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Analysis of Large and Complex Data pp 17–30Cite as

Business Intelligence in the Context of Integrated Care Systems (ICS): Experiences from the ICS “Gesundes Kinzigtal” in Germany

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Abstract

Patients generate various data with every contact to the health care system. In integrated care systems (ICS) these fragmented patient data sets of the various health care players can be connected. Business intelligence (BI) technologies are seen as valuable tools to gain insights and value from these huge volumes of data. However so far there are just sparse experiences about BI used in the integrated care (IC) context. Therefore the aim of this article is to describe how a BI solution can be implemented practically in an ICS and what challenges have to be met. By the example of a BI best practice model—the ICS Gesundes Kinzigtal—it will be shown that data from various data sources can be linked in a Data Warehouse, prepared, enriched and used for management support via a BI front-end: starting with the project preparation and development via the ongoing project management up to a final evaluation. Benefits for patients, care providers, the ICS management company and health insurers will be characterised as well as the most crucial lessons learned specified.

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Notes

  1. 1.

    This article follows the theoretical model of Ackoff (1989) as cited in Riley and Delic (2010) to define data, information and knowledge and bring them in a hierarchical connection. However, there are various perspectives on this field. For an in-depth discussion, see, e.g., Zins (2007).

  2. 2.

    In terms of data protection law this actually concerns semi-anonymous data, since there is no way for OptiMedis and GK to resolve the unique pseudonyms related to insured persons. The pseudonyms are generated by the SHIs, and are not disclosed.

  3. 3.

    In the Gesundes Gewicht program, for example, the following are recorded: weight, girth, blood pressure, fasting blood glucose, standard blood glucose, etc.

  4. 4.

    For more information see OptiMedis AG (2014).

  5. 5.

    OLAP is short for On-Line Analytical Processing and is often used as a synonym for multidimensional data analyses. More detailed explanations can be found in Azevedo et al. (2009).

  6. 6.

    Cf. also similar approach in Gröbner (2007).

  7. 7.

    For further details see Pimperl et al. (2013, 2014a).

  8. 8.

    Cf. also book contribution in German on this topic from Pimperl et al. (2014a).

  9. 9.

    Reduction in complexity with simultaneous density of information, e.g. graphic table, sparklines, notation etc. (Bissantz 2010; Gerths and Hichert 2011).

  10. 10.

    For further details see Reime et al. (2014).

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Author information

Authors and Affiliations

  1. OptiMedis AG, Borsteler Chaussee 53, 22453, Hamburg, Germany

    Alexander Pimperl

  2. OptiMedis AG, Hamburg, Germany

    Timo Schulte

  3. OptiMedis AG, Hamburg, Germany

    Helmut Hildebrandt

Authors
  1. Alexander Pimperl
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  2. Timo Schulte
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  3. Helmut Hildebrandt
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Correspondence to Alexander Pimperl .

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Editors and Affiliations

  1. Jacobs University Bremen , Bremen, Germany

    Adalbert F.X. Wilhelm

  2. Universität Ulm, Institute of Medical Systems Biology Universität Ulm, Ulm, Baden-Württemberg, Germany

    Hans A. Kestler

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Pimperl, A., Schulte, T., Hildebrandt, H. (2016). Business Intelligence in the Context of Integrated Care Systems (ICS): Experiences from the ICS “Gesundes Kinzigtal” in Germany. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1_2

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