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Reading: From data to meaningful information for population health management

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Poster Abstracts

From data to meaningful information for population health management

Authors:

Sandra de Loos ,

Robuust, NL
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Evelien Heinrich,

Robuust, NL
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Yvonne Jansen

Robuust, NL
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Abstract

Background: The quality of the Dutch health care system is found to be one of the best systems in the world. At the same time, it is one of the most expensive systems. To create a more sustainable high-quality system, various attempts are made to find efficient strategies to stimulate population health. One of which is population health management PHM. The use of big data is essential to PHM for multiple reasons. First; in order to optimize health outcomes, one has to know which part of the population will benefit most from changes to be made. Population segmentation is needed and target populations should be defined. Second; data on health care processes and outcomes will reveal improvement potential. And third; data are indispensable to be able to monitor progress of the interventions on process and health outcomes. 

The Robuust foundation initiates alliances that originate in regions with local organisations health and social care and public health working together towards population health by PHM. Robuust facilitates them by offering programme management. These alliances use data to define target populations, for agenda setting in their change programmes, to optimise health care processes and to monitor outcomes.

Aims & objectives: Our aim is to provide insight in using data for creating meaningful information and value for collaborating organizations including patients in population health alliances. There are a several possibilities to gain insight in population characteristics. We share our experiences on making regional population intelligence using open-source data and data held by organizations and public health providers, primary care and hospitals. We also share our vision on monitoring and evaluation of the value creating process in different alliances.

There is no such thing as a blue print on how one can make the best use of different kinds of data for different purposes. For example; ways to define target populations vary by the extent of your population at the start. We know alliances targeting inhabitants of a neighborhood. But we are also operating in alliances targeting inhabitants of entire regions. Our poster illustrates  our experiences on different kinds of data are used and are able to visualize value gain for target populations.    

How to Cite: de Loos S, Heinrich E, Jansen Y. From data to meaningful information for population health management. International Journal of Integrated Care. 2018;18(s2):222. DOI: http://doi.org/10.5334/ijic.s2222
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Published on 23 Oct 2018.

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