Contextual correlates of rural health clinics' efficiency: analysis of nurse practitioners' contributions.
The following research questions are posed:
1. Does RHC productivity vary depending on NP staffing?
2. To what degree do NPs con tribute to productivity?
3. What are characteristics of highly productive RHCs?
The first nurse practitioners were educated at the University of Colorado in 1965. Their numbers have grown in response to an increased recognition of the profession, and a demand for a less costly alternative to physician services. In 2000, there were 58,000 employed NPs (Hooker & Berline, 2002). Within less than 10 years (by 2009) their numbers more than doubled to about 125,000 practicing NPs throughout the United States (American Academy of Nurse Practitioners [AANP], 2009a). Approximately 85% of NPs practice in primary care (Hooker & Berline, 2002), although they serve in a range of health care organizations including rural and urban clinics, urgent care sites, private physician and NP practices, nursing homes, hospitals, colleges, and health departments.
Nurse practitioners are highly educated; the entry-level preparation is a master's level and many have doctoral degrees. They are licensed to perform many of the same services provided by physicians such as ordering, performing, and interpreting diagnostic tests; diagnosing and treating acute and chronic conditions; and developing treatment plans. The NP's role differs from that of the physician, however, in terms of its emphasis on educational aspects of care delivery. According to the AANP, among the care priorities of NPs are patient and family education, facilitation of patient participation in self-care, and health promotion.
Approximately 20% of NPs are employed in rural or frontier areas today (AANP, 2009b). According to a recent survey of 2,268 NPs, the employment of NPs in RHCs increased by almost 20% over the past 6-year period, whereas the presence of NPs in other settings, such as private physician practices and long-term care facilities, is declining (AANP, 2009b). In keeping with one of the goals of the Rural Health Clinics Act (which established the RHC program in 1977), RHCs promote the use of nurse practitioners and physician assistants (PAs) for services to Medicare and Medicaid patients even in the absence of a full-time physician (U.S. Department of Health and Human Services, 2006).
Productivity, as applied to health administration, is a generic term to reflect the volume of patient visits a health care provider (e.g., NP, PA, physician) delivers per working day, whereas efficiency constitutes three distinct but related components: technical efficiency (the optimal ratio of weighed outputs versus inputs), process efficiency (the professional staff mix, reflecting the number of mid-level providers relative to physicians), and cost efficiency (the inverse ratio of cost per visit). When a productivity measure is used in addition to such factors as time, complexity, and number of clients seen within a particular time period, it refers to a broad perspective of efficiency (Wan, 2002).
Research related to NP productivity is not abundant. Among studies on the subject are those describing measures of NP productivity. Other studies make comparisons of NP productivity to that of other mid-level providers, or based on practice setting or rural and urban location of practice. The following is a review of some of the existing literature.
Measures of NP productivity. Several studies of NP productivity have applied quantitative measures based on units of encounters (or visits), time, and money. Examples of measures based on encounters are the number of direct patient encounters per day (Mendenhall, Repicky, & Neville, 1980); and the number of visits or consults provided (Cascardo, 2003). Measures based on time have included time spent in direct patient contact per day, time spent per direct patient encounter, and time spent in telephone patient encounters per day (Mendenhall et al., 1980), office hours, and time devoted to productive activities (Cascardo, 2003). Finally, examples of measures based on money are gross income generated per day, gross income generated per direct patient encounter (Mendenhall et al., 1980), gross charges, total net medical revenue, total cost, and dollars made per day (Cascardo, 2003). Holmes, Livingston, Bassett, and Mills (1977) used relative value points and equivalent dollar values to compare productivity of physician-nurse and physician-nurse clinician (physician extenders) clinical teams.
Rhoads, Ferguson, and Langford's (2003) "9 Critical Measures of NP Productivity" includes measures to assess NP productivity over time, rather than cross-sectionally For example, instead of tracking patient visits, Rhoads and colleagues (2003) suggest determining whether patient visits have been consistent or have decreased, and whether new patients have been attracted over time. They introduce measures of "processing" patients such as the number of and reasons for no-shows, average wait time per patient, percentage of cancellations among scheduled patients, and whether cancellations are increasing.
Others (e.g., Curtin, 1995; Martin, 2005) suggest that measures of the quality of patient-provider interaction should be incorporated into an assessment of NP productivity Martin (2005) describes successful provider practices as those built on the three As: availability (of the provider to patients), affability (characteristics of the provider that make her/him accessible to the patient, such as openness and approachability), and ability (technical ability of the provider and her/his staff). Finally, Curtin (1995) advocated measuring productivity in terms of a combination of outcome, process, and resource use measures. She maintains outcome measures such as achieving full age-related functional competence should be considered alongside process measures, such as staffing patterns and staff skills.
NP productivity. Previous research describes NP productivity relative to other mid-level providers, by type of practice, and by rural versus urban practice location. The findings of a 1976 study of health clinics revealed that medex (individuals trained to perform basic diagnostic and therapeutic services) consistently saw approximately 14% more patients per hour than did NPs (Reiss & Lawrence, 1975). Another source found that PAs saw approximately 50% more patients than did NPs ("Executive Summary," 1976).
Mendenhall et al. (1980) compared the mean productivity of NPs and PAs based on several types of practice settings: solo, partnership, single-specialty group, multiple-specialty group, clinic, and other. Among these practice settings, NPs in clinics had the lowest number of direct patient encounters per day. This finding must be considered on balance, however, with the period of time the NP spent with the patient. The mean number of minutes per direct patient encounter was highest for NPs in clinics relative to that of the other practice settings.
Although several studies describe the roles and activities of NPs in rural settings (e.g., Lawler & Valand, 1988; Reid, Eberle, Gonzales, Quenk, & Oseasohn, 1975), few examine the productivity of NPs in rural practices using quantitative analysis. In their comprehensive study of utilization and productivity of NP and PA services, Mendenhall et al. (1980) assessed NP productivity in rural compared to urban practices. They observed lower levels of productivity in rural settings based on all indicators with the exception of the mean direct patient encounters per day (which was higher by 0.1 only).
Contextual factors affecting NP productivity. The ratio of physicians to population influences productivity of NP and PAs. Several researchers (e.g., Hadley, 1974; Reinhardt, 1975) suggest physicians in communities with high physician to population ratios do not use support personnel in an efficient way because they are not incented to maintain high productivity. Thus, when constructing a model to measure productivity of NPs in clinics, it would seem important to incorporate physicians per thousand population as a control variable. Similarly, the contextual factors such as health disparities, poverty level and other demographic characteristics, median wage, and regional location should be considered simultaneously when investigating the difference in RHC practices. In addition, the variability in RHCs' organizational structure with respect to size, the proportion of NP staffing, ownership, and the classification of operation (provider-based vs. independent) should be examined.
This empirical study on the contextual correlates of RHCs' efficiency is not to render a comprehensive study of NP productivity in RHCs. For lack of observational data from time-motion studies of NPs or other providers' performance in rural settings, we do not include such measures as those for "processing" patients, patient-NP interaction, or income generated. However, we rely on the administrative data sets available for a study panel of 3,565 RHCs continuously in operation during the years 2006-2007. These RHCs are located in 45 states throughout the United States.
Data sources. There were two principal sources of study data: the Medicare Cost Report and the Area Resource File or ARF System (Bureau of Health Professions, 2007). Additional sources included the CMS Online Survey (2007) and Certification Reporting System (OSCAR), the Bureau of Labor Statistics, the Department of Labor, and the Bureau of Census databases. Although the Medicare Cost Report and the OSCAR system provided limited data for analysis, they are the most complete sources of data on RHCs at the present time. All secondary data were collected for the period 2006-2007.
The study universe. A total of 3,565 RHCs was available for the investigation. Careful inspection of the data revealed a large number of RHCs reported no NP visits in the Medicare Cost Report. After excluding these from the study population, along with other clinics with missing data on a variety of measures, 1,067 clinics remained in the sample. A comparison of major structural features of RHCs in the study sample compared to those in the study universe showed minimal differences (see Table 1). Thus, the study sample can generate relatively valid information about the efficiency and productivity of RHCs.
Measures and analytical approach. Three efficiency indicators for 2007 are considered in the research: (a) the technical efficiency score as derived from data envelopment analysis (using numbers of physicians, NPs, and PAs as input variables; and number of patient visits for each of these three professionals as output variables); (b) process efficiency, as measured by the percentage of NP FTEs among the professional medical staff; and (c) cost efficiency, as measured by the inverse ratio of costs per visit. (FTE is calculated as the number of patient care hours worked as a proportion of a total of 2,080 workday hours per year. Inverse cost per visit was used in place of cost per visit because we were testing to see if the number of NP FTEs [and other organizational and community factors] was inversely related to cost.) In addition, for each RHC several organizational variables (e.g., size, for-profit/not-for-profit status), and community variables (e.g., infant mortality rate, region) were included.
The causal relationship between NP FTEs (and the other organizational and community variables) and the three efficiency indicators was examined by applying path analysis using AMOS 7 for structural equation modeling (SPSS, 2008). Path analysis is an appropriate method for analyzing causal relationships among a set of variables, and the interrelationships between the variables (Wan, 2002). Only the most parsimonious and best fitting model is presented in the study results.
Descriptive analysis. Table 2 presents the results of the descriptive analysis. Half of the study RHCs were for-profit and half were not-for-profit. Slightly more than half (51%) were provider-based; half (49%) were independent. Approximately 20% of the study clinics were located in the South. The mean clinic size (total number of professional FTEs) was 2.95; however, the clinics ranged in size from 0.03 to 33.9 FTEs. The mean number of NP FTEs per clinic (the primary organizational factor of interest) was 1.06 for 2006. NP FTEs ranged from 0 to 14.
The mean for each of the three efficiency indicators was: technical efficiency score (reflecting the relationship of inputs to outputs): 0.39; process efficiency (percentage of NP FTEs among the professional staff): 49.23%; and cost efficiency (inverse ratio of costs per visit): 0.009.
The interrelationship of the contextual variables. Table 3 presents the statistically significant intercorrelations among the contextual variables. This analysis shows although the number of NP FTEs is positively related to clinic size, NP FTEs are negatively related to provider-based clinics.
The interrelationship of the three efficiency indicators. The initial correlation analysis of efficiency indicators shows they are positively related. Further causal specifications were made to consider both process efficiency and technical efficiency of RHCs as contributing factors to the variation in cost efficiency in path analysis (see Figure 1).
Predictors of efficiency. The statistically significant predictors for each efficiency indicator are presented in Table 4 and illustrated in Figure 1. Technical efficiency is explained by six predictor variables for 13.4% of the total variance: NP FTEs, professional staff size, provider-based RHCs, for profit status, poverty level, and infant mortality rate. Process efficiency is explained by four predictor variables for 30% of the total variance: NP FTEs, professional staff size, South, and wage. Finally, cost efficiency is explained by five predictor variables accounting for 34.1% of the total variance: technical efficiency, process efficiency, provider-based RHCs, poverty level, and percentage of females in the county.
[FIGURE 1 OMITTED]
The overall goodness of fit statistics (see Table 4) for the proposed model (see Figure 1) show this path model has an excellent fit to the data. That is, the validity or usefulness of the statistical model is confirmed by structural equation modeling. Thus, the analysis reveals NP FTEs (along with other organizational and community factors) contribute positively to technical and process efficiency, but not to cost efficiency.
Implications and Limitations
Implications. Several findings can be summarized. First, the number of NP FTEs is an important positive contributing factor to the variability in technical efficiency and process efficiency, but does not influence cost efficiency. An additional finding regarding process efficiency is clinics with a large number of professional staff also tend to use a higher number of NPs than do small clinics. Second, clinic size is positively associated with technical efficiency. That is, the larger the clinic's professional staff size, the higher the technical efficiency or productivity observed among RHCs. Third, when comparing the two classifications of RHCs, provider-based clinics are less productive than independent clinics. However, provider-based clinics have higher cost efficiency than do independent clinics. Finally, RHCs' cost efficiency is positively related to technical efficiency but negatively associated with process efficiency.
Our findings offer mixed support for previous research on NP productivity. Contrary to some previous research, we found a positive relationship between NP FTEs and productivity. Our measure of technical efficiency included multiple inputs and outputs simultaneously, rather than individual measures, such as number of direct patient encounters per day or gross income generated per day. When analyzed in this manner, clinics with a higher number of NP FTEs had a positive, though relatively small, effect on productivity.
Another structural or organizational characteristic--size--had a higher relative impact on technical efficiency than NP FTEs. This suggests larger clinics are more efficient and better able to accommodate higher patient volumes, a finding consistent with previous literature (e.g., Sinay, 2001). When considering contextual or environmental characteristics, poverty emerged as an influential factor. The percentage of the population in poverty was positively and significantly related to technical efficiency. This finding is not supported by studies on efficiency in other primary care facilities such as community health centers (Rosenbaum, Shin, Markus, & Damell, 2000). However, it may be RHCs of the more impoverished counties have achieved higher levels of efficiency to accommodate the greater demand (patient volume) related to poverty-related conditions such as chronic illness. High productivity levels in these locations may also be indicative of professional staff working extensive hours because of inadequate coverage to meet these demands.
The positive relationship between technical efficiency and cost efficiency is consistent with previous research. In their longitudinal study of community health centers (CHCs), Marathe, Wan, Zhang, and Sherin (2007) found changes in CHC technical efficiency positively affected changes in CHC cost efficiency. Likewise, earlier studies of hospital performance also showed positive relationships between technical efficiency and lower costs (Ehreth, 1994; Valdmanis, 1990).
Thus, in practical terms, it appears those RHCs able to maximize use of practitioner resources (NPs, PAs, and physicians) are also those most cost effective in providing health services. In addition to the organizational contributors to efficiency identified in this study, other characteristics such as organizational culture and managerial systems and techniques may come into play. Further analysis and modeling of the more technical and cost-effective clinics is needed to include these and other qualitative aspects of performance.
Limitations. As stated previously, this study makes no attempt to analyze NP productivity in a comprehensive way. Ideally, measures of NP productivity would encompass all NP responsibilities within the RHC context and account for activities not easily measured by objective means. Data on patient acuity and complexity of services delivered were not available. Thus, it was not possible to perform risk adjustment for RHCs' performance indicators in this study.
Conclusions and Future Research
In this study, factors influencing efficiency in RHCs with a particular focus on the contribution of NPs were examined. A higher level of NP FTEs positively influenced technical and process efficiency, but not cost efficiency. The study contributes to the broad understanding of the factors that affect the variability in performance of RHCs.
RHC administrators must continually monitor and improve the daily operations of their respective clinics. Today's RHCs face financial and operational challenges, many of which are related to the complexities of serving high percentages of elderly and impoverished persons. These conditions make it imperative RHCs maximize their efficiency to remain financially viable. The study results suggest employment of higher proportions of NPs in RHCs has a positive bearing on productivity, although the cost per visit is not related to NP FTEs. That is, these more productive RHCs are able to treat greater numbers of patients without adding professional staff. Although smaller clinics may not be in a position to consider adjusting proportions of professional staff, these findings are important considerations for larger clinics and for all clinics in their planning for the future. Highly efficient RHCs may enhance their ability to increase employee compensation, and, consequently, improve recruitment and decrease turnover. The more efficient RHCs will improve their ability to remain financially viable for the long term.
We suggest several additional aspects be incorporated in future studies of the NP contribution to RHC efficiency. First, future analysis would include risk adjustment factors to assess the effects of patient demographics and socioeconomic conditions. In addition, longitudinal analysis involving several years of data are needed to confirm the findings of this study.
Examination of RHC effectiveness in achieving optimal health outcomes as related to the NP contribution would complement the study of RHC efficiency. The utilization of prevention services and the rate of hospitalizations for ambulatory care sensitive conditions might serve as indicators. In addition, the collection and analysis of observational data would provide a more complete understanding of the NP contribution to RHC performance. Case studies of highly efficient and effective RHCs would include an examination of the role and activities of their NPs, and how they compare to those of the PAs and physicians. Time and motion studies would examine the proportion of time devoted to the various activities within the purview of the NP, such as conducting patient and family education, in addition to obtaining medical histories and ordering and interpreting diagnostic tests.
A comprehensive study of RHC performance would incorporate these and other assessments of the NP contribution. A practical outcome of such a study would be the development of a decision-support system to assist RHC management in achieving optimal clinic performance. This software should be designed to facilitate RHC management in making the best use of their limited staffing and financial resources. By maximizing operational efficiency and effectiveness, RHCs would be better positioned for sustained viability and continued provision of services to meet the needs of rural populations.
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JUDITH ORTIZ, PhD, MBA, is a Research Associate, University of Central Florida, Orlando, FL.
THOMAS T.H. WAN, PhD, is a Professor and Director, Doctoral Program in Public Affairs, University of Central Florida, Orlando, FL.
NATTHANI MEEMON, MA, is a Graduate Research Assistant and Graduate Teaching Assistant, University of Central Florida, Orlando, FL.
SEUNG CHUN PAEK, MS, is a Graduate Research Assistant, University of Central Florida, Orlando, FL.
ABIY AGIRO, MHS, is a Graduate Research Assistant, University of Central Florida, Orlando, FL.
NOTE: The analysis for this paper was supported by the Targeted Rural Health Research Grant Program, Office of Rural Health Policy, HRSA. The opinions and statements made in this article reflect the views of the authors only and do not represent those of the funding agency.
Table 1. Comparison of the Study Sample to Non-Sample of 3,565 Rural Health Clinics Non- Characteristics Sample Sample Total 1,067 2,498 Percentage of all 29.9% 70.1% existing RHCs, 2006 Average number of 8.99 8.73 years Medicare certified Provider-based 42% 42% Professional staff 2.48 2.50 size Regional location Midwest 37.0% 39.4% Northeast 2.8% 3.3% South 43.9% 39.7% West 16.3% 17.5% Total 100% 100% Table 2. Descriptive Statistics of the Study Variables in 1,067 Rural Health Clinics Study Variables Minimum Maximum Mean NP FTE 0 14 1.06 For-profit 0 3 0.50 Provider-based 0 1 0.51 Size 0.03 33.89 2.9515 % Poverty 4.8 45.7 17.637 Infant mortality rate 0.0000 0.0005 0.0001 Wage $96,587 $259,560 $124,357.23 % Families female head 3.7 43.7 15.808 South 0.00 1.00 0.1968 Process efficiency 0.25 100.00 49.2335 Technical efficiency 0.0277 1.0000 0.3912 Cost efficiency 0.0000 0.0251 0.0090 Standard Study Variables Deviation NP FTE 1.030 For-profit 0.517 Provider-based 0.500 Size 3.31314 % Poverty 6.4684 Infant mortality rate 0.0001 Wage $21,495.262 % Families female head 6.2087 South 0.3978 Process efficiency 30.4833 Technical efficiency 0.19795 Cost efficiency 0.0035 Table 3. Statistically Significant Intercorrelations Among the Contextual Variables Intercorrelation Estimate S.E. C.R. Poverty [left and Infant 0.000 0.000 12.653 right arrow] Mortality Rate Wage [left and South 1,157.748 217.493 5.323 right arrow] Poverty [left and South 0.591 0.063 9.384 right arrow] Provider- [left and South 0.095 0.006 14.685 based right arrow] South [left and Size -0.107 0.030 -3.580 right arrow] Provider- [left and Size -0.147 0.046 -3.201 based right arrow] NP FTE [left and Size 1.840 0.114 16.149 right arrow] Provider- [left and NP FTE -0.034 0.014 -2.443 based right arrow] Female [left and Wage 52.962 12.801 4.137 right arrow] Intercorrelation r p Poverty [left and Infant 0.404 *** right arrow] Mortality Rate Wage [left and South 0.137 *** right arrow] Poverty [left and South 0.237 *** right arrow] Provider- [left and South 0.480 *** based right arrow] South [left and Size -0.087 *** right arrow] Provider- [left and Size -0.094 0.001 based right arrow] NP FTE [left and Size 0.570 *** right arrow] Provider- [left and NP FTE -0.065 0.015 based right arrow] Female [left and Wage 0.126 *** right arrow] *** p < 0.001 Table 4. Regression of Rural Health Clinics' Efficiency on the Contextual Variables (n = 1,067) Efficiency Indicator Predictor [beta] S.E Technical efficiency [left arrow] NP FTE 0.043 0.007 Technical efficiency [left arrow] Size 2006 0.005 0.002 Technical efficiency [left arrow] Provider- -0.047 0.011 based Technical efficiency [left arrow] For-profit 0.03 0.011 Technical efficiency [left arrow] Poverty 0.004 0.001 Technical efficiency [left arrow] Infant 238.403 92.265 mortality rate R-square = 0.134 Process efficiency [left arrow] NP FTE 13.547 0.923 Process efficiency [left arrow] Size -5.946 0.307 Process efficiency [left arrow] South 9.801 2.005 Process efficiency [left arrow] Wage 0 0 R-square = 0.300 Cost efficiency [left arrow] Technical 0.005 0 efficiency Cost efficiency [left arrow] Process 0 0 efficiency Cost efficiency [left arrow] Provider- -0.003 0 based Cost efficiency [left arrow] Poverty 0 0 Cost efficiency [left arrow] Female 0.01 0.004 R-square = 0.341 Efficiency Indicator Predictor b C.R. Technical efficiency [left arrow] NP FTE 0.082 6.514 Technical efficiency [left arrow] Size 2006 0.228 2.338 Technical efficiency [left arrow] Provider- -0.119 -4.151 based Technical efficiency [left arrow] For-profit 0.08 2.77 Technical efficiency [left arrow] Poverty 0.118 3.8 Technical efficiency [left arrow] Infant 0.081 2.584 mortality rate R-square = 0.134 Process efficiency [left arrow] NP FTE 0.463 14.677 Process efficiency [left arrow] Size -0.61 -19.381 Process efficiency [left arrow] South 0.127 4.888 Process efficiency [left arrow] Wage 0.062 2.406 R-square = 0.300 Cost efficiency [left arrow] Technical 0.298 11.732 efficiency Cost efficiency [left arrow] Process -0.053 -2.124 efficiency Cost efficiency [left arrow] Provider- -0.433 -17.178 based Cost efficiency [left arrow] Poverty 0.097 3.852 Cost efficiency [left arrow] Female 0.055 2.23 R-square = 0.341 Efficiency Indicator Predictor p Technical efficiency [left arrow] NP FTE *** Technical efficiency [left arrow] Size 2006 0.019 Technical efficiency [left arrow] Provider- *** based Technical efficiency [left arrow] For-profit 0.006 Technical efficiency [left arrow] Poverty *** Technical efficiency [left arrow] Infant 0.01 mortality rate R-square = 0.134 Process efficiency [left arrow] NP FTE Process efficiency [left arrow] Size *** Process efficiency [left arrow] South *** Process efficiency [left arrow] Wage *** R-square = 0.300 0.016 Cost efficiency [left arrow] Technical *** efficiency Cost efficiency [left arrow] Process 0.034 efficiency Cost efficiency [left arrow] Provider- *** based Cost efficiency [left arrow] Poverty *** Cost efficiency [left arrow] Female 0.026 R-square = 0.341 Tests of Model Fit: Chi-square = 147.218 D.F. = 42 NFI = 0.935 TLI = 0.910 GFI = 0.952 RMSEA = 0.048 [beta] = unstandardized b = standardized *** p < 0.0001
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|Author:||Ortiz, Judith; Wan, Thomas T.H.; Meemon, Natthani; Paek, Seung Chun; Agiro, Abiy|
|Date:||Jul 1, 2010|
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