Published 2025
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. Data and assumptions pertinent to modeling the APRN workforce are summarized in this module.
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. Each APRN type is discussed 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.
There are an estimated 83,638 active primary care NPs in 2023. These include NPs who listed a specialty in the 2022 NSSRN of ambulatory care—primary care, pediatrics, or gerontology.
To estimate 2023 NP supply by specialty, state, and metropolitan/nonmetropolitan location, the weighted number and distributions reported using primary nursing position held on December 31, 2021, as reported in the 2022 NSSRN restricted file, are used as the starting point. Those numbers are then adjusted using the change in NP employment from the 2022 to 2023 Occupational Employment and Wage Statistics (OEWS) data reported by the Bureau of Labor Statistics.
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. Certified midwives are also included. 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, the number of midwives in New York were increased by 108. According to the American College of Nurse-Midwives (which also represents certified midwives), all practicing certified midwives are in New York. There are approximately 192 certified midwives in New York per New York State Education Department. (K. Murphy, personal communication, June 12, 2025) At the national level, the total number of CMs and CNMs in 2023 was 13,229. Other categories of midwives such as Certified Professional Midwives (CPMs) are not included.
Certified Registered 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 2023 calculated from the NSSRN and OEWS was 64,995.
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 39,000 new NPs completed their academic programs in 2021-2022 which reflects rapid growth in the training of NPs.4 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 the number of new NPs trained annually.
The distribution of NPs across specialties from the NSSRN was used to proportionally allocate NP entrants into the 23 NP specialties (Exhibit VI‑2). Hence, if 10% of NPs were in a particular specialty then 10% of new graduates are modeled to 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, NSSRN data on nurses who graduated from an NP program since 2010 is used 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 2023, they reported 740 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 come 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 Registered Nurse Anesthetists: The National Board of Certification and Recertification for Nurse Anesthetists (NBCRNA) certified 2,866 new nurse anesthetists in 2024.6 Estimates of the percentage of new CRNAs who are female and the age at completing their CRNA program come 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 (for example, 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 leaving 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, it is assumed 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. This is done using the methods described in the chapter II (Supply Modeling Overview) section “Estimating Worker Attrition” with specifics unique to APRNs described below.
Attrition probabilities were created 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
The responses “already retired,” “within a year,” and “in 1-2 years” were used as indications of imminent retirement and created a distribution of APRN retirement age for APRNs aged 50 to 74. It is assumed 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. APRN supply is modeled using retirement patterns for each of the NP specialties, along with one for nurse midwives and one for nurse anesthetists. In Exhibit VI‑4, the probability for a 50-year-old APRN to be active for each age through 75, by APRN type and three specialties of NPs, is shown.
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 2019–2023 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 to which an NP is relocating work in a metropolitan area (per the 2022 NSSRN), then in the simulation, the relocating APRN has a 70% probability of being assigned a metropolitan designation due to 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, additional details specific to modeling demand for APRNs are provided.
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 becoming endemic. Similar to the model assumptions made for physicians, nurses, and other health occupations and as described in the demand overview module, it is assumed that demand for hospital-based NPs increased by about 1.27% 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 1.09-1.21%. For the NP workforce overall, the assumptions suggest a 1% starting year shortfall of NPs.
APRN demand projections for individual states reflect two additional assumptions. First, each state’s population is assumed to receive a national average level of care, adjusted for differences from the national average in patient characteristics and circumstances (for example, demographics, disease prevalence, and insurance coverage). Second, each state is assumed to have the same mix of APRNs, physician assistants (PAs), and physicians delivering care as the national average. When comparing APRN supply to demand, the adequacy of physician and PA supply should be considered. States vary in their reliance on APRNs, partly due to differences in scope of practice laws that determine their level of practice autonomy.8 9 10 APRNs provide many of the same services as physicians, particularly in primary care and other specialty areas, and increased NP availability is often seen as part of the solution to physician shortages.11 12 Consequently, some states will make greater use of APRNs relative to the national average, while others may rely more heavily on physicians and PAs.
Data limitations in several key data sources used in demand modeling present 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 the same 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 (for example, 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 demand for APRNs is modeled 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 assumes the APRN-to-physician staffing ratio remains 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 national patterns of care use (2018-2022) to the future population. Estimated increases in demand for healthcare services as COVID-19 becomes endemic are then added. 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 healthcare 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 be considered with 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 (for example, academia, public health) equal those of the Status Quo scenario.