State Nurse Practice Agreements
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Empirical Research
To What Extent Are State Scope of Practice Laws Related to Nurse Practitioners’ Day-to-Day Practice Autonomy?
Jeongyoung Park1, Erin Athey1, Arlene Pericak1, Joyce Pulcini1, and Jessica Greene2
Abstract We explore the extent to which state scope of practice laws are related to nurse practitioners (NPs)’ day-to-day practice autonomy. We found that NPs experienced greater day-to-day practice autonomy when they had prescriptive independence. Surprisingly, there were only small and largely insignificant differences in day-to-day practice autonomy between NPs in fully restricted states and those in states with independent practice but restricted prescription authority. The scope of practice effects were strong for primary care NPs. We also found that the amount of variation in day-to-day practice autonomy within the scope of practice categories existed, which suggests that factors other than state scope of practice laws may influence NP practice as well. Removing barriers at all levels that potentially prevent NPs from practicing to the full extent of their education and training is critical not only to increase primary care capacity but also to make NPs more efficient and effective providers.
Keywords legal/regulatory policy, nurse practitioners, autonomy, primary care, workforce
This article, submitted to Medical Care Research and Review on January 5, 2016, was revised and accepted for publication on October 12, 2016.
1The George Washington University School of Nursing, Washington, DC, USA 2Baruch College, New York, NY, USA
Corresponding Author: Jeongyoung Park, The George Washington University School of Nursing, 1919 Pennsylvania Avenue, NW Suite 500, Washington, DC 20006, USA. Email: [email protected]
677826MCRXXX10.1177/1077558716677826Medical Care Research and ReviewPark et al. research-article2016
Park et al. 67
Introduction
Nurse practitioners (NPs) are registered nurses with graduate degrees that prepare them to diagnose and treat acute and chronic medical conditions. While NPs may practice as independent clinicians in 21 states and the District of Columbia, in most states (29), NPs’ scope practice is restricted by state laws (American Association of Nurse Practitioners, 2015b). There is substantial variation in how states restrict NP practice, though states are often categorized based on whether physician oversight is needed to diagnose and treat patients, and/or oversight is needed for prescribing medi- cations (Fairman, Rowe, Hassmiller, & Shalala, 2011; Iglehart, 2013; Kuo, Loresto, Rounds, & Goodwin, 2013). In Arkansas, for example, where both practice and pre- scriptions are restricted, NPs must maintain a collaborative agreement with a physi- cian that includes plans for consultation, coverage, quality assurance, and protocols for prescribing (Yee, Boukus, Cross, & Samuel, 2013). In Michigan, where only pre- scriptive authority is restricted, NPs must have a written authorization from a physi- cian to prescribe noncontrolled substance medications. For controlled substances, the physician can only delegate the writing of a prescription if they practice in the same facility and NPs can only prescribe a 7-day dose (Department of Licensing and Regulatory Affairs, 2015).
Restrictions on NPs’ scope of practice have been supported by the nation’s medical societies, which argue that NPs should be part of a physician-led team to ensure qual- ity and safety of patient care (American Medical Association, 2013; Iglehart, 2013). However, a growing body of literature shows that NPs provide primary care as safely as physicians, with comparable quality outcomes, and often with better patient experi- ence and at lower cost (Horrocks, Anderson, & Salisbury, 2002; Kuo, Chen, Baillargeon, Raji, & Goodwin, 2015; Laurant et al., 2005; Martin-Misener et al., 2015; Newhouse et al., 2012; Perloff, DesRoches, & Buerhaus, 2015). With predicted short- ages of primary care providers, resulting from an aging population and insurance expansion from the Affordable Care Act, increasingly NPs are called on to provide care to patients without “unnecessarily restrictive” state requirements (Auerbach et al., 2013; Fairman et al., 2011; Naylor & Kurtzman, 2010; Petterson et al., 2012; Richards & Polsky, 2016). In 2010, the Institute of Medicine’s influential Future of Nursing report called for NPs to “to practice to the full extent of their education, training, and competence” (Institute of Medicine, 2011, p. 194).
While the underlying premise of restricted practice scope laws is that NPs need oversight to provide high-quality care, there is no evidence that care by NPs in states with restricted scope of practice laws is of higher quality compared with independent states (Fairman et al., 2011). Only a few studies examine this issue, but they either find no difference in quality or report that NP independence is related to higher quality of care (Martsolf, Auerbach, & Arifkhanova, 2015; Oliver, Pennington, Revelle, & Rantz, 2014; Stange, 2014; Traczynski & Udalova, 2014). Restrictions on NPs’ scope of practice, however, are associated with having a smaller NP workforce and fewer patients seeing NPs than states with full practice authority (Kuo et al., 2013; Reagan & Salsberry, 2013; Richards & Polsky, 2016; Xue, Ye, Brewer, & Spetz, 2015). NPs
68 Medical Care Research and Review 75(1)
report that in restrictive states they have less flexibility in the range of settings where they can practice, as one explained “We are tethered to physicians” (Yee et al., 2013).
Interestingly, almost no research exists on the impact of scope of practice laws on NPs’ actual day-to-day practice autonomy. Yee et al.’s (2013) recent qualitative study suggests that the scope of practice laws did not have a large impact on NPs’ delivery of patient care. They wrote, “In practice, most respondents viewed collaborative agreements, which stipulate how the physician will supervise or monitor the NP’s performance and competency, as a formality that does not stimulate meaningful inter- action between NPs and physicians.” They described practice culture, payer policies, and the NP’s level of experience as often more important factors in determining how much oversight NPs received. This is not the only evidence suggesting scope of prac- tice laws do not influence NPs’ day-to-day practice. The Nurse Practitioner Association New York State (2009) in their advocacy to lift a restrictive scope of practice law in 2009 argued that greater independence would “not change how nurse practitioners actually practice or interact with physicians and other healthcare professionals.” The benefit, they argued, would be in removing barriers that would improve efficiency and reduce costs. This study explores the extent to which state scope of practice laws are related to NPs’ day-to-day practice autonomy.
New Contribution
Although the potential influence of scope of practice laws on NPs’ autonomy is widely acknowledged, there have been few empirical analyses examining this issue. Moreover, there has been no research on the amount of variation in autonomy that NPs experi- ence in states where the same legal authorities were given by state laws. This analysis of nationally representative survey data is the first to highlight the amount of variation in day-to-day practice autonomy that NPs experience within the three main scope of practice categories: independent practice and prescription authority, independent prac- tice but restricted prescription authority, and restricted practice and prescription authority. To the extent that variation is observed, particularly among NPs working in fully independent states, it may suggest that factors other than state scope of practice laws, such as payer and institutional policies, influence NP practice (Hansen-Turton, Ware, Bond, Doria, & Cunningham, 2013).
Method
Data and Study Population
This is a cross-sectional study examining the relationship between state scope of prac- tice laws and NPs’ day-to-day practice autonomy, using a large, nationally representa- tive sample. We utilized the restricted data file from the U.S. Health Resources and Services Administration’s (HRSA) 2012 National Sample Survey of Nurse Practitioners (NSSNPs) and linked it to the NP state scope of practice laws from 2012. This survey provides detailed data on licensure, education, clinical practice characteristics, and
Park et al. 69
demographics of a nationally represented sample of NPs in the United States. To obtain a representative sample of NPs, HRSA obtained listings of all actively licensed NPs from each state licensing board. A single national sampling frame was created using probability matching to identify and eliminate multiple records of the same NP. A sample of NPs was selected from each state with probability proportional to size. A total of 12,923 NPs completed and returned surveys, representing a response rate of 60.1%. Additional background information is available online (http://datawarehouse. hrsa.gov/data/dataDownload/AboutNSSNP2012.aspx).
For the purpose of this study, we included NPs who practiced in clinical settings and whose principal nursing position was working as an NP. A total of 9,021 respon- dents were included (70% of 12,923 completers). The number of observations included in the analysis differed for each day-to-day practice autonomy outcome due to missing values, as is detailed in the tables.
Measures
State scope of practice laws were the key independent variable in this study. We used Kuo et al.’s (2013) classification: (a) independent practice and prescription authority (independent), (b) independent practice and restricted prescription authority (restricted prescription), and (c) restricted practice and prescription authority (restricted).
We examined the association between state scope of practice laws in 2012 and the following five NP day-to-day practice autonomy measures: (a) NP skills being fully utilized (“my NP skills are fully utilized” answered on a Likert-type scale: strongly disagree, disagree, agree, strongly agree); (b) billing independence (bill under my provider number or bill under other provider/clinic number); (c) relationship with phy- sician (hierarchical or collaborative); (d) managing a panel of patients (yes or no); and (e) hospital admitting privileges (yes or no). Selection of day-to-day practice auton- omy measures was closely tied to the four major themes that are pertinent to under- standing the meaning of autonomy as interpreted by NPs within the context of their daily practice: defending the NP role, relationships, self-reliance, and self-empower- ment (Weiland, 2015). All measures selected in this study have been used in prior research analyzing the 2012 NSSNPs (Athey et al., 2015; Spetz, Skillman, & Andrilla, 2016).
The following NP characteristics were used as control variables in regression anal- yses: gender, years since graduating from initial NP program, race/ethnicity, highest degree (less than master’s, master’s degree, and doctorate), hourly salary quartiles at their primary position, urban versus rural location, and work setting (ambulatory, hos- pital, long-term and elderly care, public or community health, and other).
Analytic Approach
To examine the relationships between scope of practice laws and each of the day-to- day practice autonomy variables, we began with bivariate analyses (chi-square test). We then ran multivariate logistic regression models that controlled for demographic
70 Medical Care Research and Review 75(1)
and practice variables. For the dependent variable with a 4-point Likert-type scale (NP skills are fully utilized) in meaningful order, we conducted an ordered logistic regres- sion. We conducted these analyses separately for NPs working in primary care (inter- nal medicine, family practice, geriatrics, general pediatrics, and adolescent medicine) and specialty care.
Due to a consequence of the study design, respondents were clustered within a state. To address this, our models calculated clustered robust standard errors with the sandwich variance estimator. We also sought to use state fixed effects to control for any other state-level factors that might be related to NP day-to-day practice autonomy; however, models using state fixed effects resulted in unreliable and unstable estimates due to high multicollinearity with our key independent variable (i.e., state scope of practice laws). All analyses were performed and reported using unweighted data, though weighted data to reflect the sampling design had consistent results.
Results
Table 1 presents the NPs’ sample characteristics, separated by those who provided primary care and specialty care. Estimates of the number and share of NPs providing primary care depend on how one defines primary care (Spetz, Fraher, Li, & Bates, 2015). If the definition is based on the field of NP education (adult, family, gerontol- ogy, pediatrics, women’s health), the estimated shares in primary care are about 89% (American Association of Nurse Practitioners, 2015a). But the estimated number shrinks to about half of NPs if focusing on current field of clinical specialization (internal medicine, family practice, geriatrics, pediatrics; Spetz et al., 2015; Spetz et al., 2016). We followed the same approach used in the 2012 NSSNPs that basically aligns with those used by the medical specialty of the practice/facility (internal medi- cine, family practice, geriatrics, general pediatrics, adolescent medicine, obstetrics and gynecology, women’s health, school health). Our definition of primary care is further limited, which only includes internal medicine, family practice, geriatrics, gen- eral pediatrics, and adolescent medicine. Together the primary care fields indicate that 38% of NPs in our sample (n = 3,471) practice in primary care.
Consistent with the demography of nursing, the study population was predomi- nantly female (92.9%) and White (85.7%). Almost all NPs held master’s degrees (89.1%), while a small minority (4.9%) had earned doctorates. Nearly, one third reported that they graduated from their initial NP education program within the past 5 years. As seen in the last two columns in Table 1, primary care NPs were more than twice as likely to practice in rural areas compared with specialty care NPs (25.5% vs. 10.7%) and they were substantially more likely to practice in ambulatory care settings (75.3% vs. 45.0%).
Only 16.6% of all NPs in our sample practiced in states with independent scope of practice. Twenty percent worked in states with restricted prescription authority and the majority (63.5%) worked in states with restricted scope of practice. NPs working in primary care were slightly more likely to practice in states with greater independence than their specialty care counterparts.
Park et al. 71
Table 1. Characteristics of NPs in the Sample.
All NPs, percentage, N = 9,021
Primary care NPs, percentage,
n = 3,471
Specialty care NPs, percentage,
n = 5,550
Demographics Gender Male 7.1 6.7 7.3 Female 92.9 93.3 92.7 Years since graduating from initial NP program*** 5 Years and under 29.2 30.6 28.3 6-10 Years 20.9 19.2 22.0 11-15 Years 22.7 22.6 22.8 16-20 Years 13.2 14.2 12.6 21 Years and over 14.0 13.4 14.4 Race/ethnicity*** Hispanic 3.6 3.0 3.9 White, non-Hispanic 85.7 85.1 86.2 Black, non-Hispanic 4.9 5.9 4.2 Asian, non-Hispanic 4.3 4.4 4.3 Other and not reported 1.5 1.6 1.5 Highest degree*** Less than master’s 6.1 4.1 7.3 Master’s degree 89.1 91.1 87.8 Doctorate 4.9 4.8 4.9 Hourly salary at primary position*** Lowest quartile 23.8 25.7 22.7 Second quartile 25.3 27.1 24.2 Third quartile 21.6 21.8 21.5 Top quartile 23.2 18.7 25.9 Not reported 6.1 6.8 5.7 Practice Urban/rural*** Urban 83.6 74.5 89.3 Rural 16.4 25.5 10.7 Work setting*** Ambulatory 56.7 75.3 45.0 Hospital 30.4 9.7 43.3 Long-term and elderly care 4.3 6.1 3.3 Other and not reported 8.7 8.9 8.5 State scope of practice*** Independent 16.6 17.9 15.7 Restricted prescription 20.0 20.5 19.7 Restricted 63.5 61.7 64.6
Note. NP = nurse practitioner. Primary care specialties include internal medicine, family practice, geriatrics, general pediatrics, and adolescent medicine. Lowest quartile was up to $36.54 per hour, second quartile was from $36.55 to $43.27, the third quartile was from $43.28 to $50.96, and the top quartile earned $50.97 and higher. Adapted from 2012 National Sample Survey of Nurse Practitioners.***p < .01.
72 Medical Care Research and Review 75(1)
NPs reported varying levels of day-to-day practice autonomy across the five mea- sures (Table 2). The majority of NPs reported that their skills were being fully utilized (50.0% strongly agreed and 34.5% agreed, while only 15.6% disagreed or strongly disagreed). Most NPs (84.3%) described their working relationships with physicians as collaborative rather than hierarchical. However, fewer (46.6%) reported billing under their own provider number or managing their own patient panel (46.1%). Only one in five reported having hospital admitting privileges.
The degree of day-to-day practice autonomy that NPs reported significantly dif- fered by the state scope of practice laws (see the second panel in Table 2). NPs who worked in independent states reported the highest levels of day-to-day practice auton- omy for four of the five measures, followed by those in restricted prescription states and then those in fully restricted states. For example, 57.7% of NPs who worked in independent states strongly agreed that their skills were fully utilized, compared with 50.3% and 47.8% in restricted prescription and fully restricted states, respectively. Similarly, 56.4% of NPs in independent states billed under their own provider number, whereas 45.5% and 44.3% did in restricted prescription authority and fully restrictive states. Interestingly, there was no relationship between scope of practice laws and hospital admitting privileges.
Table 2. Variation in Reported Autonomy Measures by State Scope of Practice Laws.
All NPs, percentage
Independent, percentage
Restricted prescription, percentage
Restricted, percentage
Skills are fully utilized (N = 9,021)*** Strongly disagree 2.2 1.3 2.2 2.4 Disagree 13.4 9.1 12.7 14.7 Agree 34.5 31.9 34.8 35.1 Strongly agree 50.0 57.7 50.3 47.8 Billing independence (n = 7,238)*** Bill under other provider
or clinic number 53.4 43.6 54.5 55.7
Bill under my provider number
46.6 56.4 45.5 44.3
Relationship with physician (n = 8,727)*** Hierarchical 15.7 9.4 16.1 17.0 Collaborative 84.3 90.6 83.9 83.0 Manage own panel of patients (n = 8,944)*** No 53.9 46.9 55.9 55.1 Yes 46.1 53.1 44.1 44.9 Hospital admitting privileges (n = 8,958) No 79.3 79.9 79.6 79.1 Yes 20.7 20.2 20.4 20.9
Note. NP = nurse practitioner. ***p < .01.
Park et al. 73
Table 3 presents the odds ratios (ORs) from logistic regression models examining state scope of practice laws and day-to-day practice autonomy, controlling for demo- graphic and practice characteristics. Consistent with the bivariate analyses, for all but one measure (hospital admitting privilege) NPs in independent states had higher odds of day-to-day practice autonomy than those in states with restricted prescription authority (ORs ranged from 1.28 to 1.72). For only one outcome (skills fully utilized) was there a statistically significant difference between NPs in states with restricted prescription and fully restricted authority.
The state scope of practice effects were strong for primary care NPs (see the second and third horizontal panels in Table 3). For primary care NPs, fully independent scope of practice was associated with higher odds of NPs’ degree of day-to-day practice autonomy (ORs ranged from 1.48 to 2.61) compared with those in states with restricted prescription authority. For specialty care NPs, the general patterns observed were sim- ilar to primary care NPs, but the effects of independent scope of practice laws were smaller in magnitude (ORs ranged from 1.16 to 1.56).
The one measure of day-to-day practice autonomy that was not related to state scope of practice was having hospital admitting privileges. We observed no statisti- cally significant relationships between state scope of practice laws and hospital admit- ting privileges for primary care NPs, specialty care NPs, or all NPs.
In addition to state scope of practice laws, practicing in rural areas was consis- tently positively associated with the five day-to-day practice autonomy measures. For example, NPs in rural areas were more likely to have their own patient panel (OR = 1.77) and to bill under their own provider number (OR = 1.43) compared with NPs in urban areas. Conversely, working in hospital settings was negatively associ- ated with day-to-day practice autonomy (ORs ranged from 0.26 to 0.76) for four of the five day-to-day practice autonomy outcomes. In general, demographic character- istics were not related to the degree of day-to-day practice autonomy that NPs reported. Experienced NPs and those with higher salaries were more likely to report that their skills were fully utilized and that they had a collaborative relationship with their physician colleagues. The full regression results are provided in the Appendix Tables 1 to 5.
Discussion
This study assessed the association between state scope of practice laws and the degree of day-to-day practice autonomy that NPs reported. What we found was a somewhat complex story. First, while NPs in states with greater legal authority were associated with higher degree of day-to-day practice autonomy in four out of the five measures we examined, it was having independent prescription authority that was critical. Independent practice authority without prescription authority was associated with very little benefit over fully restricted states. Second, notably our findings also highlight that scope of practice laws are not the only barriers that restrict NPs practice. Third, the state scope of practice effects were strong for primary care NPs. We will discuss each finding below.
74
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Park et al. 75
NPs in states with full practice and prescription authority were more likely to have their skills fully utilized, bill independently, have a collaborative relationship with physicians, and manage patient panels than those in states with independent practice but limited prescription authority. In a surprising finding, there were only small and largely insignificant differences reported in the degree of day-to-day practice auton- omy between NPs in fully restricted states and those in states with independent prac- tice but restricted prescription authority. In other words, having only independent practice was associated with no more likelihood of billing independence, having a collaborative relationship, or managing patient panels than having a restricted practice.
There has been much disagreement over the ability of NPs to prescribe, in particu- lar, controlled drugs that have potential for abuse or dependence. Thus, some states do not allow NPs to prescribe controlled substances, while others have restrictions or limitations on the drug type, dosing, or length of supply. However, prior studies have shown that patient outcomes were comparable between the care provided by NPs and physicians in states where NPs and physicians have the same prescriptive authority (Mundinger et al., 2000). Furthermore, granting independent prescriptive authority to NPs had a direct impact on increased office-based primary care visits (Stange, 2014), and NPs were more likely to move from states without controlled substance prescrib- ing authority to states with such authority (Perry, 2012).
Although states in recent years have moved toward adding more independent pre- scription authority, currently, only 21 states and the District of Columbia allow NPs to prescribe medications without any restrictions, whereas the remaining 29 states require physician collaboration or restrictions on what NPs can prescribe (American Association of Nurse Practitioners, 2015b). In states with restrictions, NPs must refer patients to or consult with a physician. These restrictions increase wait times for patients, delay treatment, and increase health care costs associated with a possible second visit (Yee et al., 2013). Our findings add to the literature suggesting that pre- scribing medications independently is key for NPs to provide continuous, comprehen- sive, and coordinated care to patients. States considering more independent for NPs should not opt for the middle ground of independent practice and restricted prescrip- tion authority, as it seems to accrue little day-to-day practice autonomy benefit to NPs over fully restricted scope of practice.
Our findings also highlight that scope of practice laws are not the only barriers that restrict NPs practice. We found that the state scope of practice laws were not a signifi- cant predictor of having hospital admitting privileges that is often determined by their institutional bylaws and policies. We also found that some restrictions exist even within the independent scope of practice states. Only half of NPs reported that they billed under their own provider number and had their own patient panels even in states where their independent practice and prescription authority was legally guaranteed by state laws. Interestingly, even in fully restricted states, most NPs reported higher level of day-to-day practice autonomy in terms of having their skills fully utilized and described their working relationships with physicians as collaborative. These findings suggest that while state regulations will be a primary barrier to limiting the degree to
76 Medical Care Research and Review 75(1)
which NPs may practice autonomously, NP practice is often constrained by other fac- tors other than state scope of practice laws.
For example, hospital and facility bylaws often define specific services that NPs can or cannot provide within institutions. In our study, only 20% of NPs reported that they had hospital admitting privileges and this was consistent for NPs regardless of the state scope of practice regulations. Furthermore, it is unclear why the majority of NPs do not have hospital admitting privileges. Nevertheless, this can create challenges for patient follow-up because continuity of care is an important aspect of providing the best care for patients. At a time when care coordination and continuity of care have drawn national attention, lack of hospital admitting privileges poses a significant obstacle to achieving these goals.
Payer policies also likely have a significant impact on NPs’ ability to practice inde- pendently (Yee et al., 2013). Under Medicare, there is a clear financial incentive on incident-to-billing on part of physicians, who can garner 100% of the reimbursement rate versus 85% if the claim is billed by an NP. We know anecdotally, NPs working in full scope of practice states whose employer requires them to bill under physicians’ billing number, because of the greater revenue. Private insurance payment policies may vary and often impose additional restrictions.
Last, we found that the state scope of practice effects were strong for primary care NPs. This finding, in turn, suggests that the restrictive state regulations might result in NPs choosing to practice in specialty care rather than in primary care. Given the nation’s need for more primary care providers, that would be problematic. Additionally, prior studies have indicated that restrictive state regulations also limit where NPs can deliver care (Xue et al., 2015; Yee et al., 2013). As physicians are more likely to be concentrated in urban areas, NPs in states requiring physician collaboration or super- vision tend to be close to physicians, and thereby these restrictions disproportionally affect populations in rural and medically underserved areas.
NPs have emerged as primary care providers in response to shortages of primary care physicians, and have historically played a vital role in providing primary care in rural and medically underserved areas. Thus, relaxing scope of practice may be more beneficial for populations who seek for primary care in rural and medically underserved areas. Our data, however, indicated that only 38% of NPs in our sample practiced in primary care. Recent evidence has shown that at least half of NPs did not practice in primary care and NP specialization will continue to grow (Spetz et al., 2015).
The results of this research should be considered in light of the study’s limitations. Most notably, our study was cross sectional in nature so that causal assumptions can- not be made. Future research should examine the impact of changing state scope of practice laws over time. Second, the validity of measuring day-to-day practice auton- omy is a concern. Studies are still sparse with regard to understanding the meaning of autonomy as interpreted by NPs through their everyday practice. It is reasonable to assume that all the five items selected in this study have face validity and are pertinent to understanding NP autonomy as they have been widely used in prior research. Another key limitation is that we used a commonly used categorization scheme for state scope of practice laws, which does not address the variation in policies within the
Park et al. 77
categories (Martsolf et al., 2015). Yee et al.’s (2013) paper shows how restrictions dif- fer within each of the three categories of scope of practice. Last, there are likely to be some important confounding variables related to the outcomes. For example, a socio- cultural ideology sometimes obscures legitimate state NP policies. However, we were unable to control for unobserved state differences because introducing state fixed effects resulted in unreliable and unstable regression estimates due to high multicol- linearity with our key independent variable.
Despite these limitations, this is the first nationally representative study of NPs to examine the association between state scope of practice laws and the degree of day-to- day practice autonomy that they reported. As the demand for primary care increases, NPs are expected to have an active role in meeting primary care needs. In addition, the use of NPs is believed to be the plausible strategy to improve health care efficiency through health care redesign such as patient-centered medical homes, retail clinics, and nurse-managed health centers. This study’s findings have implications for state governments as well as hospitals and delivery systems. Removing barriers at all levels that potentially prevent NPs from practicing to the full extent of their education and training is critical not only to increase primary care capacity but also to make NPs more efficient and effective health care providers.
Appendix
Table 1. Skills are Fully Utilized, Full Ordered Logistic Regression Results.
All NPs, N = 9,021
Primary care NPs, n = 3,471
Specialty care NPs, n = 5,550
OR [95% CI] p OR [95% CI] p OR [95% CI] p
State scope of practice Independent 1.28 [1.11, 1.47] .001 1.48 [1.11, 1.96] .007 1.16 [0.98, 1.37] .08 Restricted
prescription (ref) 1.00 1.00 1.00
Restricted 0.89 [0.81, 0.98] .02 0.86 [0.72, 1.04] .12 0.91 [0.80, 1.02] .11 Demographics Gender Male (ref) 1.00 1.00 1.00 Female 1.06 [0.92, 1.23] .41 1.24 [0.89, 1.72] .20 0.99 [0.83, 1.19] .93 Years since graduating from initial NP program 5 Years and
under (ref) 1.00 1.00 1.00
6-10 Years 1.23 [1.09, 1.39] .001 1.25 [1.07, 1.47] .006 1.24 [1.06, 1.45] .007 11-15 Years 1.27 [1.14, 1.42] <.001 1.35 [1.12, 1.62] .002 1.25 [1.06, 1.48] .01 16-20 Years 1.70 [1.46, 1.98] <.001 1.77 [1.36, 2.30] <.001 1.65 [1.33, 2.04] <.001
(continued)
78 Medical Care Research and Review 75(1)
All NPs, N = 9,021
Primary care NPs, n = 3,471
Specialty care NPs, n = 5,550
OR [95% CI] p OR [95% CI] p OR [95% CI] p
21 Years and over
1.65 [1.46, 1.89] <.001 1.84 [1.45, 2.34] <.001 1.56 [1.29, 1.88] <.001
Race/ethnicity Hispanic 0.85 [0.67, 1.08] .18 0.95 [0.67, 1.33] .76 0.81 [0.60, 1.11] .20 White, non-
Hispanic (ref) 1.00 1.00 1.00
Black, non- Hispanic
0.88 [0.73, 1.07] .20 0.88 [0.61, 1.25] .46 0.82 [0.63, 1.06] .13
Asian, non- Hispanic
1.16 [1.00, 1.35] .06 1.07 [0.78, 1.47] .66 1.16 [1.00, 1.35] .05
Other and not reported
0.77 [0.57, 1.04] .08 0.72 [0.44, 1.18] .20 0.81 [0.51, 1.26] .35
Highest degree Less than
master’s (ref) 1.00 1.00 1.00
Master’s degree 1.10 [0.91, 1.34] .31 0.70 [0.47, 1.04] .08 1.10 [0.89, 1.37] .37 Doctorate 1.00 [0.76, 1.31] 1.00 0.56 [0.33, 0.95] .03 1.11 [0.78, 1.59] .56 Hourly salary at primary position Lowest quartile
(ref) 1.00 1.00 1.00
Second quartile 1.09 [0.98, 1.22] .10 1.08 [0.87, 1.33] .48 1.09 [0.96, 1.24] .17 Third quartile 1.22 [1.10, 1.35] <.001 0.98 [0.83, 1.15] .77 1.39 [1.18, 1.62] <.001 Top quartile 1.33 [1.19, 1.49] <.001 1.08 [0.87, 1.34] .47 1.54 [1.33, 1.79] <.001 Not reported 1.06 [0.90, 1.26] .46 0.99 [0.76, 1.29] .95 1.07 [0.83, 1.38] .61 Practice Urban/rural Urban (ref) 1.00 1.00 1.00 Rural 1.29 [1.16, 1.44] <.001 1.20 [1.02, 1.41] .03 1.12 [0.94, 1.33] .20 Work setting Ambulatory
(ref) 1.00 1.00 1.00
Hospital 0.58 [0.51, 0.65] <.001 0.71 [0.59, 0.86] <.001 0.66 [0.58, 0.75] <.001 Long-term and
elderly care 0.87 [0.74, 1.01] .06 0.87 [0.69, 1.10] .25 0.85 [0.64, 1.13] .27
Other and not reported
0.61 [0.53, 0.70] <.001 0.70 [0.57, 0.86] .001 0.61 [0.53, 0.72] <.001
Note. NP = nurse practitioner; OR = odds ratio; CI = confidence interval. Standard error adjusted for 51 clusters in state.
Table 1. (continued)
Park et al. 79
Table 2. Billing Independence, Full Logistic Regression Results.
All NPs, n = 7,238
Primary care NPs, n = 2,870
Specialty care NPs, n = 4,368
OR [95% CI] p OR [95% CI] p OR [95% CI] p
State scope of practice Independent 1.56 [1.09, 2.23] .01 1.62 [1.11, 2.35] 0.01 1.55 [1.02, 2.36] .04 Restricted
prescription (ref)
1.00 1.00 1.00
Restricted 1.00 [0.75, 1.33] .98 1.14 [0.80, 1.62] 0.48 0.92 [0.69, 1.23] .59 Demographics Gender Male (ref) 1.00 1.00 1.00 Female 0.89 [0.77, 1.03] .11 1.10 [0.88, 1.38] .41 0.77 [0.64, 0.92] .005 Years since graduating from initial NP program 5 Years and
under (ref) 1.00 1.00 1.00
6-10 Years 1.05 [0.91, 1.20] .50 1.02 [0.84, 1.24] .81 1.09 [0.92, 1.28] .33 11-15 Years 1.08 [0.95, 1.23] .25 1.03 [0.81, 1.31] .81 1.12 [0.95, 1.32] .18 16-20 Years 1.02 [0.88, 1.18] .78 0.96 [0.76, 1.22] .76 1.05 [0.88, 1.26] .60 21 Years and
over 0.83 [0.70, 1.03] .09 0.64 [0.48, 0.86] .003 0.99 [0.79, 1.25] .95
Race/ethnicity Hispanic 0.59 [0.49, 0.72] <.001 0.52 [0.37, 0.73] <.001 0.64 [0.49, 0.83] .001 White, non-
Hispanic (ref) 1.00 1.00 1.00
Black, non- Hispanic
0.68 [0.53, 0.88] .003 0.58 [0.43, 0.79] <.001 0.72 [0.51, 1.03] .07
Asian, non- Hispanic
0.72 [0.55, 0.96] .02 0.57 [0.41, 0.79] .001 0.82 [0.52, 1.30] .40
Other and not reported
0.80 [0.58, 1.10] .17 0.69 [0.40, 1.19] .18 0.89 [0.55, 1.44] .64
Highest degree Less than
master’s (ref) 1.00 1.00 1.00
Master’s degree
1.35 [1.09, 1.67] .01 1.66 [1.18, 2.34] .004 1.20 [0.94, 1.54] .14
Doctorate 1.46 [1.09, 1.96] .01 1.59 [1.02, 2.48] .04 1.37 [0.90, 2.10] .14 Hourly salary at primary position Lowest
quartile (ref) 1.00 1.00 1.00
Second quartile
1.02 [0.88, 1.18] .80 1.04 [0.84, 1.30] .71 1.00 [0.80, 1.24] .97
Third quartile 0.96 [0.84, 1.09] .53 0.92 [0.71, 1.19] .53 0.98 [0.81, 1.17] .80
(continued)
80 Medical Care Research and Review 75(1)
All NPs, n = 7,238
Primary care NPs, n = 2,870
Specialty care NPs, n = 4,368
OR [95% CI] p OR [95% CI] p OR [95% CI] p
Top quartile 1.04 [0.81, 1.29] .72 1.27 [0.90, 1.80] .18 0.94 [0.74, 1.20] .61 Not reported 1.03 [0.87, 1.22] .71 1.07 [0.77, 1.50] .69 1.00 [0.77, 1.29] .99 Practice Urban/rural Urban (ref) 1.00 1.00 1.00 Rural 1.43 [1.23, 1.66] <.001 1.37 [1.14, 1.65] .001 1.32 [1.09, 1.59] .004 Work setting Ambulatory
(ref) 1.00 1.00 1.00
Hospital 0.76 [0.61, 0.94] .01 0.88 [0.65, 1.18] 0.40 0.80 [0.64, 1.01] .07 Long-term and
elderly care 3.03 [2.24, 4.09] <.001 3.55 [2.34, 5.39] <.001 2.60 [1.79, 3.79] <.001
Other and not reported
0.91 [0.71, 1.17] .47 1.11 [0.78, 1.57] 0.56 0.83 [0.62, 1.12] .22
Note. NP = nurse practitioner; OR = odds ratio; CI = confidence interval. Standard error adjusted for 51 clusters in state.
Table 2. (continued)
(continued)
Table 3. Collaborative Relationship With Physician, Full Logistic Regression Results.
All NPs, n = 8,727
Primary care NPs, n = 3,343
Specialty care NPs, n = 5,384
OR [95% CI] p OR [95% CI] p OR [95% CI] p
State scope of practice Independent 1.72 [1.22, 2.42] .002 2.61 [1.61, 4.23] <.001 1.56 [1.05, 2.30] .03 Restricted
prescription (ref)
1.00 1.00 1.00
Restricted 0.96 [0.70, 1.32] .80 1.10 [0.77, 1.56] .62 0.94 [0.66, 1.34] .73 Demographics Gender Male (ref) 1.00 1.00 1.00 Female 0.98 [0.80, 1.21] .85 0.88 [0.46, 1.67] .69 1.02 [0.83, 1.24] .87 Years since graduating from initial NP program 5 Years and
under (ref) 1.00 1.00 1.00
6-10 Years 1.20 [1.02, 1.41] .03 1.45 [1.05, 2.01] .03 1.19 [0.96, 1.47] .11 11-15 Years 1.68 [1.40, 2.02] <.001 2.23 [1.63, 3.05] <.001 1.60 [1.28, 2.00] <.001 16-20 Years 1.76 [1.42, 2.19] <.001 1.58 [1.15, 2.19] .005 1.78 [1.32, 2.38] <.001
Park et al. 81
All NPs, n = 8,727
Primary care NPs, n = 3,343
Specialty care NPs, n = 5,384
OR [95% CI] p OR [95% CI] p OR [95% CI] p
21 Years and over
1.61 [1.24, 2.09] <.001 1.77 [0.93, 3.34] .08 1.52 [1.18, 1.95] .001
Race/ethnicity Hispanic 0.78 [0.61, 1.00] .05 1.02 [0.38, 2.79] .96 0.75 [0.56, 0.99] .05 White, non-
Hispanic (ref) 1.00 1.00 1.00
Black, non- Hispanic
0.88 [0.66, 1.17] .38 0.91 [0.51, 1.60] 0.74 0.77 [0.55, 1.09] .14
Asian, non- Hispanic
0.82 [0.66, 1.02] .07 0.54 [0.30, 0.95] 0.03 0.84 [0.62, 1.14] .26
Other and not reported
0.58 [0.34, 0.98] .04 0.73 [0.25, 2.12] 0.57 0.54 [0.31, 0.97] .04
Highest degree Less than
master’s (ref) 1.00 1.00 1.00
Master’s degree 0.97 [0.71, 1.32] .83 0.77 [0.34, 1.73] .52 0.87 [0.62, 1.22] .42 Doctorate 1.08 [0.71, 1.64] .73 0.45 [0.17, 1.16] .10 1.19 [0.69, 2.05] .54 Hourly salary at primary position Lowest quartile
(ref) 1.00 1.00 1.00
Second quartile 1.23 [1.05, 1.44] .01 1.49 [1.07, 2.07] .02 1.15 [0.92, 1.43] .22 Third quartile 1.28 [1.07, 1.54] .01 2.23 [1.48, 3.35] <.001 1.13 [0.92, 1.37] .24 Top quartile 1.44 [1.15, 1.81] .001 1.67 [1.07, 2.60] .02 1.45 [1.12, 1.87] .005 Not reported 1.18 [0.87, 1.59] .29 1.11 [0.63, 1.94] .72 1.17 [0.81, 1.70] .39 Practice Urban/rural Urban (ref) 1.00 1.00 1.00 Rural 1.54 [1.28, 1.85] <.001 1.08 [0.80, 1.45] .63 1.44 [1.15, 1.82] .002 Work setting Ambulatory
(ref) 1.00 1.00 1.00
Hospital 0.26 [0.22, 0.31] <.001 0.32 [0.22, 0.47] <.001 0.36 [0.30, 0.42] <.001 Long-term and
elderly care 1.02 [0.70, 1.48] .92 0.61 [0.32, 1.19] .15 1.34 [0.92, 1.93] .12
Other and not reported
0.88 [0.72, 1.09] .25 0.76 [0.47, 1.24] .28 1.10 [0.84, 1.44] .48
Note. NP = nurse practitioner; OR = odds ratio; CI = confidence interval. Standard error adjusted for 51 clusters in state.
Table 3. (continued)
82 Medical Care Research and Review 75(1)
Table 4. Manage Own Panel of Patients, Full Logistic Regression Results.
All NPs, n = 8,944
Primary care NPs, n = 3,449
Specialty care NPs, n = 5,495
OR [95% CI] p OR [95% CI] p OR [95% CI] p
State scope of practice Independent 1.36 [1.16, 1.61] <.001 1.81 [1.33, 2.48] <.001 1.18 [1.00, 1.40] .046 Restricted
prescription (ref)
1.00 1.00 1.00
Restricted 1.06 [0.94, 1.19] .32 1.12 [0.91, 1.39] .28 1.04 [0.89, 1.21] .66 Demographics Gender Male (ref) 1.00 1.00 1.00 Female 1.12 [0.96, 1.31] .14 0.98 [0.75, 1.28] .90 1.20 [0.95, 1.53] .13 Years since graduating from initial NP program 5 Years and
under (ref) 1.00 1.00 1.00
6-10 Years 1.22 [1.09, 1.36] .001 1.45 [1.14, 1.84] .003 1.19 [0.98, 1.44] .07 11-15 Years 1.00 [0.89, 1.12] .99 1.05 [0.84, 1.30] .69 1.00 [0.85, 1.18] .98 16-20 Years 1.19 [1.04, 1.36] .01 1.10 [0.89, 1.35] .39 1.20 [0.97, 1.47] .09 21 Years and
over 1.12 [0.98, 1.27] .09 1.11 [0.85, 1.44] .45 1.13 [0.93, 1.37] .23
Race/ethnicity Hispanic 1.18 [0.95, 1.47] .12 0.95 [0.67, 1.35] .78 1.35 [1.08, 1.69] .008 White, non-
Hispanic (ref)
1.00 1.00 1.00
Black, non- Hispanic
1.01 [0.82, 1.24] .95 0.80 [0.58, 1.10] .17 1.03 [0.83, 1.29] .77
Asian, non- Hispanic
1.15 [0.93, 1.43] .20 1.05 [0.79, 1.41] .74 1.10 [0.82, 1.49] .51
Other and not reported
1.14 [0.84, 1.55] .39 0.83 [0.47, 1.47] .52 1.45 [1.02, 2.06] .04
Highest degree Less than
master’s (ref)
1.00 1.00 1.00
Master’s degree
1.42 [1.19, 1.71] <.001 1.27 [0.81, 1.99] .30 1.16 [0.95, 1.41] .14
Doctorate 1.64 [1.26, 2.12] <.001 1.76 [1.04, 2.98] .04 1.26 [0.97, 1.62] .08
(continued)
Park et al. 83
All NPs, n = 8,944
Primary care NPs, n = 3,449
Specialty care NPs, n = 5,495
OR [95% CI] p OR [95% CI] p OR [95% CI] p
Hourly salary at primary position Lowest
quartile (ref) 1.00 1.00 1.00
Second quartile
1.14 [1.01, 1.28] .03 1.12 [0.87, 1.44] .38 1.12 [0.93, 1.36] .23
Third quartile 1.15 [1.00, 1.32] .06 1.00 [0.76, 1.31] .99 1.23 [1.03, 1.46] .02 Top quartile 0.91 [0.80, 1.05] .19 0.85 [0.67, 1.07] .17 1.02 [0.87, 1.21] .78 Not reported 0.91 [0.71, 1.17] .46 0.74 [0.53, 1.02] .06 0.98 [0.70, 1.37] .90 Practice Urban/rural Urban (ref) 1.00 1.00 1.00 Rural 1.77 [1.54, 2.04] <.001 2.07 [1.73, 2.48] <.001 1.00 [0.81, 1.23] .99 Work setting Ambulatory
(ref) 1.00 1.00 1.00
Hospital 0.44 [0.39, 0.50] <.001 0.67 [0.55, 0.82] <.001 0.60 [0.52, 0.68] <.001 Long-term
and elderly care
1.25 [0.97, 1.61] .09 1.02 [0.71, 1.46] .91 1.55 [1.20, 2.02] .001
Other and not reported
0.78 [0.65, 0.93] .01 0.81 [0.64, 1.03] .09 0.88 [0.68, 1.13] .31
Note. NP = nurse practitioner; OR = odds ratio; CI = confidence interval. Standard error adjusted for 51 clusters in state.
Table 4. (continued)
Table 5. Hospital Admitting Privileges, Full Logistic Regression Results.
All NPs, n = 8,958
Primary care NPs, n = 3,455
Specialty care NPs, n = 5,503
OR [95% CI] p OR [95% CI] p OR [95% CI] p
State scope of practice Independent 1.02 [0.74, 1.40] .92 0.90 [0.61, 1.33] .60 1.09 [0.75, 1.58] .67 Restricted
prescription (ref)
1.00 1.00 1.00
Restricted 1.05 [0.81, 1.36] .71 1.11 [0.78, 1.57] .57 1.02 [0.78, 1.33] .91
(continued)
84 Medical Care Research and Review 75(1)
Table 5. (continued)
All NPs, n = 8,958
Primary care NPs, n = 3,455
Specialty care NPs, n = 5,503
OR [95% CI] p OR [95% CI] p OR [95% CI] p
Demographics Gender Male (ref) 1.00 1.00 1.00 Female 1.10 [0.90, 1.35] .33 1.14 [0.74, 1.76] 0.55 1.08 [0.83, 1.41] .56 Years since graduating from initial NP program 5 Years and
under (ref) 1.00 1.00 1.00
6-10 Years 1.03 [0.94, 1.13] .52 1.12 [0.84, 1.50] .44 0.96 [0.83, 1.11] .57 11-15 Years 0.89 [0.78, 1.02] .09 1.18 [0.92, 1.50] .20 0.78 [0.67, 0.92] .003 16-20 Years 0.73 [0.61, 0.87] .001 0.94 [0.68, 1.30] .70 0.66 [0.55, 0.80] <.001 21 Years and
over 0.76 [0.64, 0.90] .002 1.07 [0.79, 1.45] .65 0.65 [0.54, 0.80] <.001
Race/ethnicity Hispanic 1.04 [0.86, 1.26] .71 0.89 [0.59, 1.32] .56 1.08 [0.83, 1.40] .57 White, non-
Hispanic (ref)
1.00 1.00 1.00
Black, non- Hispanic
0.71 [0.47, 1.07] .10 0.55 [0.27, 1.13] .10 0.86 [0.61, 1.21] .37
Asian, non- Hispanic
0.87 [0.61, 1.24] .45 0.98 [0.57, 1.69] .95 0.89 [0.63, 1.26] .51
Other and not reported
1.03 [0.72, 1.48] .88 0.98 [0.40, 2.41] .96 1.06 [0.67, 1.66] .81
Highest degree Less than
master’s (ref)
1.00 1.00 1.00
Master’s degree
1.33 [0.95, 1.85] .10 1.10 [0.56, 2.14] .78 1.54 [1.13, 2.08] .006
Doctorate 1.37 [0.96, 1.96] .08 1.21 [0.54, 2.71] .65 1.53 [1.11, 2.12] .01 Hourly salary at primary position Lowest
quartile (ref)
1.00 1.00 1.00
Second quartile
1.10 [0.93, 1.27] .29 0.98 [0.79, 1.21] .82 1.17 [0.96, 1.43] .13
Third quartile
1.00 [0.84, 1.19] .98 0.90 [0.66, 1.23] .52 1.06 [0.88, 1.29] .54
(continued)
Park et al. 85
All NPs, n = 8,958
Primary care NPs, n = 3,455
Specialty care NPs, n = 5,503
OR [95% CI] p OR [95% CI] p OR [95% CI] p
Top quartile 1.10 [0.93, 1.30] .29 1.28 [0.99, 1.66] .06 1.02 [0.83, 1.26] .84 Not
reported 0.85 [0.68, 1.06] .14 0.78 [0.52, 1.18] .24 0.90 [0.69, 1.17] .43
Practice Urban/rural Urban (ref) 1.00 1.00 1.00 Rural 1.20 [1.00, 1.44] .06 1.76 [1.44, 2.16] <.001 1.08 [0.86, 1.36] .50 Work setting Ambulatory
(ref) 1.00 1.00 1.00
Hospital 1.72 [1.44, 2.05] <.001 1.75 [1.30, 2.35] <.001 1.31 [1.07, 1.59] .009 Long-term
and elderly care
0.45 [0.31, 0.67] <.001 0.77 [0.47, 1.26] .30 0.29 [0.16, 0.51] <.001
Other and not reported
0.82 [0.63, 1.07] .15 0.89 [0.61, 1.31] .56 0.69 [0.50, 0.95] .02
Note. NP = nurse practitioner; OR = odds ratio; CI = confidence interval. Standard error adjusted for 51 clusters in state.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the George Washington University School of Nursing and the school’s former Dean Jean Johnson for providing funding to analyze the NSSNP restricted data in the Research Data Center of the National Center for Health Statistics.
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Table 5. (continued)
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