Modeling efficiency at the process level: an examination of the care planning process in nursing homes.

Concern about the quality of care in nursing homes is widespread. One response has been to recommend increased staffing, even though this would entail significant costs (Institute of Medicine 1996, 2001). Despite good evidence linking nurse staffing to resident outcomes, the effects are not large enough or robust enough to convince skeptics (Weech-Maldonado et al. 2004). These mixed results may reflect a lack of standardization (Rantz et al. 2003). Relatively few studies of nursing homes have directly examined the work of nurses or aides, but variability appears to be the norm (Schweizer et al. 2005; Taunton et al. 2005). Adding staff to a poorly designed or poorly supported process may or may not improve outcomes, but it will certainly increase costs. Furthermore, ongoing substandard performance signals leadership problems, which adding staff does not address (Rummler and Brache 1995). Clearly nurses and aides feel that there is too little time to do their work. They must cut corners and break rules to get the work done (Bowers and Becker 1992). An open question, however, is whether all the work needs to be done. The case can be made that nursing processes are unnecessarily complex and entail work that does not add value for residents, such as fetching supplies or completing redundant documentation (Institute of Medicine 2004). Certainly the experience of hospitals suggests that costs can be reduced without harming quality (Nelson-Peterson and Leppa 2007). Whether nursing homes are efficient is important. If they are, higher quality requires higher costs. If nursing homes are inefficient, increasing quality need not increase costs (Crosby 1979). For example, in addition to being a signal of low quality, a high pressure ulcer rate means that the nursing home has to bear higher costs of treatment. Moreover, ineffective programs, which usually involve failures to plan, communicate, coordinate, or execute, need not cost less than effective programs (Rantz et al. 2004). Multiple studies have found evidence of technical inefficiency in nursing homes (excessive resources per standardized unit of output) or cost inefficiency (excessive cost per standardized unit of output). Many of these studies have used data envelopment analyses (DEA), a linear programming approach (Nyman and Bricker 1989; Sexton et al. 1989; Nyman, Bricker, and Link 1990; Kooreman 1994; Rosko et al. 1995; Chattopadhyay and Ray 1996; Bjorkgren, Hakkinen, and Linna 2001; Blank and Valdmanis 2005; Laine et al. 2005a). Other studies have used forms of regression analysis (McKay 1988, 1991; Dor 1989; Gertler and Waldman 1992; Vitaliano and Toren 1994; Blank and Eggink 2001; Chen and Shea 2002; Christensen 2004; Crivelli et al. 2002; Laine et al. 2005b, 2005c). Although all of these studies have found evidence consistent with considerable inefficiency, none have directly confronted concerns about untested modeling assumptions, about input measurement, or output measurement (Newhouse 1994). To see how challenging these questions are, consider only output measurement. Even for a nursing home that provides no short-term care, output standardization must address two domains. The first is case mix, meaning variations in the care that residents should get, given their needs. The second is quality, meaning how well the care provided matches what residents should get. Both domains are multidimensional, and few investigators have had the data to carry out case mix or quality adjustments. Recent analyses by Laine et al. (2005c) have reinforced these concerns by offering compelling evidence that efficiency estimates are vulnerable to unmeasured differences in case mix and quality. This paper seeks to analyze variations in nursing home efficiency while directly addressing concerns about measurement. It examines a single process with a standard output: the federally required care planning process. It uses state survey assessments of the care planning process to control for variations in quality. It uses primary data that were collected using a rigorous protocol to ensure that all relevant inputs have been identified. And it uses multiple techniques to analyze efficiency.