly as possible and meets the requirements of research ethics (FAIR principles) and the EU General Data Protection Regulation.

The CODEX project was launched in August 2020 and is a central component of the Network University Medicine (NUM), which is funded by the BMBF with 150 million euros. In the initial phase, the clinical research platform of the German Center for Cardiovascular Research (DZHK) will be used. In the 2021 expansion stage, the DIC of the MII will be used.

The cross-site platform will make complex Covid-19 research datasets including clinical data and biospecimen data from all German university hospitals available to researchers in a pseudonymized form. CODEX thus aims to contribute to a better understanding of the Covid-19 disease, to serve as a basis for policy decisions, and to advance the development of innovative and high-quality services and applications for healthcare institutions, citizens.

Further Information

MII Website – CODEX
NUM Website – CODEX

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