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Institute of Medical Information Management

Current Research Projects

Project “H2OgoesEHDEN”

Funding: EHDEN

Duration: 2022–ongoing

Team members at MIM: Georg Duftschmid, Florian Katsch

Background: The European Health Data & Evidence Network (EHDEN) aims to improve health outcomes for patients and care providers by means of real world evidence across different care institutions. EHDEN partner institutions transform their local health data from its source format into the standardized OMOP common data model (CDM). Hereby, an EU-wide federated network of harmonized health data sources is established that allows cross-institutional analyses.

Methods: We will map patient data that are recorded at MedUni in the context of the IMI-project H2O to the OMOP CDM. The data will be related to the following diagnoses: (1) diabetes, (2) inflammatory bowel disease and (3) cancer. The data will be extracted from the MedUni research information system RDA. Amongst other items, the data will cover diagnoses, procedures, lab values and medication of specific relevance to the three disease areas.

Project “ELGA-Recruit” – Recruitment of Patients for Clinical Trials Using Data from the Nationwide Shared EHR System ELGA

Funding: None

Duration: 2019–ongoing

Team members at MIM: Georg Duftschmid, Raik Müller, Maximilian Schubert

Partners: Doreen Schmidl (Department of Clinical Pharmacology), Clemens Nadvornik (Department of Clinical Pharmacology), Markus Zeitlinger (Department of Clinical Pharmacology)

Background: Many clinical trials suffer from delays due to inefficient and time-demanding recruitment methods or are not completed at all. Reuse of data from Electronic Health Record (EHR) systems can improve trial recruitment. According to our research, Shared EHR systems represent attractive data sources for this purpose. The Austrian nationwide Shared EHR system ELGA in particular holds structured data for more than 60% of eligibility criteria that are commonly used in clinical trials. The goal of this project is to analyze to what extent trial recruitment at the Medical University of Vienna (MedUni) can benefit from reusing ELGA data.

Methods: The corresponding potential of ELGA data will be analyzed based on two running trials of the MedUni. Hereby, ELGA data of patients who were included in / excluded from these trials will be analyzed to what extent they provide clues that match with the decision that was made based on conventional methods (=gold standard). For this purpose, the trial’s inclusion and exclusion criteria for which ELGA contains relevant data will be represented in a machine-readable format. Only ELGA data of MedUni patients who signed a project-specific informed consent statement will be used. A positive votum of the MedUni ethics committee was retrieved for the project.


  • R. Müller, G. Duftschmid: Semiautomatic Recruitment of Trial Patients Using ELGA Data: Conceptual Design and Implementation of an IT Tool, Stud Health Technol Inform; pp. 38-45.doi: 10.3233/SHTI210087 (Best student paper award at dHealth-2021); 2021
  • G. Augustinov, G. Duftschmid: Can the Austrian nation-wide EHR system support the recruitment of trial patients?, Studies in Health Technology and Informatics (Brönnimann’s Young Researcher Award at Conference “Healthcare of the Future”, 5. April 2019, Biel/Bienne, Switzerland); 259; pp. 87-91; 2019.

Project “QI-KA” – Cross-Sector Cardiologic Quality Indicators for the Austrian Health Care System

Funding: Main Association of Austrian Social Security Institutions

Duration: 2017–ongoing

Team members at MIM: Georg Duftschmid, Walter Gall (project leader)

Partners: Alexander Niessner, Patrick Sulzgruber (Division of Cardiology, Department of Internal Medicine II, Medical University of Vienna), Georg Heinze, Hana Sinkovec (Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna)

Background: The quality assessment of health care (structures, processes, results) becomes more and more important. For the judgement and the reporting the application of quality indicators is widespread in Austria (e. g., A-IQI) as well as in other countries (e. g., QSR in Germany). However, up to now data usually only hospital data has been used for this purpose.

Within the present project quality indicators will be developed which should connect the inpatient and the outpatient area. The results should provide information about whether patients are treated in compliance to existing practice guidelines and should serve as a basis for discussion in regional supply zones.

Goal: The aim of the project is to develop, evaluate and propose quality indicators considering the whole patient treatment in the inpatient and the outpatient area. The project aims at the medical discipline of cardiology and hereby focuses on heart attack as tracer (about 15,000 cases per year).

Methods:  As data source, claims data provided by the Social Security Institution from Lower Austria in form of the research database LEICON is used. The data cohort includes anonymized data of all persons who had a heart attack and received inpatient and outpatient care in Austria between 2012 and 2016.

Results: The project is ongoing.

Conclusions: The project is ongoing.

Project “ADE-PIM” – Adverse Drug Events in Relation to Inappropriate Medication in Geriatric Patients

Funding: Main Association of Austrian Social Security Institutions

Duration: 2017–ongoing

Team members at MIM: Walter Gall (project leader), Andrea Haberson, Christoph Rinner

Partners: Georg Heinze, Hana Sinkovec (Section of Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna), Safoura Sheikh Rezaei,  Michael Wolzt (Department of Clinical Pharmacology, Medical University of Vienna)

Background: With increasing age the number of diagnosed illnesses and thereby also the number of prescribed drugs increases. As a consequence also the number of adverse drug events (ADEs) increases in older population. In many countries lists of “potentially inappropriate medications” (PIM) are published to prevent ADEs and inform about drugs not suitable for elderly people.

Goal: The aim of the project is to analyse if the reason for drugs being listed in the PIM-lists is provable with evidence on the basis of Austrian claims data. For this purpose the drugs of the PIM-lists are analysed in relation to ADEs. Main research question are:

  • Is the risk of an ADE higher in PIM-drugs and therefore a justification to be listed as PIM?
  • For which substances a raised risk to be involved in ADEs can be observed in elderly people and are therefore possible candidates for the PIM list?

Methods: The European and the Austrian PIM-lists are analyzed.

The claims data is provided by the Main Association of Austrian Social Security Institutions in form of the research database GAP-DRG. The database includes anonymized data of all persons who received inpatient and outpatient care in Lower Austria between 2008 and 2011. Information about hospital diagnoses and the dispended medications are used in the project. 

To identify ADE-relevant diagnoses, the ADE-Diagnoses defined by Stausberg (GER) were adapted to fit the documentation habits in Austria.

Results: The project is ongoing.

Conclusions: The project is ongoing.

Project “RNFB-Tracing”– Individual Structure Function Map Using Nerve Fiber Tracing

Funding: Research Grant - FWF Austrian Science Fund (FWF Stand-Alone Project)

Duration: 2017–ongoing

Team members at MIM: Georg Fischer (project leader), Florian Schwarzhans

Partners: Clemens Vass (Department of Ophthalmology and Optometry, Medical University Vienna) Christoph Hitzenberger (Center for Medical Physics and Biomedical Engineering, Medical University Vienna)

Background: The general aim is to develop improved methods for early diagnosis of glaucoma. In glaucoma, the retinal nerve fiber bundles (RNFBs), which carry the electrical signals in response to a visual stimulus from the retinal photoreceptors to the brain, get damaged. Therefore, an analysis of the RNFBs can be used for early glaucoma diagnosis. One of the main obstacles for an accurate glaucoma diagnosis is the large interindividual variation of the trajectories of the RNFBs within the retinal nerve fiber layer (RNFL). We will tackle this problem by using polarization sensitive optical coherence tomography (PS-OCT) to trace the RNFBs, and use the measured traces to construct an individualized structure-function model (iSFM) describing the topographic association of structural (RNFL thickness and polarizing properties) and functional (visual field) deficits in early glaucoma.

The main hypotheses are that PS-OCT can be used for RNFB tracing in healthy and glaucoma; this will allow for construction of a high-resolution iSFM; which will improve diagnostic accuracy. Additionally we will investigate the suitability of PS-OCT for functional PS-OCT.

Methods: PS-OCT data will be acquired, using a prototype instrument, and obtain visual fields (VF) from 200 healthy volunteers and 100 glaucoma patients. Axis information contained in the PS-OCT data, together with local thickness variations will be used to trace the RNFBs. An individualized theoretical model of the RNFB trajectories will be developed describing the association of the RNFB trajectories with retinal and optic nerve head (ONH) biomarkers. This model will then be used as a prior to improve RNFB tracing in areas with thin RNFL. We will finally create an accurate iSFM with high topographic resolution and test its diagnostic accuracy in comparison with the standard parameters of VF and OCT.

Explanation indicating what is new and/or special about the project: We will be the first to develop an iSFM with high topographic resolution to enhance glaucoma diagnosis. We build on a pilot study from our group, which demonstrated the feasibility of RNFB tracing with PS-OCT in a few selected healthy volunteers. This project will overcome the difficulties of PS-OCT based RNFB tracing in diseased retinas, by integrating actual measurements with an individualized theoretical model of RNFB trajectories. The resulting high-resolution iSFM will improve the correlation between structural and functional defects and thus support early glaucoma diagnosis. We will be the first to explore functional PS-OCT measurements of the RNFL using the knowledge of the RNFB traces, which allows recording the PS-OCT signals exactly along the measured RNFBs. We will thus examine changes in light polarization by RNFBs as a function of visual stimuli along RNFBs. This might in future allow for objective VF testing.

Results: The project is ongoing.

Project “ibCGR” – The Relation between Retinal and Optic Nerve Head Parameters and Circumpapillary Retinal Nerve Fiber Layer Profile

Funding: Research Grant - Vienna Science and Technology Fund (Life Sciences Call 2011)

Duration: 2011–ongoing

Team members at MIM: Georg Fischer (project leader), Ivania Pereira, Florian Schwarzhans

Partners: Clemens Vass (Department of Ophthalmology and Optometry, Medical University Vienna)

Background: Measurement of retinal Nerve Fiber Layer (RNFL) is of major importance for early glaucoma diagnosis. However, its high variability in the normal population may lead to a wrong diagnosis. In order to determine an independent set of retinal and optic nerve head parameters that may be related with the distribution of RNFL thickness (RNFL profile), an automatic method of image processing will be developed. A multivariate model describing the relation between the retinal parameters and the RNFL profile will be developed and validated in an independent sample based on the output of the image processing measurement techniques. In the future, the validated model opens the perspective of compensation for the intersubject variability of RNFL measurements. This will benefit the early diagnosis of Glaucoma.

Methods: Retinal Nerve Fiber Layer (RNFL) is a retinal layer composed by the extension of cell axons of the retinal ganglion cells and has been a relevant concern in ophthalmic research in the past years, due to its diagnostic value for early glaucoma diagnosis. When measuring its thickness in the vicinity of the optic disc in healthy subjects, one can observe a large variability of values, which decreases sensitivity and specificity of RNFL measurements and its diagnostic value, leading sometimes to false conclusions in diagnosis. It is, then, of major relevance for clinical concerns, to understand the reasons of this intersubject variability and which factors may correlate with cell axon distribution. The relation between the major retinal vessel location and RNFL maxima location and the circumpapillary RNFL distribution (RNFL profile) has been proven, as well as the relation between optic disc size and RNFL thickness. It is now important to investigate whether other parameters may be correlated with the RNFL profile and whether this relation can be modeled in a multivariate way. We focus on parameters that are independent from the imaging device, but are representative of the physiologic characteristic of the population and present themselves a high variability in healthy population. These parameters comprise a circumpapillary distribution density function of retinal vessels, descriptors of size and shape of the optic disc and of the topographic relation between the fovea and the optic disc.

In order to obtain the referred parameters, algorithms for automatic feature detection are developed using intensity images from High Definition Optical Coherence Tomography from a sample of 120 healthy subjects. This process integrates image normalization and enhancement steps, based on contrast and luminance drifts, vessel extraction based on active contours theory and skeleton decomposition based on region growing and morphological operators. RNFL data will be parameterized and the obtained coefficients will be correlated with the retina and optic disc parameters. The significant parameters are selected for multiple regression analysis, in order to determine a model of the relation between the retinal and optic disc parameters and the parameterized RNFL profile. The model will then be validated in an independent sample of at least 120 healthy subjects.