Crucially, the study then adjusts for possible co-linearity of different cost drivers by the use of the sparse group lasso technique. An essential step is dimension reduction using Principal Component Analysis to pre-process the data by separating out the low value ‘noise’ from otherwise valuable information. This study identifies prime cost drivers in a typical, mid-sized Australian hospital and classifies them as sources of cost variability that are either warranted or not warranted-and therefore contributing to waste. Accurate identification of the main hospital cost drivers and relating them quantitatively to the observed cost variability is a necessary step towards identifying and reducing waste. The large cost variability observed between hospitals and also between patients in the same hospital has fueled the belief that consumption of a significant portion of this funding may result in no clinical benefit to patients, thus representing waste. Public hospital spending consumes a large share of government expenditure in many countries. The associated source code of the software described in the manuscript, the documentation for running and installing the software, and a test dataset with associated control parameter setting has been deposited in the Open Science Framework ( ) with the project title, “Identification of hospital cost drivers using sparse group lasso.” Phone: +617 3646 5490 Email: Chair: Dr Gordon McGurk Coordinator: Ann-Maree Gordon. Data requests may be sent to: Human Research Ethics Office James Mayne Building Butterfield Street ROYAL BRISBANE HOSPITAL HERSTON QLD 4029. As part of the approval granted by the Royal Brisbane & Women’s Hospital Human Research Ethics Committee for this study, data is non-disclosable, except for aggregated summary statistics that would not lead to breaches in terms of patient confidentiality. Data contain patient-level information that is therefore potentially identifiable.

GUID: 59D47937-A6EF-423D-BD4D-9A2E2CA31840 Data Availability Statementĭata have limitations on disclosure as a results of relevant privacy legislation.

Example Of Cost Pools And Cost Drivers