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With rising costs and increasing complexities, many hospitals seek to better understand the intricate details of their operations. Increasingly, these organizations havea strong desire to accurately predict the resources required to effectively treat their patientload. This research investigates patient length-of-stay in a hospital neurological unitusing an empirical modeling approach. Factors significantly affecting patient length ofstay were identified and used to construct a regression model. The predictive modelprovides hospital decision makers with a compact tool to input what-if scenarios andpredict future patient treatment lengths, thus, allowing the hospital to properly allocateresources.