Routine data

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In the healthcare system, routine data (also secondary data ) is standardized information that is collected primarily for billing purposes with the service providers .

Data description

The routine data consists of continuously collected primary data or process data as well as administrative data from the areas of outpatient care, drug data, remedies and care. The SHI routine data includes the insured person's master data (e.g. gender, date of birth, highest school leaving certificate, insurance status), diagnostic data (ICD diagnoses) and inpatient stays in hospitals.

The special thing about these data is that the entire SHI population is included, there is no selection and no drop-out. In this way, there are no biases caused by memories, interviewers or observation, as can be the case with other surveys. The long observation period enables both cross-sectional and longitudinal studies. In Germany around 85% of the population have statutory health insurance.

Elevation

In 2002 the research project Quality Assurance with Routine Data was started in order to examine possibilities of quality reporting on the basis of routine data from the health insurance companies.

In the course of the Data Transparency Ordinance, the data is merged and held at the German Institute for Medical Documentation and Information (DIMDI). It receives selected data from the Federal Social Security Office on outpatient and inpatient treatment for those with statutory health insurance as well as on drug prescriptions. These are already transmitted to the BVA in encrypted form for the risk structure compensation (RSA).

use

Routine data are of interest for health services research , epidemiological questions and analyzes of the quality of care. They are routinely collected by the statutory health insurance (GKV), pension and accident insurance (social data) or in (population-based) disease registers.

In 2017, Lars G. Hemkens, Despina G. Contopoulos-Ioannidis, and John PA Ioannidis received the David Sackett Prize for research on routine data.

Individual evidence

  1. ↑ Goffrier, B., Schulz, M., & Bätzing-Feigenbaum, J. (2017). Administrative prevalence and incidence of diabetes mellitus from 2009 to 2015. Central institute for statutory health care in Germany. P. 4.
  2. ↑ Heidemann, C., & Scheidt-Nave, C. (2017). Prevalence, incidence and mortality of diabetes mellitus in adults in Germany - inventory for diabetes surveillance. P. 122.
  3. ↑ Data Transparency Ordinance: More data on health services research - Article in Ärzteblatt, Krüger-Brand, Heike E., 2012

Web links

literature

  • Swart, E. (Ed.). (2005). Routine data in health care: Handbook for secondary data analysis: Basics, methods and perspectives. Huber.
  • Gaebel, W., Spießl, H., & Becker, T. (Eds.). (2009). Routine data in psychiatry: cross-sector health services research and quality assurance. Springer Science & Business Media.
  • Trittin, C. (Ed.). (2015). Healthcare research between routine data, quality assurance and patient orientation. Asgard Publishing House.