Registries in Austria and their usage for healthcare improvement
Project leaders: Christoph Strohmaier
Project team: Christoph Strohmaier, Julia Kern
Duration: Mid-April to Mid-November 2023
Language: German (with English summary)
Background: Assessing and generating reliable healthcare-related data is a cornerstone for governing and planning the healthcare system. Randomised controlled trials (RCTs) are the gold standard for proving causal evidence for the relative efficacy and safety of pharmaceuticals or other healthcare technologies [1-4]. In recent years, there has been an emerging trend to utilise so-called real-world data (RWD)  from registries to complement the use of RCT data and to improve healthcare quality and guarantee patient safety.
By now, many countries maintain different registries to answer healthcare-related questions or make evidence-based healthcare decisions. For some medical indications, disease groups or interventions, registries are even required by law. In Germany, the German Federal Joint Committee (GBA) can demand data collection alongside clinical use for certain pharmaceuticals . In Austria a wide variety of medical registers exists. Examples are quality registries for quality improvement of healthcare services (heart, stroke, in-vitro-fertilization), registries for collecting patient data alongside clinical use for cost-intensive therapies for the sake of generating further evidence, registries for monitoring of epidemiological data (Austrian oncology registry, Tyrolean oncology registry, oncology registry of Vorarlberg) and registries that focus on technological interventions (pacemaker registries, ICD-registries, loop recorder registries) . By now, there are also general efforts to establish the use of registry data for benefit assessments of pharmaceuticals, especially for early benefit assessments or for early health technology assessments [5, 6]. An overview or a database of all available registries, a so-called registry of registries (RoR), does currently not exist .
Filling evidence gaps due to the lack of clinical trials in some healthcare areas with data from registries is discussed controversially in the scientific community [10-12]. Generally, data from registries for healthcare planning, improvement and benefit assessments must fulfil the same quality criteria as the data from clinical studies. The validity and reliability of registry data depend not only on data quality or its potential for bias but also on further criteria like clearly formulated aims, unambiguous inclusion and exclusion criteria, sustainable financing, or data management aspects. Fulfilling these criteria is the only way to ensure that valid data are available to answer questions relevant to healthcare and decision-making. Although different guidelines and instruments to assess the quality of registries and registry data exist [13-16], they are neither mandatory nor widely accepted. Instead, the quality of registries is often assessed with internal checklists . As a result, registries may vary in their rigour in implementing quality standards, leading to some registries being able to provide better information for decision-making than others. In addition, the joint usage of large amounts of data (big data) from different registries for governing the healthcare system becomes increasingly important.
Furthermore, connecting data from different registries is often impossible due to lacking technical quality and data privacy standards. Therefore, joint and efficient data usage to improve healthcare can only be achieved through common quality standards. If registries fulfil standardised quality requirements, a further benefit could be achieved from connecting and sharing data from different registries: data economy and the valid assessment of healthcare-relevant questions across disease classes and indications.
Project goals: The project aims to give an overview of the existing registries in the Austrian healthcare landscape. The main focus will be on registries that can potentially be used to improve healthcare and are healthcare-relevant. Healthcare-relevant registries are registries that specifically include patients with a particular disease or from a specific disease class (disease-related registries), registries that deal with the effectiveness of one or multiple interventions (intervention-specific registries), or registries that focus on product or patient safety . We will further give an overview of identified registries in the form of registry profiles and their characteristics regarding their current use. Characteristics include registry-specific aspects like registry type (intended use), registry aims, and operational time; aspects regarding data management, including data protection and quality assurance and aspects concerned with the technical implementation. In addition, the prevailing circumstances and scope of the use of the registry data for answering healthcare-related questions will be outlined.
A further goal is to identify Austrian registries that meet a minimum quality standard and can serve as best-practice examples for existing and prospective registries. We will use available guidelines and criteria catalogues that assess registry quality and contrast collected characteristics and data from prioritised registries. In the final step, based on the compiled findings, we will derive needs for further improvements and outline how registries could be used jointly beyond their intended purpose through unified quality standards. This information shall inform decision-makers, registry operators and researchers about the numerous fundamental quality standards for an efficient register operation and the potential of common data usage for healthcare improvement. In the report, ambiguous terms like real-world data, big data and real-world evidence, which are central to an understanding of registries, will be discussed.
- does not address research- or documentation registries.
- does not address registries or data, which play a role in regulation or admission processes.
- does not address registries that do not contain medical- or health-science-related data for research or the regulation of health systems, e.g. registries for the storage of (resident) registration data (e.g. registries for contact-tracing of infectious diseases).
- does not systematically assess the relative efficacy of registries or their effectiveness on the healthcare systems.
- neither designs an independent Austrian RoR nor gives specific instructions for implementing one.
The following research questions (RQ) will be answered in the course of the report:
RQ1: What implemented healthcare-relevant registries exist in Austria, what are the purposes, and what aims do they pursue?
RQ2: What are the registry-specific characteristics of the identified registries and to what extent and for which purpose do decision-makers and healthcare planners currently use them for the improvement of healthcare?
RQ3: In what areas do Austrian registries meet the quality standards of international guidelines and criteria catalogues for assessing registry quality?
RQ4: What can be inferred from the gained information about the joint usage of registry data, and which areas need further improvements?
RQ1: Identification and classification of existing registries in Austria
- Identification of registries 1: Hand search in metasearch engines and search on websites of public healthcare institutions with the help of already available listings according to the PICo-scheme
- Identification of registries 2: Consultations of experts with a focus on quality and safety in healthcare
RQ2: Overview and mapping of the Austrian registry landscape:
- Tabulation of identified registries, collection of baseline data, and descriptive analysis
- Contacting prioritised registries and collection of predefined data and characteristics concerning the current usage and prevailing circumstances of registries using a quality criteria catalogue: EUnetHTA Register Evaluation and Quality Standards Tool (REQueST) [14, 15]
- Tabulation of the collected data of prioritised registries and descriptive analysis
RQ3: Comparison of the collected data and results of RQ2 with identified quality standards and analytical development of implications regarding the fulfilment of quality standards
RQ4: Formulation of conclusions from the obtained information and deduction of development needs:
- Narrative synthesis of the results and their meaning concerning their improvement of healthcare
All medical indications, disease groups, medical products, pharmaceuticals, or health-relevant interventions; guidelines and criteria catalogues in the context of healthcare-relevant registers
RQ1 and RQ2: The report deals with medical registries that (potentially) contribute to the improvement of healthcare, their characteristics and their purpose regarding their current usage. The focus is on the following registries:
RQ3: To what extent do Austrian registries fulfil quality standards from international guidelines and criteria catalogues for the assessment of registry quality
RQ4: Status quo of Austrian registries, their meaning and need for further development for their usage to improve healthcare
Not interests: Other types of registries (Study, documentation or volunteer registries, registries relating to regulation or authorisation processes), systematic assessment of the (relative) efficacy of registries or their effectiveness on the healthcare system; the conception of an independent Austrian registry of registries
Austrian healthcare context and registry landscape
All types of publications
The workflow is designed after the principle of double inspection (CS, JK) and the results will be evaluated by internal and external reviewers.
 European Network for Health Technology Assessment (EUnetHTA). Methodology Guidelines. 2022 [cited 13/04/2022]. Available from: https://www.eunethta.eu/methodology-guidelines/.
 Odgaard-Jensen J., Vist G. E., Timmer A., Kunz R., Akl E. A., Schünemann H., et al. Randomisation to protect against selection bias in healthcare trials. Cochrane Database of Systematic Reviews. 2011;2015(4). DOI: 10.1002/14651858.mr000012.pub3.
 Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG). Allgemeine Methoden. Entwurf für Version 7.0 vom 06.12.2022. 2022 [cited 17/04/2023]. Available from: https://www.iqwig.de/methoden/allgemeine-methoden_entwurf-fuer-version-7.pdf.
 Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed: Chichester (UK): John Wiley & Sons; 2019.
 Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG). Konzepte zur Generierung versorgungsnaher Daten und deren Auswertung zum Zwecke der Nutzenbewertung von Arzneimitteln nach § 35a SGB V. 2020 [cited 17/04/2023]. Available from: https://www.iqwig.de/download/a19-43_versorgungsnahe-daten-zum-zwecke-der-nutzenbewertung_rapid-report_v1-1.pdf.
 Makady A., Ham R. t., de Boer A., Hillege H., Klungel O. and Goettsch W. Policies for Use of Real-World Data in Health Technology Assessment (HTA): A Comparative Study of Six HTA Agencies. Value in Health. 2017;20(4):520-532. DOI: https://doi.org/10.1016/j.jval.2016.12.003.
 Niemeyer A., Kluge S., Gurisch C., Hoffmann W., Kostuj T., Olbrich K., et al. Positionspapier des Deutschen Netzwerk Versorgungsforschung (DNVF) zur anwendungsbegleitenden Datenerhebung nach Sozialgesetzbuch V. Gesundheitswesen. 2021;83(04):309-313. DOI: 10.1055/a-1391-3908.
 Degelsegger?Márquez A., Gruböck A. and Fidon I. K. Gesundheitsdaten in Österreich – ein Überblick. Gesundheit Österreich (GÖG), Wien: 2022 [cited 19/04/2023]. Available from: https://jasmin.goeg.at/2409/2/Gesundheitsdaten%20in%20%C3%96sterreich_bf.pdf.
 Niemeyer A., Semler S. C., Veit C., Hoffmann W., van den Berg N., Röhrig R., et al. Gutachten zur Weiterentwicklung medizinischer Register zur Verbesserung der Dateneinspeisung und -anschlussfähigkeit Gesundheitswesen. 2021;83(04):309-313. DOI: 10.1055/a-1391-3908.
 Stausberg J., Maier B., Bestehorn K., Gothe H., Groene O., Jacke C., et al. Memorandum Register für die Versorgungsforschung: Update 2019. Gesundheitswesen. 2020;82(03):e39-e66. Epub 2020/02/18. DOI: 10.1055/a-1083-6417.
 Antes G. Ist das Zeitalter der Kausalität vorbei? Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen. 2016;112:S16-S22. DOI: https://doi.org/10.1016/j.zefq.2016.04.007.
 Windeler J., Lauterberg J., Wieseler B. and Sauerland S. Patientenregister für die Nutzenbewertung: Kein Ersatz für randomisierte Studien. Dtsch Arztebl International. 2017;114(16):A-783.
 Mandeville K. L., Valentic M., Ivankovic D., Pristas I., Long J. and Patrick H. E. Quality Assurance of Registries for Health Technology Assessment. International Journal of Technology Assessment in Health Care. 2018;34(4):360-367. DOI: https://doi.org/10.1017/S0266462318000478.
 Guilhaume C. A tool to assess the registries quality: The Registry Evaluation and Quality Standards Tool (REQueST). European Journal of Public Health. 2021;31(Supplement_3). DOI: 10.1093/eurpub/ckab164.573.
 Allen A., Patrick H., Ruof J., Buchberger B., Varela-Lema L., Kirschner J., et al. Development and Pilot Test of the Registry Evaluation and Quality Standards Tool: An Information Technology–Based Tool to Support and Review Registries. Value in Health. 2022;25(8):1390-1398. DOI: https://doi.org/10.1016/j.jval.2021.12.018.
 Klimek P., Baltic D., Brunner M., Degelsegger-Marquez A., Garhöfer G., Gouya-Lechner G., et al. Quality Criteria for Real-world Data in Pharmaceutical Research and Health Care Decision-making: Austrian Expert Consensus. JMIR Med Inform. 2022;10(6):e34204. Epub 20220617. DOI: 10.2196/34204.
 “Versorgungsnahe Daten” (VeDa) or “routinemäßig erhobene Daten” are more widespread terms in the German speaking area . Both terms translate to “routine practice data”.
 Pure research registers are registries that are specifically set up for studies without the purpose of improving healthcare. In this report, documentation registries are understood as registries that collect diagnostic or laboratory data, medical visual material or physician’s letters and only have the purpose of documentation (e.g., individual documentation registries in clinics or radiology centres).
 A PICo-scheme is a standard scheme from qualitative evidence synthesis that structures an analysis into problems (P), interests (I) and contexts (Co).