VI. Advanced Practice Nurse Model Components

Published 2024

In this module:

This module describes the data and assumptions used in the Health Workforce Simulation Model (HWSM) to project the future supply and demand for advanced practice nurses (APRNs). The three types of APRNs modeled are nurse practitioners (NPs), certified nurse midwives (CNMs), and certified registered nurse anesthetists (CRNAs). Historically, clinical nurse specialists (CNSs) are often included in the definition of APRNs but are not modeled in HWSM due to data limitations. Many CNSs are licensed as NPs and thus included in the NP projections. APRNs hold at least a master’s degree in addition to being licensed as a registered nurse (RN). NPs practice across many specialties—including primary, acute, and specialty care. They are modeled by specialty in the HWSM.1 CNMs provide women’s health services with their work overlapping with obstetrician-gynecologists but focusing on preventive, gynecological, and reproductive health without the surgical component. CRNAs provide anesthesia and pain management services with their work overlapping that of anesthesiologists.

Other modules in this technical document describe the overall modeling approach. In this module we summarize data and assumptions pertinent to modeling the APRN workforce.

Modeling supply

Supply modeling consists of estimating the number and characteristics of current supply, modeling the number and characteristics of new entrants to supply, modeling attrition, and modeling workforce behavior to include hours worked patterns and geographic mobility.

Estimating the current active workforce supply

This section describes construction of the starting supply file to model NPs, CNMs, and CRNAs. We discuss each APRN type separately.

Nurse Practitioners: NPs are certified in one or more of six areas: family, gerontology, neonatal, pediatrics, women’s health, and psychiatric-mental health. Certification in family or pediatrics is the usual path to primary care practice.2 Published estimates indicate that 88% of NPs are certified in primary care and 70.3% deliver primary care.3

HRSA’s 2022 National Sample Survey of Registered Nurses (NSSRN) suggests the number of NPs working in primary care is much smaller than published estimates. The NSSRN collected data from 24,299 NPs that, with sample weights, represent 331,513 active NPs. The HRSA survey includes data on certification, self-reported specialty area for primary position, and setting type for primary position. NSSRN data show that many NPs certified in primary care work in roles or settings not considered primary care. Also, some NPs with a non-primary care certification work in primary care. Primary care NPs typically work in provider offices or outpatient clinics. Some NPs report doing primary care work in nursing facilities and residential care facilities.

We estimate 79,398 active primary care NPs in 2022. These include NPs who listed a specialty in the 2022 NSSRN of ambulatory care – primary care, pediatrics, or gerontology.

To estimate 2021 NP supply by specialty, state, and metropolitan/nonmetropolitan location, we start with the weighted number and distributions reported using primary nursing position held on December 31, 2017, as reported in the 2018 NSSRN restricted file.

Certified Nurse Midwives: CNMs are registered nurses that complete additional training to focus on women’s health—especially care related to childbirth. The 2022 NSSRN includes nurse midwives in the survey sample frame and has a variable identifying the respondent as a CNM. We also included certified midwives. This group has similar training to nurse midwives but does not have a nursing degree and so does not appear in the NSSRN. To account for certified midwives, we increased the number of midwives in New York by 108. According to the American College of Nurse-Midwives (which also represents certified midwives), all practicing certified midwives are in New York. Discussions with the Center for Health Workforce Studies in Albany, NY, found that there are currently 108 certified midwives in New York. At the national level, the total number of CMs and CNMs in 2021 was 14,704.

Certified Nurse Anesthetists: CRNAs are registered nurses who complete training in administering anesthesia to patients. The 2022 NSSRN also includes nurse anesthetists and has a variable identifying the respondent as a CRNA. The total number of CRNAs in 2022 calculated from the NSSRN was 57,999.

Modeling new entrants

The mechanism for modeling new APRN entrants to the workforce each year is the creation of a “synthetic” population of the profession based on the number and characteristics of recent graduates in each specialty. HWSM assigns each new APRN an age, sex, and specialty that reflects the distributions observed in the 2022 NSSRN.

Nurse Practitioners: The American Association of Colleges of Nursing (AACN) reports that 36,000 new NPs completed their academic programs in 2020-2021 4 which reflects rapid growth in the training of NPs. The annual number of new NPs completing an NP program has grown substantially over the past decade, and this growth could continue. However, a growing shortfall of RNs, a shortfall of nursing program faculty, and constraints on funded clinical training slots could slow the growth in number of new NPs trained annually.

We used the distribution of NPs across specialties from the NSSRN to proportionally allocate NP entrants into the 24 NP specialties (Exhibit VI‑2). Hence, if 10% of NPs were in a particular specialty then we model that 10% of new graduates will go into that specialty. An area for future research is the degree to which NPs are entering different specialties—as demand is likely to increase faster for specialties that treat older populations compared to pediatrics and specialties that disproportionately treat younger populations. To estimate the demographics of new graduates, we used NSSRN data on nurses who graduated from an NP program since 2010 for the sex distribution and since 2000 for the age distribution.

Certified Nurse Midwives: The American Midwifery Certification Board (AMCB) reports the number of certifications received each year for certified nurse midwives as well as certified midwives. In 2022, they reported 806 nurse midwife certifications and certified midwife certifications.5 Estimates of the percentage of new CNMs who are female and the age at completing their CNM program comes from analysis of the NSSRN. These estimates use data for CNMs who graduated since 2010 for the sex distribution and since 2000 for the age distribution.

Certified Nurse Anesthetists: The National Board of Certification and Recertification for Nurse Anesthetists (NBCRNA) certified 2,711 new nurse anesthetists in 2022.6 Estimates of the percentage of new CRNAs who are female and the age at completing their CRNA program comes from analysis of the NSSRN. These estimates use data for CRNAs who graduated since 2010 for the sex distribution and since 2000 for the age distribution.

Newly graduated APRNs have an estimated probability of starting their career in a particular state (Exhibit VI‑3). This state distribution is based on our analysis of the current geographic distribution of APRNs who graduated in the last 20 years using the 2022 NSSRN. More recent time frames (e.g., 10-year time frame) produced small sample sizes for less populated states.

Modeling workforce attrition

Advanced practice nurses can leave the workforce of their current occupation permanently by changing careers. Each year, nurses under age 50 have a low probability of being removed from the workforce based on observed career change using the 2022 NSSRN. Note that this does not include nurse practitioners changing their specialty. While these workers may join other health care occupations, we assume that they are captured in the new graduate data for that occupation.

HWSM simulates annual attrition probabilities for APRNs over age 50 based on provider age and APRN type or NP specialty. We created attrition probabilities based on APRN responses to the following 2022 NSSRN question:

  • Approximately when do you plan to retire from nursing?
    • Within a Year
    • In 1-2 years
    • In 3-5 years
    • More than 5 years from now
    • Undecided
    • Already retired

We used the responses “already retired”, “within a year”, and “in 1-2 years” as indications of imminent retirement and created a distribution of APRN retirement age for APRNs aged 50 to 74. We assume that all APRNs have retired by age 75. Because APRNs are approximately 90% female, there is insufficient data to create separate retirement patterns for males. We model APRN supply using retirement patterns for each of the 4 NP specialties, along with one for nurse midwives and one for nurse anesthetists. In Exhibit VI‑4, we show the probability for a 50-year-old APRN to be active for each age through 75 by APRN type and 3 specialties for NPs.

Modeling hours worked

The 2022 NSSRN was used to estimate weekly hours worked patterns by APRN age and specialty (Exhibit VI‑5). Due to the small sample of male APRNs in the 2022 NSSRN, HWSM applies the same hours worked patterns to both male and female APRNs for each age by specialty combination. The changing age distribution of APRNs has implications for future FTE supply.

Modeling geographic mobility

The model accounts for annual movement of APRNs between states in two steps. First, logistic regression7 using 2017-2021 American Community Survey (ACS) data estimates the probability of an APRN under the age of 50 migrating to another state based on their age group, sex, the population of the state from which the person moved, and a year indicator. Comparing each person’s move probability to a random number between 0 and 1 determines which APRNs move each year. The likelihood that each person who moves will relocate to a specific state is based on the proportion of APRNs moving to that state as observed in ACS data. For example, if 10% of APRNs who relocated, according to ACS, moved to California, then each APRN who moves in the simulation has a 10% probability of moving to California. This approach ensures that cross-state migration has no impact at the national level in terms of the number and characteristics of APRNs.

Second, when an APRN moves to a specific state, the model then assigns them a value of metropolitan or nonmetropolitan location designation equal to the proportion of APRNs practicing in metropolitan and non-metropolitan areas of the new state. For example, if 70% of APRNs in the state an NP is moving to work in a metropolitan area (per the 2022 NSSRN), then the moving APRN has a 70% probability of being assigned a value of metropolitan in the simulation because of the move.

Modeling demand

Consistent with the approach adopted for other health occupations modeled, the projected demand for APRNs is derived from the common model outlined in a prior module. Here, we provide additional details specific to modeling demand for APRNs.

In prior years, the projections started with the assumption that at the national level APRN demand equals supply in the starting year. Demand projections, therefore, are an extrapolation of current patterns of health care use as the population grows and ages as well as an extrapolation of current care delivery patterns. In the updated projections, we start with the assumption of a small shortfall of NPs at the national level to reflect the unanticipated increase in demand for healthcare services associated with COVID-19. Similar to the model assumptions made for physicians, nurses, and other health occupations, we assume that demand for hospital-based NPs increased by about 1.5% and demand for outpatient-based NPs in primary care and in select medical specialties that treat patients with acute COVID-19 and long COVID increased by about 1-2%. We also modeled a starting year shortfall for psychiatric NPs associated with 12.3% unmet demand for behavioral health services. For the NP workforce overall, the assumptions suggest a 2% starting year shortfall of NPs.

APRN demand projections for individual states reflect two additional assumptions. First, the state’s population receives a national average level of care adjusting for differences from the national average in patient characteristics and circumstances (e.g., demographics, disease prevalence, and insurance coverage). Second, the state has the same mix of APRNs, PAs, and physicians delivering care as the national average. When comparing APRN supply to demand, supply adequacy of physicians and PAs should be considered. Some states make greater use of APRNs than do other states. State differences in use of APRNs can reflect differences in state scope of practice laws that govern the level of practice autonomy.8 9 10 APRNs overlap with physicians for many of the services they provide. In primary care, as well as other specialty areas, increased availability of NPs is viewed as part of the solution to a shortfall of physicians.11 12 Hence, some states will make greater use of APRNs relative to the national average, while other states might make greater use of physicians and PAs relative to the national average.

Data limitations in several key data sources used in demand modeling presents challenges for modeling which patients seeking care are seen by an APRN. The Medical Expenditure Panel Survey (MEPS) only lists the highest-level provider seen. If the patient sees both an APRN and a physician during that visit, then MEPS only identifies the physician specialty seen. Ambulatory visits in MEPS, therefore, will undercount care provided by APRNs. Similarly, in billing records, many APRNs might bill under the national provider identification (NPI) of a physician in the office.

Another data limitation is the paucity of data on the setting location where APRNs practice. For some NPs, their specialty categorization is determined by where they provide care (e.g., Emergency Medicine, Home Health, Hospital Medicine, Nursing Home Health, School Health). For other specialties, there is less information on where services are provided because APRNs can split their time across care settings. Because of this limitation, for many specialties we model demand for APRNs using a ratio of APRNs-to-physicians of the same specialty (Exhibit VI‑6).

Thus, if the demand for primary care services resulted in total primary care physician demand growth of 10% under the Status Quo scenario, demand for NPs in primary care is also modeled to grow by 10%. The Status Quo scenario forces the APRN-to-physician staffing ratio to remain constant over the projection horizon.

Demand scenarios modeled

As with other health occupations modeled, HWSM models demand for APRNs under two scenarios as described in other modules and below:

  • The Status Quo scenario begins by extrapolating pre-COVID-19 (2015-2019) national patterns of care use to the future population. We then add estimated increases in demand for healthcare services as COVID-19 becomes endemic. This scenario captures geographic variation in demographics, health risk factors, disease prevalence, insurance coverage, level of rurality, and household income that can affect demand for physician services. Similarly, the scenario captures population growth and aging—and the associated implications for disease prevalence and other health risk factors—over the projection horizon. The scenario evaluates whether the nation’s future APRN workforce is sufficient to provide at least the current level of care. Interpretation of projections should be within the context that physicians and PAs overlap in scope of practice with APRNs. Hence, imbalances between supply and demand for APRNs should consider supply adequacy for physicians and PAs.
  • The Reduced Barriers scenario estimates the number of APRN FTEs required if populations that historically faced barriers to accessing health care services demonstrated care use patterns comparable to populations perceived to have fewer barriers to accessing care. The scenario modifies the health care use patterns of nonmetropolitan county residents, racial and ethnic minority populations, and people without health insurance to the health care use patterns of their peers living in metropolitan counties, who are non-Hispanic White, and who have health insurance. Each of the three components of this scenario (non-metropolitan versus metropolitan county of residence, minority versus non-Hispanic White, and uninsured versus insured) is modeled in isolation and together to quantify the magnitude of each factor on demand for services. This hypothetical scenario describes the implications on APRN demand if policies and programs reduced access-based disparities to health care services. The impact of reducing barriers to accessing care is modeled only for care provided in ambulatory settings and hospital settings. Reduced Barrier Demand projections for APRN services delivered in nursing homes, residential care facilities, and other settings (e.g., academia, public health) equal those of the Status Quo scenario.
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