An examination of technical efficiency, quality, and patient safety in acute care nursing units.

The perfect storm of dramatic cost pressures, increasing public awareness of quality problems, and concerns arising from a significant nursing shortage have escalated hospitals’ attention to improving the efficient management of costly resources. With nursing unit costs comprising a substantial portion of hospital operating expenses, improving the efficiency of nursing care delivery has become a critical strategic goal. Yet evaluations of the efficiency of nursing units have rarely been reported. Hospital managers typically examine profitability, services offered, and patient populations served. The information is then compared with resources consumed to evaluate organizational efficiency. This analysis tends to take place at the hospital level, which ignores the fact that nursing units are significant resource consumers within hospitals. In addition, care provided to patients on different nursing units is sufficiently distinct that aggregation of cost and expense data to the hospital level ignores a rich source of data at the nursing unit level – data that, were it systematically collected and investigated, could provide valuable information regarding potential approaches to improve efficiency. Economists typically view efficiency as either technical efficiency or allocative efficiency. A firm is technically efficient when it produces the maximum output from a given amount of input, or alternatively, produces a given output with minimum input quantities (Hollingsworth, 2008). A firm is allocatively efficient when the input mix minimizes cost, given input prices, or alternatively, when the output mix maximizes revenue, given output prices. Taken together, technical and allocative efficiency comprise overall efficiency; when a firm is overall efficient, it operates on its cost or revenue frontier (Hollingsworth, 2008). Traditional hospital efficiency measures have utilized selected ratios in an attempt to understand the relationship between a single output and a single input variable. Unfortunately, the unidimensional nature of this method makes it impossible to consider additional variables or possible environmental influences that may affect efficiency. A more comprehensive approach can be found in regression analysis, because it can accommodate multiple inputs and outputs. However, regression techniques examine “average” practice, when what would be of more substantive interest would be to examine “best” practice. Neither the ratio method nor regression analysis can identify specific sources of inefficiency, nor can either method accommodate the multiple outputs that characterize hospital production. Further, the inability of either ratio methods or regression analysis to identify specific sources of inefficiency leaves a significant gap in the capacity of managers to improve the performance of areas for which they are responsible (McGlynn, 2008; Ozcan, 2008). Recent developments in management engineering and econometrics have produced techniques that do accommodate the use of multiple inputs and multiple outputs, provide information on sources of inefficiency, and identify best practice work units, thus providing actionable information for managers. In particular, the technique of Data Envelopment Analysis (DEA) is one that has been used to study hospital efficiency. DEA is designed to measure relative efficiency in situations where there are multiple inputs and outputs and there is no obvious objective way of aggregating either inputs or outputs into a meaningful index of productive efficiency (Ozcan, 2008). This method determines a “best practice” frontier that is built empirically from observed inputs and outputs. Each relevant organizational unit, called a “decision-making unit” (DMU), is then compared with its peers. The optimally functioning DMU achieves an efficiency score of 1. DMUs not on the frontier attain efficiency scores between 0 and 1. DEA has been used extensively in non-health care settings. Since 1983, it has gained popularity as a useful tool for measuring productivity in healthcare settings (Clement, Valdmanis, Bazzoli et al., 2008; Hollingsworth, 2008; Ferrier, Rosko & Valdmanis, 2006; Ozcan, 2008). A recent evidence report on health care efficiency measures completed by the Agency for Healthcare Research and Quality (McGlynn, 2008) cited only 3 studies examining efficiency in nursing, none of which used DEA. However, we found 2 older studies that did use DEA, one of which (Nunamaker, 1983) examined the efficiency of nursing departments, the other (Juras and Brooks, 1993) reported on efficiency at the nursing unit level. In that study, Juras and Brooks found that 32% of the 41 nursing units were relatively inefficient (efficiency scores ranged from .51 to .86), that the use of rotating shifts was negatively associated with efficiency, and the use of acuity data to prepare the staff budget was positively associated with efficiency. Although DEA has increasingly been used in hospital efficiency analyses, it has only rarely incorporated quality or patient safety variables. In fact, the AHRQ evidence report calls quality measures in efficiency reports “silent” (McGlynn, 2008, p. iv), and recommends that studies of efficiency also contain quality measures. Some newer studies have incorporated quality or patient safety variables as outputs. For example, Clement et al. (2008) found that lower technical efficiency was associated with poorer risk-adjusted quality outcomes, measured with the AHRQ’s Inpatient Quality Indicators (IQIs). And Nayar and Ozcan (2008) found that technically efficient hospitals were performing well with regard to several process quality measures for patients hospitalized with pneumonia. Examining 1,377 urban hospitals, Valdmanis, Rosko and Mutter (2008) found that in low-quality hospitals quality could be improved by increasing labor inputs, in contrast to high quality hospitals, which tended to use too much labor. These recent studies provide examples of the ways in which quality and patient safety variables can be integrated into DEA for a more comprehensive examination of both efficiency and quality of care. The purpose of our study was to assess the technical efficiency of general medical, general surgical, and combined medical-surgical nursing units, and to incorporate into the DEA model several relevant indicators of quality and patient safety.