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THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND  PRACTICE, V.71,  # 1, 2014,  pp. 127-139



               overall saving of 323,53 US$ per patient per year over all entities. The real cost saving without a


               QALY saving effect would be at a 111,33 US$ per year. Taking into account, that no cost for the

               telemedical measures were calculated and the service would include a 2-3 week phone call and a

               telemedical care system worth a monthly fee of 15 US$ production cost, it turns out, that the


               technology may not be able to save “real” cost in the system. A positive return would only be

               generated  over   the additional QALY effect. Overall the technology  could produce a QALY

               effect of 8889 QALYs per year in the AR.


                     With the tool and methodology itself a custom-tailored health economic feasibility study

               e.g. in terms of a specific population mix of the investigation and/or targeted outcome parameters


               (e.g.  saved cost at Provider, saved transportation  cost, etc.) was  produced.  Additionally the

               possibility of the implementation of a reinsurance model allows selective “risk adjustment” or a

               nearly complete  “buy  out”  of  the risk  of the  new technology over  an external reinsurance


               provider.  The designed reinsurance model is based exactly on the simulation model described in

               this work and calculates the premium on an individually selectable risk algorithm basing on the

               calculated underlying risks.


                     The whole simulation can only give a very rough overview on the potential savings and

               effects of the technology – a big problem to get precise effects from the simulation is the fact of

               the missing data: As there was only some cost data available and some epidemiology data was


               completely missing for the specific distribution of CHF, the value simulated must been seen as a

               “rough range” value. Positively speaking the tool and methodology can be used and integrated in

               the development cycle of a new AR care program – as soon there is more evidence available, the


               tool can simulate more precise results. New scientific evidence and market-related needs can be







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