The case view, a generic method of visualization of the case mix

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Abstract

Objective: There is a worldwide consensus to use the diagnosis related groups (DRG) to consider hospital activity. This tool leads to the production of tables with numbers (case mix), the interpretation of which is difficult. The issue is to translate these tables of numbers into an image named Case view. Method: It assimilates, in a way, each DRG to a “pixel”, the set of the DRGs being an image, the case view. The methods consist of two phases: the first one is to define the reference set while the second one is to visualize data through the reference set. The “DRG-pixels” which constitute the reference set should be organized according to three criteria: medical/surgical, nosological and economic. Results: This method is used to answer theoretical questions or to visualize activity at the level of a hospital or at the level of a department. It generates information of synthetic nature and ought to be used as a complement to existing methods. Discussion: An important advantage of this method compared to the existing ones (DRGs listing, DRGs clustering, histograms …) is that it presents data simultaneously at a global level (sets of DRGs) and at a local level (the DRG). Conclusion: We hope this method to be a supplementary step toward the creation of tools capable of eliciting the semantic content hidden in the medical data banks. The purpose of this paper is to explain the underlying general principles that define the graphic representation and to illustrate this model with the use of the French reference set.

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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