OBJECTIVES: Analyzing the cancer incidence and TNM – classification is done by national statistic in high spatial resolution, but no detailed data regarding pre-existing illnesses and treatment pathways are gathered. That is why these problems are focused on using billing data from extramural and intramural anonymised patients datasets extended by drug prescription information. METHODS: Starting with anonymized single person spatio-temporal hospital data including diagnoses coded by ICD10, medical attendance data and patient identity key a pre-selection is realized. In the next step the intramural patient history is focused on, detecting the first indicated hospitalization. Afterwards criteria for the number of reuptakes as well as for exclusion of cases (filtering not new diseases) are defined based on the intramural patient history. Analyzing cancer indicated drug administration and drug prescription the year before the first hospitalization, knowledge about risk groups is collected and evaluated. Additionally the probability of surviving regarding different treatment courses is measurable. These calculations are done exemplary. RESULTS: Comparing the incidences calculated out of casemix datasets for liver cancer, lung cancer and mamma carcinoma high accordance comparing to cancer registry of Austria is observed. In case of liver cancer the overall deviation is 14 cases per year; equal to a difference of 1.5 percent. In case of mamma carcinoma 4882 detected new infections in control year 2007 are faced with 4833 new cancer diseases registered by national statistics. CONCLUSIONS: Using detailed single person spatio-longitudinal billing datasets in combination with extended search strategies using exclusion criteria based on expert knowledge as well as data structure information and modeling skills, highly reliable datasets are edited. The analyzed background knowledge can be used in modern dynamical simulation models producing reliable results.