Length of stay (LOS) is the most widely used indicator of hospital cost of stay and LOS means, percentiles, and other statistics are essential for the pricing of hospital procedures, hospital budgeting, and reimbursement. Since extremely long or unusually short stays are often observed, advanced robust statistical techniques (not affected by the presence of atypical data) are vitally needed to assist in the computation of reliable LOS statistics.
In this project we will propose and develop a family of new robust estimates of negative binomial regression, which has often been recommended in the literature as a convenient model for LOS. We will focus on the weighted likelihood approach which provides highly robust and efficient estimates.
The application of these methods could help provide more reliable LOS statistics and identify misclassification problems. This is an essential process for the efficient application of patient classification systems in hospital management both in Switzerland and internationally. In addition, this project will include a new (currently lacking) specific product for the analysis of asymmetric count distributions into the easy-to-use statistical environment R in public domain. This tool will therefore be made available to a broad community of researchers in different fields, both those with extensive statistical expertise and those with a relatively limited statistical background.