Quantifying low-value services by using routine data from Austrian primary care

Eur J Public Health. 2016 Dec;26(6):912-916. doi: 10.1093/eurpub/ckw080. Epub 2016 Jun 16.


Background: Open debates about the reduction of low-value services, unnecessary diagnostic tests and ineffective therapeutic procedures and initiatives like "Choosing Wisely "in the USA and Canada are still absent in Austria. The objectives of this study are: (i) to establish a list of ineffective or low-value services possibly provided in Austrian primary care, (ii) to explore how many of these services are quantifiable using routine data and (iii) to estimate the number of affected beneficiaries and avoidable costs arising from the provision of these services.

Methods: In May 2014, we identified low-value care services relevant for primary care in Austria. For our analysis we used routine data sets from the Austrian health insurance. All analysis refer to the insured population of the Lower Austrian Sickness Fund (n = 1 168 433) in the year 2013.

Results: (i) We found 453 low-value services possibly offered in Austrian primary care. (ii) Only 34 (7.5%) services were quantifiable using routine data. (iii) In the year 2013, these 34 services were provided to at least 246 131 beneficiaries and the estimated avoidable costs arising were at least 11.38 million Euros. This accounts for 1.2% of overall spending of the Lower Austrian Sickness Fund for drugs and services provided by primary care doctors in the year 2013.

Conclusion: The absence of a homogeneous, transparent and accessible coding system for diagnosis in Austrian primary care restrained our assessment. However, our study findings illustrate the potential utility and limitations of using claims-based measures to identify low-value care.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Age Factors
  • Austria
  • Humans
  • Medical Overuse / economics*
  • Medical Overuse / prevention & control*
  • Primary Health Care / statistics & numerical data*
  • Quality Indicators, Health Care / statistics & numerical data*
  • Sex Factors
  • Socioeconomic Factors