gms | German Medical Science

Introduction: In 1997 a regression tree based patient classification system for all inpatients in Austria was introduced. Since this time the so-called “Austrian DRG-system” is regularly updated.

Background: The classification rules are based on regression tree models which terminal nodes should be cost homogenous. However, the resultant regression tree often has to be adjusted manually to be medically reasonable. Despite the possibility of manually adapting the original tree, bootstrapping can be used to search systematically for alternatives.

Material / Methods: The data base are the Austrian DRG-data of previous years (approx. 2.500.500 admissions per year divided into approx. 900 groups according to the main diagnosis or procedures). The dependent variable is the length of stay of the cost of a patient.

Bootstrap-based methods and different model evaluation criteria are used for searching through a space of models.

Results:

The use of bootstrapping assisted in constructing a number of alternative trees which are at least as accurate as the tree constructed by the currently used semi-automatic procedure. In some of the datasets the bootstrap search for alternative models helped to overcome local minima and increased accuracy significantly.

Conclusion: Bootstrapped regression trees are a powerful tool to assist in the development of patient classification systems. They can improve accuracy and offer several models to choose from. Moreover, by the use of bootstrapping often less complex trees can be found. The principles of this tree selection strategy can of course be used for many other applications.