High-cost users generate extremely high costs when compared with average users in the same diagnostic-related group (DRG). They represent a major financial loss for a health service organization. The research was conducted using an area health service patient database for online analytical processing to produce descriptive statistics and graphs of 'high-cost' and 'non-high-cost users'. Trends and patterns were identified across key variables derived from clinical, financial and operational categories. The main results are: 20% of costs are spent by 3% of the population; elective admission is higher in the high-cost group; tracheostomy has the most number of cases and is the most expensive DRG; LOS is mostly longer for complex cases however, high costs can be attributed to other factors. In conclusion, these findings are potentially useful to patients, medical staff, management and health service decision-makers. The limitation of this study is the exclusion of profitability.

Nurses generate large quantities of data at different operational levels in a health service organization. Administrative managerial data include the number of nursing hours per patient day and cost data related to nursing services while clinical data include the documentation of direct patient care only. In this paper, we explain standard clinical data elements in the HIS (Hospital Information System). The construction of the data is traced from patients' medical records to coding procedures within ICD (International Classification of Disease) classification and DRG (Diagnostic Related Groups) of casemix. Examples are given from Australian data and definitions, but much of the same information can be found in hospital information systems throughout the world. Practical applications that demonstrate how patient data can be used for research and management purposes in nursing are given. Finally, future directions and issues related to the use of datasets for nursing research are explored.

This paper discusses a study conducted to identify factors that contributed to increased length of stay for two diagnosis related groups (DRGs) and their consequential impact on nursing salaries, The study shows that three separate clusters of cost drivers (DRG-related, nurse-related, and patient-related) Casemix fundingNursing services are the primary reason for admitting patients to hospital, and the provision of 24-hour care seven days per week means that nursing service costs are the largest solitary factor impacting on a hospital's expenditure. While it is possible to identify nursing expenditure as a proportion of the gross operating budget from the cost centre reports, according to Hathaway and Picone (1995) it is not possible to gain an accurate picture of patient expenditure due to lack of data on resource consumption. In spite of this, nurse executives and nurse unit managers have come under increasing pressure to account for spending.Reacting to the high costs associated with the provision of health care, the Victorian Government introduced casemix funding in the early 1990s. The new model provided funding to hospitals based on identifying diagnosis related groups (DRGs), which theoretically denote patients with similar levels of complexity, who consume similar resources and have a similar length of stay (LOS). Each DRG has its own cost weight and an inlier length of stay (a range of days the patient is expected to stay in hospital, related to the disease category) that incorporates high and low boundary points. When a patient's stay is within the range but above the state average number of days for a particular DRG, then the term high inlier is invoked and if the stay extends beyond the number of inlier days expected, the term high outlier is used (Johnson-Lutjens, 1991). The length of stay in the casemix model is important because of the relationship between funding for a particular DRG and the associated costs encountered during an inpatient stay. Understanding the factors involved with length of stay will help policymakers and managerial staff administer and control budgets more effectively.Identification of factors contributing to increased length of stay in two Diagnosis Related Groups

For the purposes of funding and policy development, the Victorian Department of Human Services expects Victorian health care institutions to capture patient data at all levels. These data can be extracted from hospital information systems and potentially offer a business role within a health service organisation. However, there are many issues to be addressed at the organisational level in order that operational directors can be enabled to use hospital data to solve health service operational problems. In this paper, we discuss some of those considerations and give practical examples of how patient data can be used for research and management purposes.