Political decision makers on federal and federal state level are in need of detailed information about regional structures of health services in order to plan and provide optimum structures of health services. Health insurance companies and health care providers also rely on detailed information about regional structures of health services to suit the range of offered services to regional market conditions by means of precise knowledge about health care chains (key word: integrated health care) as well as patient potentials.
For a while now, one of the central contents of the health care debate is avoiding overtreatment and unmet medical needs. This information is vital for planning new health care chains, ambulatory health care centres, hospitals, units or medical specialists' practices in order to analyse regional market opportunities of a new or existing business venture in a scientifically sound manner including economic and disease-related information.
infas offers – on the basis of a tool for regional analysis by infas geodaten – an instrument to evaluate regional health services research allowing for analysis of the health services landscape via geo-coding by regional units. Thus, analysis can differentiate with respect to 16 Federal States, 440 districts, approx. 8,500 postal code areas, approx. 12,500 municipalities, or even on a small scale with respect to approx. 75,000 living quarters or more than 1.6 million street sections. Analysis also includes merging household-related (economic or sociodemographic) information as well as indication-related key figures.
Therefore, the following questions, amongst others, are feasible:
- Determining the density of health services. What distance does the average resident need to cover in which region in order to reach the nearest general practitioner/medical specialist/hospital? Are there differences in health services with regard to regionally different population structure (comparison of urban quarters e.g. by different levels of education or proportions of migrants)?
- Geographic differences in prevalences or expenses. How do certain disease patterns allocate to regions? Do regions provide sufficient offers of health services with regard to the population's prevalences or age pattern? How do treatment expenses differ in regions? Thus required: merging of e.g. (de-identified) health insurance information: patients with certain diseases – as measured by diagnostic data, drugs' data, and many other analyses.
- Potential analyses. How many residents/potential patients live in a health care provider's catchment area (e.g. hospital, ambulatory healthcare centre, old-age or nursing home)?