Objectives: The study identifies patient profile variables that explain variation in resource utilization and outcomes for home healthcare. Background: The healthcare reform and the demand for quality patient care have increased the need to identify key patient characteristics that can predict the use of resources and outcomes; however, home healthcare industry currently lacks adequate information collection to reflect these needs. This study explored both the resource use and care outcomes for nursing administrators in monitoring quality, resource distribution, and reimbursement policy decision making. Method: The conceptual framework is based on Donabedian's quality care elements (structure, process, and outcome) and Nursing Minimum Data Set. This is a retrospective descriptive study design in which 244 patient records and data were obtained from a home healthcare agency located in Washington, DC. A series of stepwise and discriminant analyses was conducted for data analysis. Results: The findings indicated that the total number of nursing diagnoses and two specific nursing diagnoses (alteration in mobility and knowledge deficit in IV therapy) were strong predictors of overall resource use. Prognosis proved to be the strongest predictor of discharge outcomes. Conclusions/implications: The results indicated that data related to nursing diagnoses and nursing interventions can provide valuable information in predicting resource use. Prognosis made by nursing judgment was also sensitive in predicting patient outcomes. These critical data elements should be included in describing home health patient characteristics and related resource utilization and care outcomes.