Previous studies have shown the feasibility of using activity-based costing (ABC) in hospital environments. However, many of these studies discuss the general applications of ABC in health-care organizations. This research explores the potential application of ABC to the nuclear medicine unit (NMU) at a teaching hospital. The finding indicates that the current cost averages 236.11 US dollars for all procedures, which is quite different from the costs computed by using ABC. The difference is most significant with positron emission tomography scan, 463 US dollars (an increase of 96%), as well as bone scan and thyroid scan, 114 US dollars (a decrease of 52%). The result of ABC analysis demonstrates that the operational time (machine time and direct labour time) and the cost of drugs have the most influence on cost per procedure. Clearly, to reduce the cost per procedure for the NMU, the reduction in operational time and cost of drugs should be analysed. The result also indicates that ABC can be used to improve resource allocation and management. It can be an important aid in making management decisions, particularly for improving pricing practices by making costing more accurate. It also facilitates the identification of underutilized resources and related costs, leading to cost reduction. The ABC system will also help hospitals control costs, improve the quality and efficiency of the care they provide, and manage their resources better.

This research examines how the patients' characteristics and clinical indicators affect length of stay for the top five Diagnosis-Related-Groups (DRGs) for Medicare patients at a teaching hospital in the United States. The top DRGs were selected on the basis of volume per year. Teaching hospitals in the United States devote a significant amount of their resources to research and teaching, while providing treatment for patients. The ability to predict length of stay can substantially improve a teaching hospital's capacity utilization, while ensuring that resources are available to meet the health care needs of the Medicare population. Multiple regression models are developed to predict the length of stay using the patients' characteristics and clinical indicators as independent variables. The results indicate that approximately 60 percent (R(2)) of the variance in the length of stay is explained by the patients' characteristics and clinical indicators for these DRGs. The Mortality and Severity indices are found to be the strongest predictors for length of stay in all DRGs. Other patients' characteristics and clinical indicators such as age, gender, race/ethnicity, marital status, admission type and admission source are also significant predictors for some DRGs. In addition, most of these variables affect the length of stay in the same manner as shown in previous studies, even though the previous studies do not have the DRG specificity of this study.