title: [Economic Evaluation of Integrated Care Systems - Scientific Standard Specifications, Challenges, Best Practice Model].
abstract: OBJECTIVE
  Transparency of economic performance of integrated care systems (IV) is a basic requirement for the acceptance and further development of integrated care. Diverse evaluation methods are used but are seldom openly discussed because of the proprietary nature of the different business models. The aim of this article is to develop a generic model for measuring economic performance of IV interventions.
  
  
  METHODS
  A catalogue of five quality criteria is used to discuss different evaluation methods -(uncontrolled before-after-studies, control group-based approaches, regression models). On this -basis a best practice model is proposed.
  
  
  RESULTS
  A regression model based on the German morbidity-based risk structure equalisation scheme (MorbiRSA) has some benefits in comparison to the other methods mentioned. In particular it requires less resources to be implemented and offers advantages concerning the relia-bility and the transparency of the method (=important for acceptance). Also validity is sound. Although RCTs and - also to a lesser -extent - complex difference-in-difference matching approaches can lead to a higher validity of the results, their feasibility in real life settings is limited due to economic and practical reasons. That is why central criticisms of a MorbiRSA-based model were addressed, adaptions proposed and incorporated in a best practice model: Population-oriented morbidity adjusted margin improvement model (P-DBV(MRSA)).
  
  
  CONCLUSIONS
  The P-DBV(MRSA) approach may be used as a standardised best practice model for the economic evaluation of IV. Parallel to the proposed approach for measuring economic performance a balanced, quality-oriented performance measurement system should be introduced. This should prevent incentivising IV-players to undertake short-term cost cutting at the expense of quality.
authors.affiliation: Controlling & Health Data Analytics, OptiMedis AG, Hamburg.
authors.forename: A
authors.initials: A
authors.lastname: Pimperl
authors.affiliation: Lehrstuhl für Management im Gesundheitswesen, Fachbereich Betriebswirtschaftslehre, Fakultät für Wirtschafts- und Sozialwissenschaften, Universität Hamburg, Hamburg.
authors.forename: J
authors.initials: J
authors.lastname: Schreyögg
authors.affiliation: Zentrum für Sozialpolitik, Universität Bremen, Bremen.
authors.forename: H
authors.initials: H
authors.lastname: Rothgang
authors.affiliation: Management im Gesundheitswesen, TU Berlin, Berlin.
authors.forename: R
authors.initials: R
authors.lastname: Busse
authors.affiliation: Zentrum für Sozialpolitik Unicom-Gebäude, Universität Bremen, Bremen.
authors.forename: G
authors.initials: G
authors.lastname: Glaeske
authors.affiliation: Vorstand, OptiMedis AG, Hamburg.
authors.forename: H
authors.initials: H
authors.lastname: Hildebrandt
mesh_terms: D019985:Benchmarking / Q000191:economics / Q000458:organization & administration*; D003362:Cost-Benefit Analysis / Q000191:economics / Q000592:standards; D019033:Delivery of Health Care, Integrated / Q000191:economics* / Q000592:standards*; D005858:Germany; D018803:Models, Economic; D017063:Outcome Assessment, Health Care / Q000191:economics / Q000592:standards*; D017410:Practice Guidelines as Topic / Q000592:standards*; D012586:Science / Q000592:standards