The case view, a generic method of visualization of the case mix
Introduction
It has been widely advocated to consider hospital activity using noso-economic tools. One of the recommended tools for this purpose is the diagnosis related groups (DRG) tool [1], [2]. In some countries, this tool is used for tariffing (for example in the USA [2]), in other countries it is used to control the budget of hospitals (for example, in France [3]). Whatever its use may be, this tool generates tables with numbers the interpretation of which is delicate if not daunting. To improve the tables’ presentation, a new method was developed, in which each diagnosis related group (DRG) is seen as a pixel, the set of all DRGs becoming a single image called “case view.” We describe the general principles that allow to define the reference set of this image and show the French reference set as an example.
Section snippets
Methods1
The method consists of two phases: the first one is to define the reference set while the second one is the use of this reference set to visualize various data.
Results
In the French example [4] the three principal criteria have played the following role (Fig. 3):
- a.
the medical/surgical criterion has allowed to split the reference set in two parts separated by an horizontal line, the superior part being medical;
- b.
the nosological criterion has allowed to define 10 columns corresponding to major categories of diagnosis or to major categories of diagnosis clustering;
- c.
the “cost” criterion has allowed, owing to the French scale (synthetic index of activity
Discussion
In the first place, the benefit of this method lies:
- a.
in the aesthetic side of the presentation which provides a very useful “communication tool”. For example this instrument allowed to improve the motivation of the coding physicians by providing “nice” results of their work;
- b.
in the synthetic nature of the information; indeed, this method allows to show information’s which are not visible in another way; for example it provided an immediate feed back allowing to detect problems in the quality of
Conclusion
Hospitals’ activity data represent a treasure of information. The problem is that this richness is, for a large part, only potential. Therefore, the optimal use of this information, the quality of which keeps improving, represents an important challenge for the future. We hope that the method proposed here would be a supplementary step towards the creation of more and more efficient methods capable of eliciting the semantic content hidden in the medical data banks.
Acknowledgements
We are grateful to the anonymous reviewers and to the editorial board for their useful comments; particularly, we wish to thank Dr Jan Talmon for his numerous helpful comments which allowed to improve the readability of this paper. We are grateful to Dr Emmanuel Rusch for critical reading of the manuscript. We are grateful to Dr Mira Pavlovic and to Mrs Béatrice Ollier for correction of the English version.
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