Austria Data Source

DIAG-Extranet is the most comprehensive data base on hospitals in Austria. It contains data routinely collected at social-insurance funded hospitals. DIAG-Extranet is an expert portal based on an agreement between the Länder and the federal level and was intended for their use only. The catalogue of outpatient care (KAL) will be integrated into DIAG making it an even more comprehensive dataset. Yet, so far outpatient services were collected and integrated in four provinces only as part of a pilot project.


The DIAG-Extranet is maintained by the Ministry of Health

  • ulrike.schermann-richter(at), phone: 00431711004163
  • manfred.pregartbauer(at), phone: 00431711004186

Data in registry is updated monthly; main findings and evaluations are not publicly available

Access to database Data is not accessible for researchers unless for very specific purposes. Requests to be made to Statistics Austria or the Ministry of Health (see contacts above)

DIAG-Extranet is based on data from MBDS (Minimum Basic Dataset). Monthly data is available on

  • each hospital and all its cost units (e.g. #beds, personnel, medical equipment, cost data)
  • anonymised patient level data per hospital stay (sex, age, place of residence, #days of hospitalisation)
  • medical data on hospital stay (major and additional diagnoses according to ICD-10 standards, treatments)
  • points awarded per hospital stay based on DRG system
  • DRG codes per hospital

Information on diagnoses since 1989, treatments since 1997, documentation of intensive care since 1998. All persons who have sought and received care in a social insurance funded hospital in Austria are covered.

Statistical representativeness: Not representative: Only includes hospital/in-patient data (There are plans to integrate out-patient care services, but this has not occurred to date).


Identification via date of birth or postal code is possible in principle. However, linkage requires substantial justification on behalf of researchers due to data protection issues (e.g. it is not allowed to have postal codes and dates of birth in the same data set so that individual hospitals or patients cannot be identified).

Linked to BIG (Buisness Intelligence in the Health Care System)

Data quality Knowledge on basis of documentation (diagnoses and services catalogue, statistical information), on changes in quality of documentation as well as hospital specific organisation is necessary to use database correctly. Each data item refers to one hospital stay. Consequently, whether one patient is hospitalized X times per quarter or whether X patients are hospitalized once cannot be easily distinguished. 
Strengths and weaknesses

The DIAG-Extranet is THE dataset used to run DEA or conduct other performance assessment for hospitals. However, access to the database is limited and usage is not outsider-friendly.

The Patient Satisfaction Survey 2010/2011 is the first Austrian wide survey on patients’ satisfaction. 99,000 questionnaires were distributed in 49 hospitals with a 22% return rate. In the near future routine surveys on patients’ satisfaction are foreseen for all Austrian hospitals.

Governance Health Austria Limited: sonja.gleichweit(at), reinhard.kern(at) (phone: 0043151561127)
Access to database
  • Aggregate results can be accessed by anyone, only in German
  • Individual level data is available to participant hospitals/care faciltites through an online evaluation tool (Online-Auswertungstool) that is not available to the public
  • Information on health status prior and after hospital stay,
  • Hospital admission, discharge and follow-up care,
  • Care during hospital stay,
  • Personnel,
  • Coordination between different health service providers.

21,780 persons completed the questionnaire (voluntary particpation; 99,000 questionnaires were distributed among patients in particpating care facilities) by patients staying in 49 participating in-patient care facilities for at least 2 nights

Linkage No linkage possible because no identifier queried 
Data quality This is not a representative survey.
Strengths and weaknesses

Strenghts: This is one of very few surveys focusing on interface management. Questions were worded such that data can be compared to surveys from Germany, England, the US and Switzerland. Captures patient reported health status prior to hospitalisation and potentially significant information about integration and coordination of care. Also benchmark analyses among all hospitals that participated is possible.

Weaknesses: Self-administered questionnaire of self-reported data not methodologically rigorous; Does not include outpatient services.


Austrian Health Interview Survey 2006/07

Governance Statistics Austria: jeannette.klimont(at) (phone: 00431711288277), survey renewed every 5 years (next probably 2014)
Access to database
  • Microdata for free upon request
  • Report in German only
  • Cross-sectional, regular (5 year intervals from next wave onwards)
  • Aggregate tables

Detailed data on

  • Physical and mental health status
  • Need for care and/or support
  • Health behaviour (alcohol/drug consumption, physical activity)
  • Frequency of medical consultations/check-ups
  • Demand for health services abroad
  • Waitlists for operations
  • Medication
  • Basic sociodemographic (age, gender, nationality) and socioeconiomic indicators (profession, income, education)

Gross sample from each 32 Care Service Region (Versorgungsregionen): 770; for the three Public Service Regions within Vienna: 933; Total gross sample size : 25,130; Actual sample size from each Region: 483, for Regions within Vienna: 488; Total actual sample size: 15,474.

Statistical respresentativness: The survey includes but slightly underrepresents people in old-age homes or residential care.

2006-2007 was the first year AHIS was undertaken as an independent initiative, with an entirely different survey design and concept, based on the Europan Health Information Survey (EHIS). In previous years (1973,1983,1991,1999), the survey was carried out as part of Austria's Microcensus.


Health interview surveys have been conducted since the 1970s. Because questionnaires and study design have changed, results from 2006/07 can only punctually be compared to those from previous years.

The survey design is based on Eurostat’s European Health Interview Survey (EHIS) and thus internationally comparable.

Data quality

This is a representative survey for the Austrian population aged 15 years and over (6.9 million people).

Entry errors: As data collection was computer-assisted, quality checking was undertaken while still in the data collection phase. Item-non-responses were imputed;
In order to balance out the distortions caused by missing data and data stratification, collected data was weighted.

Breaks: Interview manual used to ensure consistency of definitions and terminology; No breaks reported in data collection team or responsible institution.

Consistency of terminology: Interviewers required to complete training before going into the field to ensure familiarity and consistency in terminology and coding.

Strengths and weaknesses

Strengths: Underlying mechanisms for differences in health status and healthcare use be analysed as environmental indicators are assessed as well. Wider definition of health by considering not only physical but also mental health. Harmonised data collection rendering it comparable with other international sources. Inclusion of institutionalised population (albeit with caveats). Consistency of terminology and coding. Survey design harmonized with EHIS and data is internationally (among participating EU countries) comparable.

Weaknesses:  Most indicators on health status are self-assessed. No biomarkers and limited objective indicators on health. It is not possible to accompany individuals over time and thus understand mechanism behind changes in health condition (e.g. unemployment, loss of close family members). Under-representation of population in institutions may limit the robustness of data for older age groups.


Austrian National Cancer Registry reports on cancer incidence, prevalence and survival per calendar year. Mortality due to cancer is reported in the „Cause of death statistics“

  • Statistics Austria
  • Contact by email (nadine.zielonke(at) or phone 00431711287228
Access to database
  • Publications in German only
  • Some aggregate data and studies available online
  • For access to the raw database containing patient level data a formal request is necessary. Fees might apply.
  • Information necessary for linkage not available.
  • 1983 marked the first year data was of sufficient completeness and quality (cancer registry first established in 1969, but online registry provides data since 1983)
  • Data collected on: Personal information, hospitalization, information about tumor (type, localisation, histology, stage of tumor, diagnosis, treatment, anamnestic data, cancer suspected due to occupation
  • Four provinces also maintain a regional register (Carinthia, Salzburg, Tyrol and Vorarlberg)
  • Data collected from hospitals but not from the outpatient/ambulatory sector.
  • Cancer registration forms completed by all institutions obligated to report this information; cause of death statistics
  • Each year (wave), the sample includes all new cases of cancer in the Austrian population for that year: approx. 36,000-38,000 per year.

Statistical representativenss:

  • Patients treated exclusively in outpatient facilities are not included in the registry
  • In some states (Bundesländer), cancer incidence is underreported making intra-state comparisons unreliable
  • Austrian residents who seek care exclusively abroad are not included in the registry

Linked to mortality statistics (but only used for internal quality check).

There are no unique person identifiers, but data can be linked by other identifiers such as date of birth and names.

Data quality

In some Länder there is underreporting of cancer cases, hence regional comparisons are possible to a limited extent only.


Strengths and weaknesses

Because cancer patients treated in outpatient care are not reported, cancer cases in Austria are underestimated. Yet, the large majority of cancer patients are referred to hospitals which are covered in the data set


The Austrian Health Information System (ÖGIS) is a geographic information system. Database contains data from numerous and wide-ranging national registries and surveys; explicitly created to provide rapid regional epidemiological data analysis spanning the entire health system (especially to the Ministry of Health) for health reporting and health planning purposes.

  • Gerhard Fülöp (gerhard.fueloep(at), Phone: 0043151561163) or Anton Hlava (anton.hlava(at), both Health Austria Limited
  • Database is fed data from Statistik Austria, social insurance institutions, Federal Ministry of Health (BMG),Federal Ministry of Defence and Sports (BMLVS), several research institutions and other providers of data
  • Aggregate data tables available from REGIS (Regional Health Information System), a database that is part of the much larger ÖGIS, and makes public selected indicators at
  • Data continuously updated; latest report to the Ministry of Health available for 2012
Access to database
  • Access to microdata is not available to the public including researchers; can commission the analysis of specific questions, usually for a fee
  • Time between application and reception: usually within 2 weeks 
  • Aggregate data (according to data protection regulations in Austria)    
  • Standard formats (MS-Excel, SQL-Tables, JPEG for maps, etc.)  
  • Reports and aggregate data tables in German only
  • Data collection ongoing since 1993 (in addition REGIS created by 2000 as ÖGIS's internet platform accessible to the public)
  • Data collected on: life expectancy, mortality, cancer incidence, hospital stays, self-reported health) and on different health care sectors: acute care, rehabilitative care, old-age homes, outpatient care, mobile care.
  • Covers entire population of Austria (everyone who has been captured in the registries, censuses, and surveys of government and research institutions
  • Stratification: By age and sex with respect to the population 
  • There are five levels of regional data resolution: 2,300 municipalities (population ranging from 100 to 200.000 inhabitants); 121 districts (population ranging from 10.000 to 200.000 inhabitants); 35 NUTSIII-regions (NUTS-III-level of EUROSTAT, population ranging from 30.000 inhabitants to 1.5 million  inhabitants); 9 provinces (NUTS-II-level of EUROSTAT, population ranging from 200.000 to 1.5 million inhabitants); and the national level
  • Base used for sampling: Routine data from the health care system; national registries, censuses, and surveys of government and research institutions
  • Representativeness: Making mostly use of complete data sets, so representativeness mostly ensured.
Linkage Individual data sets cannot be linked within ÖGIS, but may be displayed according to, eg. postal codes
Data quality No entry errors because all data are pre-checked before integration into the ÖGIS database. Terminology is mostly according to international databases by WHO, OECD and EU. No breaks except HIS (not available for each year).
Strenghts and weaknesses

Strengths: Comprising almost all data sources on health and healthcare system in Austria (presentable via tables, graphs and maps)
Weaknesses: Data availability on the outpatient sector yet lacking in Austria.


BIG (Buisness Intelligence in the health care system), a health data pool hosted by the Main Association of Austrian Social Security institutions, containing prescription data, data on ambulatory services of sickness funds, electronic reimbursement codex, data of population register, data on people insured, hospital data from the Ministry of Health (BMG) on diagnoses, treatment and costs, statistical data on outpatient care.

Governance BIG (Buisness Intelligence in the health care system), is maintained by Main Association of Austrian Social Security institutions.
  • Alexander Ganjeizadeh-Rouhani: alexander.ganjeizadeh(at)
Access to database Data is not accessible for researchers unless for very specific purposes. Requests to be made to Main Association of Austrian Social Security institutions.
Coverage BIG is a health data information system that contains anonymized registry micro data from various sources, including:
  • Drug information: prescription data (monthly updated) and (electronic) reimbursement codex. Data can be broken down by DDD and ATC-code and by type of medical specialist and geographical region, by cost of drug, area of prescription, sickness fund, type and age of patient since 2000.
  • General information from sickness funds (financial and cost-performance statistics, number of entitled persons)
  • DIAG-Extranet data (inpatient data)
  • Out-patient data (Ärztekostenstatistik, ambulatory care providers, e-card consultations)
  • Population registry data (Statistics Austria)

Data includes all those covered by social insurance (approximately 98% of the population). Stratification possible for age, gender and district.

Linkage The data warehouse BIG matches data from listed data sources, i.e. matching of alternative drugs to each prescription thus allowing for calculations on cost reduction potentials. No matching on the level of persons.
Data quality Depending on quality of original data sources.
Strenghts and weaknesses

Strengths: Has the potential to match different data sources. Through this system health experts can find answers on complex and specific research questions regarding health care system performance. Prescription data is updated every month. Other data updated quarterly or once a year. Users may retrieve information through standardreports, ad-hoc queries directly from dataset or may obtain customized reports choosing relevant parameters.

Weaknesses: Use of the complex dataset on the ad-hoc level demands expert knowledge.


GAP-DRG (General Approach for Patient oriented Outpatient-based DRG) is a research data base with reimbursement data for outpatient services of sickness funds (social insurance) and Federal Ministry of Health (hospital data).


Nina Pfeffer/Main Association of the Austrian Social Insurance Institutions (Hauptverband der österreichischen Sozialversicherungsträger)


Source data is collected annually, but this research database only contains data for 2006-2007; Main findings published in 2011; The Main Association of the Austrian Social Insurance Institutions (Hauptverband der österreichischen Sozialversicherungsträger) makes the R & SQL programming codes available for replication of matching procedure.

Access to database Access subject to request and prior approval. Data in German.

Anonymised registry microdata. Covers all ages and all 9 regions. Database was assembled with data for years 2006-2007: All persons who received services in 2006/07 are included in data. Data includes all those covered by social insurance (approximately 98% of the population).

Data collected on: demand for health care services, i.e. episodes of care (outpatient and inpatient); sickness leaves. It also contains data on prescriptions, diagnoses (from prescriptions: ATC->ICD), hospitals, diagnoses (main, additional), way through the hospital, DRG details. Does not cover ambulatory care in hospitals. Conditions: chronic obstructive pulmonary disease, diabetes, cancer, coronary health disease, mental health. Socio-economic variables: Age, gender and district.

Linkage It is possible to link to other national databases through a Unique Person Identifier (UPI)
Data quality Database is composed of several regional and occupational sickness funds. Hospital data was added and linked through a complex algorithm. About 99% of observations in all databases were matched. Data includes also seasonal workers and tourist who used health care services, but the inclusion of these observations does not impact quality of data, given the total number of observations (approximately 98% of population)
Strenghts and weaknesses

Strengths: Coverage of the entire population; possibility to link the data through Unique Person Identifier; reliable data on actual health care services usage.

Weaknesses: Possibility to identify episodes of health care use, but not really pathways of care (date of episode is not recorded exactly); lack of data on diagnosis; limited socio-economic variables; limited coverage of outpatient care (ambulatory care in hospital settings is not covered). Expertise is needed to use complex dataset.

The Cause of Death Statistics (Todesursachenstatistik) is a registry of physician-completed forms maintained by Statistics Austria.


Maintained by Statitiscs Austria:

  • Anita Mikulasek: anita.mikulasek(a)
  • Barbara Leitner: barbara.leitner(a)
Access to database

Microdata for a 20% sample of any given year's total cause of death statistics is made available annually

Anonymized microdata in English.

  • Data collected since 1970 (42 years)
  • All deaths registered in Austria in each year; Approximately 76,479 in 2011
  • Disaggregated data is available by year, by residential district, by gender, age, marital status, age of surviving partner, cause of death, country at birth, nationality at death, religion, place of death.
  • Data collected on: Cause of death according to 78 categories; sociodemographic information about the deceased is collected, as well as marital status
Linkage Linked to registry of births and marriages, and Cancer statistics (Krebstatistik)
Data quality

Entry errors: No errors reported. Plausibilty checks are conducted annually before final results are released; Coders receive training annually in an effort to reduce the number of entry errors; In cases of missing data, phone calls are placed to the person who filled out the form.

Consistency of terminology: As of 2009, persons of Austrian origin residing abroad at time of death are not captured in this data set; Coders receive training annually and are updated on new codes and new terminology as needed to ensure a high level of consistency.

Strengths and weaknesses

Strengths: Future participation in cross-national HEDIC study (Health Expenditure by Diseases and Conditions, which will involve the linkage of Cause of Death statistics with other, yet to-be-determined data sets); linkage to multiple other national registries.  

Weaknesses: Data accuracy and completeness depends on physician completing it and local registry submitting it.