In this module:
This module summarizes the data sources and assumptions for projecting supply and demand for primary care physicians, nurse practitioners (NPs) and physician assistants (PAs). The initial year for modeling is 2018, the latest year for which many of the supply and demand data sources are available, with projections through 2030. Physician specialties categorized under primary care providers are family medicine, general internal medicine, geriatric medicine, and general pediatrics. Physicians trained under these primary care specialties but who are practicing as hospitalists are excluded from these workforce projections. A 2020 report by the Association of American Medical Colleges estimates that in 2018 there were approximately 32,200 primary care-trained hospitalists in practice.1 Primary care NPs and PAs are identified based on their primary practice specialty and setting collected through surveys.
Identifying primary care providers in association databases, licensure files, and surveys is complicated by three factors:
- Many providers self-report more than one specialty area—e.g., physicians who list one specialty as internal medicine and a second specialty as an internal medicine subspecialty like cardiology. It must be determined who is likely in primary care versus who has subspecialized and not counted in primary care.
- Many providers report the health care delivery setting in which they work. The setting is sometimes inconsistent with the specialty—e.g., specialty is listed as primary care but the work setting is listed as emergency department or hospital inpatient care.
- Area of certification sometimes differs from practice specialty—e.g., many NPs certified in family medicine or pediatrics report working in a specialty area such as cardiology or a non-primary care setting such as a hospital inpatient setting.
The research team reached out to key stakeholders with the goals to identify the best available data sources, discuss trends affecting primary care supply and demand, and provide the opportunity for feedback on preliminary findings. The research team met with the organizations listed in Exhibit 11. The information provided in this technical documentation and in HRSA reports does not necessarily reflect the views of the organizations that participated in the workforce study. Also, there may not be clear consensus on all study assumptions and findings.
- 1Dall TM, Reynolds R, Chakrabarti R, Jones K, Iacobucci W. 2020 Update, The Complexities of Physician Supply and Demand: Projections from 2018 to 2033 (PDF - 3 MB). IHS Markit report prepared for the Association of American Medical Colleges; 2020.
This module describes the data and assumptions for modeling supply and demand. The microsimulation modeling approach for projecting future supply and demand for primary care providers and other health workers is described in Modeling Supply of Health Professionals and Modeling Demand for Health Care Services and Providers, respectively.
Supply modeling consists of:
- estimating the number and characteristics of current supply
- modeling the number and characteristics of new entrants to supply
- modeling attrition
- modeling workforce behavior like hours worked patterns and geographic mobility
In addition to the Status Quo scenario, we also model a “graduates trend” scenario for APRNs and PAs. This model attempts to project the number of new entrants instead of using the most recent historical numbers for all future years.
Estimating the Current Active Workforce Supply
Estimates of starting year (2018) supply of primary care providers came from multiple sources.
Physicians: The 2018 American Medical Association (AMA) Master File provides data for estimating the number, characteristics, and geographic location of primary care physicians. The HWSM analysis is limited to physicians who have completed their residency and whose status is listed as ‘active’. Active is defined by AMA as working 20 or more hours per week in professional activities.
The AMA file is known to misclassify older physicians who have retired as active. This can be due to time lags between when physicians retire and when their status is updated in the AMA file. To address this issue, we omitted physicians age 75 and older from the estimate of starting supply. Without making this adjustment, projections of future supply would drop during the first few years as the status of many older physicians changed to ‘retired’ in the simulation model. This is because the age distribution of physicians currently in the AMA file is inconsistent with an age distribution indicated by retirement patterns.
Starting supply also removes estimates of physicians trained in primary care but who work as hospitalists. An analysis by AAMC, summarized in a recent report2 , estimates that approximately 32,200 physicians trained in primary care were practicing as hospitalists in 2018. AAMC identified practicing hospitalists trained in primary care by using national provider identification to combine data from 2018 Medicare fee-for-service billing records with the AMA Masterfile. AAMC categorized as hospitalists any physicians where 90% or more of their Evaluation and Management billing is hospital-based.
Identifying primary care physicians in the AMA Masterfile considered both the first and second recorded specialty. The physician is categorized as primary care if the physician’s first specialty is in primary care and their second specialty is
- another primary care specialty
- or did not fall within one of the other 32 medical or surgical categories in HWSM
The estimated supply of family physicians in 2018 consisted of 105,470 FTEs in family medicine, 81,739 FTEs in general internal medicine, 60,820 FTEs in general pediatric medicine, and 8,230 FTEs in geriatric medicine. These specialties represent about 28% of the physician workforce in 2018.
Estimates by state and by metropolitan/nonmetropolitan location are based on primary practice location address in the AMA Masterfile. When that field is not available (the case for less than 5% of the records), estimates are based on the physician’s mailing address.
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.3 Published estimates indicate that 87% of NPs are certified in primary care and 73% deliver primary care.4
HRSA’s 2018 National Sample Survey of Registered Nurses (NSSRN) suggests the number of NPs actually working in primary care is much smaller than published estimates. The NSSRN collected data from 2,487 NPs that, with sample weights, represent 258,241 active NPs. The HRSA survey included 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 are working 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.
Applying the following rules, we estimate 67,515 active primary care NPs in 2018:
- The NP listed primary care as his/her specialty (n=48,449). Half (51%) of these NPs listed their practice setting as private medical practice (office, clinic, etc.). Most of the other NPs listed public clinic, hospital-sponsored ambulatory care, school or university health service, or work in a nursing home or other institutional setting. About 3% identified their setting as emergency department, hospital inpatient, or disease management/case management but were still categorized as primary care based on their self-reported specialty.
- The NP listed his/her specialty as ambulatory care. The setting is also consistent with where primary care services are provided like private medical practice, public clinic or community health, school health services, or nurse managed health center (n=18,320).
- The NP listed “other specialty (gerontology)” as their specialty (n=565). Most of these NPs work in long term care facilities or nursing homes. Others work in public clinics and other settings.
- The NP is not categorized as primary care if self-reported specialty is one of the following:
- general medical surgical
- critical care
- cardiac or cardiovascular care
- chronic care
- emergency or trauma care
- home health/hospice
- infectious/communicable disease
- labor and delivery
- occupational health
- psychiatric or mental health (substance abuse and counseling)
- pulmonary/respiratory, radiology (diagnostic or therapeutic)
- other specialty
- other specialty (neonatology)
- other specialty (school health service).
- Although NPs (and physicians and PAs) in these specialties might spend part of their time providing primary care services to patients, these clinicians are not counted as part of the primary care workforce for purposes of this study.
- The NP is not categorized as primary care if:
- the self-reported specialty is “ambulatory care (including primary care outpatients settings, except surgical)”
- and the setting is listed as a hospital-related setting, ambulatory surgery center, or another setting that typically does not provide primary care.
NSSRN data show that approximately 26% of active NPs in 2018 were practicing in primary care. About 59% of NPs report having certification in a primary care related area (Family, Pediatric, or Gerontology) but the majority of these NPs report specialties other than primary care.
Estimates by state and by metropolitan/nonmetropolitan location are based on location of the primary nursing position held on December 31, 2017 as reported in the NSSRN restricted file. The constructed starting year file used for modeling primary care NP supply consists of 67,515 constructed records, reflecting the 67,515 NPs in the NSSRN, with NP age, sex, state, and metropolitan/nonmetropolitan location.
Physician assistants: Estimates of the number, characteristics, and location of PAs practicing primary care come from two data sources—2018 data from the National Commission on Certification of Physician Assistants (NCCPA), and 2018-2019 data from the American Academy of Physician Assistants (AAPA). NCCPA files contain data on all certified PAs, and NCCPA’s Professional Profile Survey collects data on practice specialty. AAPA membership files are a subset of certified PAs. AAPA survey data combined with membership files provided greater flexibility to categorize primary care PAs as working in a metropolitan or nonmetropolitan area and also provide demographic information on PAs. Consequently, we used NCCPA data to estimate the total number of active PAs in each state. We used AAPA data to estimate the proportion of active PAs in primary care, the proportion working in a metropolitan or a nonmetropolitan area, and PA demographics.
The 2018 Statistical Profile of Certified Physician Assistants by Specialty is the source for estimating the supply of primary care PAs in 2018.5 Certified PAs are categorized under primary care based on self-reported current practice area of principal clinical position. Practice areas categorized as primary care are family medicine/general practice, general internal medicine, and general pediatrics. We also include the small number of PAs in geriatric medicine, which accounts for about 0.8% of PA supply, though these PAs are categorized under internal medicine subspecialties in NCCPA reports.
Taking into consideration the response rate to NCCPA surveys and the percentage of certified PAs who are in non-clinical positions, retired, or working outside the U.S., we estimate active 2018 primary care supply at 33,323. This represents 27% of the estimated active PAs working in the U.S. This includes 23,751 PAs in family medicine, 5,865 in general internal medicine, 2,711 in general pediatrics, and 996 in geriatric medicine. For supply modeling, all primary care PAs are modeled together rather than by primary care specialty.
The constructed starting year file used for modeling primary care PA supply consists of 33,323 records (reflecting 33,400 FTEs) with PA age, sex, state, and metropolitan/nonmetropolitan location.
Modeling New Entrants
The mechanism for modeling new 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 occupation and specialty. Each new clinician is assigned an age and sex that reflect the distribution seen in recent years. Estimates of total annual new physicians, NPs, and PAs and the characteristics of new graduates came from multiple sources.
Physicians: Published studies of graduates from programs accredited by the Accreditation Council for Graduate Medical Education (ACGME) and the American Osteopathic Association (AOA) suggest approximately 8,360 physicians completed primary care residencies in the 2018-2019 school year and entered the primary care workforce.6 7 This represents 29% of new physicians trained. It excludes an estimated 1,221 primary-care-trained physicians who will practice as hospitalists as well as physicians who will complete a fellowship to subspecialize.
The number of physicians being trained and entering primary care is largely constrained by the number of funded residency positions. The supply projections model the continuation of 8,366 new physicians entering the workforce annually absent legislation and funding to expand graduate medical education pipeline capacity. This includes 3,456 new family physicians, 2,973 general internists, 1,683 general pediatricians, and 254 geriatricians annually.
Estimates of the proportion of new physicians by sex and age came from analysis of the 2018 AMA Physician Masterfile using data on physicians who completed their training since 2010 for the sex distribution and since 2000 for the age distribution (Exhibit 12).
Nurse practitioners: The American Association of Colleges of Nursing (AACN) reports that 28,700 new NPs completed their academic programs in 2017-2018. This is an increase from 23,000 new NPs completing their academic programs in 2015-2016.8
Analysis of HRSA’s 2018 NSSRN suggests that 25.1% of new NPs practice in primary care—equivalent to about 7,210 new primary care NPs in 2018. The size of the NP training pipeline has grown substantially over the past decade.9 A shortfall of nursing program faculty and constraints on funded clinical training slots could slow that growth.
Estimates of the percentage of new primary care NPs by sex and the age at completing their NP program come from analysis of the NSSRN. They use data on NPs who graduated from an NP program since 2010 for the sex distribution and since 2000 for the age distribution (Exhibit 12). The annual number of new NPs completing an NP program has grown substantially over the past decade, and this number likely will continue into the future.
Physician assistants: NCCPA reports 9,287 physician assistants were newly certified in 2018. NCCPA surveys new graduates to ascertain the specialty area for PAs who have accepted a position and targeted specialty area for PAs who have not yet accepted a position. Specialty is defined as the specialty of the physician(s) with whom the PA primarily interacts.
The proportion of PAs seeking to work in primary care is similar for both PAs who have accepted and those who have not yet accepted a new position. These survey data suggest that 24% of new PAs are practicing in primary care.10 The number of PAs completing training each year has grown from year to year, and this growth trend is projected to continue.
The Physician Assistant Education Association (PAEA) reports that in 2018 there were 236 PA programs with average enrollment of 109.4 students versus average capacity of 118.9 students.11 Furthermore, PAEA reports that in the 2017-2018 school year there were 9,202 graduating students and 10,814 first-year students. There was an average graduating class size of 44.2 and average first year class size of 47.8. Exhibit 12 summarizes the number, age and sex distribution of new entrants to the primary care workforce.
- 2Dall TM, Reynolds R, Chakrabarti R, Jones K, Iacobucci W. 2020 Update, The Complexities of Physician Supply and Demand: Projections from 2018 to 2033 (PDF - 3 MB). IHS Markit report prepared for the Association of American Medical Colleges; 2020.
- 3APRN Consensus Work Group and the National Council of State Boards of Nursing APRN Advisory Committee. Consensus Model for APRN Regulation: Licensure, Accreditation, Certification & Education.; 2008. https://www.ncsbn.org/public-files/Consensus_Model_for_APRN_Regulation_July_2008.pdf (PDF - 269 KB)
- 4American Association of Nurse Practitioners. NP Fact Sheet. American Association of Nurse Practitioners; 2019.
- 5National Commission on Certification of Physician Assistants. 2018 Statistical Profile of Certified Physician Assistants by Specialty (PDF - 4 MB). NCCPA; 2019.
- 6Brotherton SE, Etzel SI. Graduate Medical Education, 2018-2019. JAMA. 2019;322(10):996. doi:10.1001/jama.2019.10155.
- 7Martinez B, Biszewski M. Appendix: Osteopathic Graduate Medical Education, 2019. J Am Osteopath Assoc. 2019;119(4):268. doi:10.7556/jaoa.2019.044.
- 8American Association of Nurse Practitioners. 2018-2019 Enrollment and Graduations in Baccalaureate and Graduate Programs in Nursing. AANP; 2019.
- 9Enrollment and Graduations in Baccalaureate and Graduate Programs in Nursing, Annual Issues 2008 - 2018. American Association of Colleges of Nursing.
- 10The 2,258 estimate of new primary care PAs consists of 1,614 PAs working with family physicians, 337 PAs working with general internists, 54 PAs working with geriatricians, and 253 physicians working with pediatricians. For supply modeling, however, all primary care PAs are modeled together rather than by primary care specialty.
- 11p34: Physician Assistant Education Association. Program Report 34: Data from the 2018 Program Survey. (PDF - 3 MB); 2019. Accessed March 18, 2021.
The state where new providers practice is simulated based on probabilities summarized in Exhibit 13. The state distribution for newly trained primary care physicians and NPs is based on our analysis of the current geographic distribution of primary care physicians and NPs who have graduated in the last 20 years using, respectively, the 2018 AMA Masterfile and 2018 NSSRN. NCCPA reports the distribution across states of all newly accredited PAs. We use this distribution as a proxy for the distribution of newly trained primary care PAs.
Modeling Workforce Attrition
HWSM simulates provider attrition probabilities based on provider age, sex, and occupation. Probabilities the provider remains active after age 50 are summarized in Exhibit 14.
Physicians: Attrition probabilities for primary care physicians are based on self-reported expected retirement age for 2,400 primary care physicians who participated in AAMC’s 2019 National Sample Survey of Physicians (NSSP). To conduct this survey, AAMC contracted with Toluna, an external firm that recruited active physicians from proprietary panels of healthcare professionals. The survey started February 25, 2019 and concluded March 25, 2019 upon reaching the desired quota of 6,000 participants. Survey responses were weighted to be representative of practicing physicians by specialty, age group, sex, and International Medical Graduate status consistent with the 2018 AMA Master File.
NSSP findings indicate that physicians intend to retire earlier than in previous HRSA reports. We assume that all physicians have retired by age 90—though, in reality, very few remain active past age 75. Many of those older physicians have reduced work hours per week.
Nurse practitioners: Attrition probabilities for primary care NPs are based responses to the following question in HRSA’s 2018 NSSRN:
- 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
- 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 NP retirement age for NPs age 50 to 74. We assume that all NPs have retired by age 75. Because NPs are approximately 90% female, there is insufficient data to create separate retirement patterns for males. NPs supply is modeled using a single retirement pattern.
Physician assistants: Attrition probabilities for primary care PAs are based on responses to the question in AAPA’s 2015 National Survey of PAs asking whether the PA plans to retire in the next three years. We created a retirement age distribution of PAs age 50 to 74 based on answers to this question. We assume that all PAs have retired by age 75. The sample of older primary care PAs in this survey is insufficient to obtain reliable estimates of retirement intention by individual age, so the retirement patterns used for modeling are based on all PAs—with separate patterns for male and female PAs.
Modeling Hours Worked
Hours worked in professional activities differ systematically by provider age, sex, and occupation (Exhibit 15). These changing demographics of the primary care workforce have implications for future FTE supply. Hours worked patterns were modeled using Ordinary Least Squares regression with self-reported total weekly hours worked as the dependent variable. The survey data also included information on patient care hours worked. For consistency with health occupations modeled previously, we use total hours worked for modeling.
Explanatory variables consisted of age group, sex, and age group by sex interaction. Metropolitan/nonmetropolitan location was included as a dichotomous variable in preliminary analysis. It was omitted from the final regressions as the effect is small and statistically insignificant. Survey data collected in 2018 and 2019 were used to estimate hours worked patterns.
Physicians: AAMC’s 2019 NSSP asked 2,334 primary care physicians how many hours they worked during their last typical week of work (excluding any week with leave). The survey also asked physicians the percent of time spend on activities (patient care, teaching, research, administration, other) as well as weeks worked per year. We found little difference in hours worked patterns for male and female physicians or by physician age in weeks worked per year. As such, we used weekly hours worked for modeling. Primary care physicians worked 44.7 hours per week, on average. Of this, 36.7 (82%) were for patient care activities. Using 40 hours per week to define an FTE, total FTE physician supply in 2018 is approximately 12% higher than total active supply (44.7/40=1.1175).
Nurse practitioners: HRSA’s 2018 NSSRN asked the 6,893 NPs that we categorized in primary care the number of hours worked in a typical week for their primary nursing position. The weighted mean of weekly hours worked is 41.5 hours. There were statistically significant differences in weekly hours worked across age groups and by sex. The interaction terms by age group and sex were not statistically significant. This is likely due to the small sample of male NPs. The only statistically significant differences in hours worked by Census region is that NPs in the West work about 1.2 fewer hours per week than NPs in the Midwest., NPs in the Northeast and South work about the same number of hours per week as their peers in the Midwest. Total FTE primary care NP supply in 2018 is approximately 4% higher than total active supply (41.5/40=1.0375).
Physician assistants: AAPA’s 2019 Salary Survey collected data on hours worked per week for the primary employer for 1,597 PAs who reported a specialty of Family Medicine, General Internal Medicine, Pediatrics, or Geriatrics. Average hours worked per week were 43.3. PAs worked similar hours in the Midwest, South, and West. They worked about 1.9 fewer hours in the Northeast (though not statistically significant). There is no statistically significant difference in hours worked by PAs in metropolitan versus nonmetropolitan areas.
Hours worked declined starting around age 55. Hours were lower for female PAs compared to male PAs. Reported hours for NPs and PAs were lower than for physicians. One reason might be that the NP and PA surveys only collected hours worked data for the primary employer. Total FTE primary care PA supply in 2018 is approximately 8% higher than total active supply (43.3/40=1.0825).
Modeling Cross-State Migration
HWSM accounts for annual movement of primary care physicians, PAs, and NPs across states. This is accomplished in two steps. First, a logistic regression estimates the probability of migrating to any other state for the under 50 population as a function of age group, sex, race, the state’s population, and a year indicator. For physicians, we also used OES data on physician salary by state as an explanatory variable. The state salary and state population were scaled by a factor of 100,000 and 1,000,000, respectively. The physician salary data were adjusted for state cost of living using data from the Bureau of Economic Analysis.
The main data source for physician migration is the National Plan and Provider Enumeration System (NPPES) for 2019 and 2020. The data source for NPs and PAs is the 2014-2018 ACS. Using multiple years of NPPES data allowed us to identify physicians who changed states by merging the two years using the physician’s national provider identification (NPI). The ACS contains a question asking respondents what state they lived in one year ago. The NPPES data does not contain the provider’s age, so we merged it with Medicare data that had NPI and graduation year. We also assume that physicians are age 28 at graduation from medical school. We compare each person’s move probability to a uniform random number between 0 and 1 to simulate whether a physician moves to a new state each year over the projection horizon.
The likelihood that each person moving will relocate to a specific state is based on the proportion of people moving to that state as observed in NPPES or ACS data. For example, if NPPES shows 5.8% of general internists who relocated between 2019 and 2020 ended up in Florida, then each general internist who moves has a 5.8% probability of moving to Florida in the HWSM.
When a primary care provider moves to a specific state, HWSM then tags that provider by the level of rurality of the area in which s/he practices. The provider’s rurality designation is based on the current rurality distribution of the workforce in the state. For example, if in a state 50% of pediatricians work in a large core metro location, then each pediatrician moving to that state has a 50% probability of being assigned as working in a large core metro area. All primary care physicians have the same probability to relocate and the same distribution of relocation states. These inputs are created from combined data on family medicine, general internal medicine, pediatrics, and geriatrics.
The projected demand for physicians, NPs, and PAs is derived from the common model outlined in Modeling Demand for Health Care Services and Providers. Demand for primary care services is modeled under a Status Quo scenario that extrapolates current patterns of care use to the projected population through 2020. A hypothetical Reduced Barriers scenario is modeled to quantify what demand for health care services and providers would be if there were improved access to care by populations that historically faced barriers to receiving health care services. These populations include people without health insurance, people living in rural or under-served communities, and racial and ethnic minority populations.
Demand for primary care providers is largely tied to projected demand for ambulatory visits in private or public medical practices (e.g., provider offices and health clinics). A portion of primary care services is provided to populations living in nursing homes, residential care facilities, and correctional facilities. Limited data exist to model primary care use patterns for populations in institutional settings. For modeling purposes, we assume that demand for primary care services by people in institutional settings is the same as by people living in the community with similar demographics, health risk factors, and disease prevalence.
Prediction equations for use of office and outpatient services were estimated using negative binomial regression with 2013-2017 MEPS data. Separate regressions were estimated for children and adults, for office visits and outpatient/clinic visits, and by physician specialty. The dependent variables were annual office visits and annual outpatient visits for each specialty. Explanatory variables consisted of the patient characteristics, socioeconomic and insurance variables, and health status variables described previously.
The Status Quo scenario extrapolates current patterns of care use and delivery into the future. However, one study reports that there were declining rates of primary care visits between 2008 and 2016 among commercially insured adults.14 Visit rates declined particularly for low acuity conditions (such as those that could be addressed at a retail clinic). The largest declines were among young adults without chronic conditions and those living in lower-income areas. Visit rates to urgent care clinics increased during this period.
Primary care-trained hospitalists are omitted from both the primary care FTE demand and FTE supply projections. Still, some primary care physicians spend a portion of their time conducting hospital rounds. HWSM accounts for this in the demand projections. The workload drivers for growth in demand for hospital rounds delivered by physicians in family medicine, general internal medicine, pediatric medicine, and geriatric medicine are:
- total hospital days (all ages)
- total hospital days for adults (age 18 and older)
- total hospital days for children (under age 18)
- total hospital days for patients age 75 and older (Exhibit 16)
Between 2018 and 2030, the growth in hospital days for these four populations is 18%, 20%, 6%, and 56%, respectively.
Current national estimates of the workload measures for primary care services and FTE physician distribution are shown in Exhibit 17.
- 14Ganguli I, Shi Z, Orav EJ, Rao A, Ray KN, Mehrotra A. Declining Use of Primary Care Among Commercially Insured Adults in the United States, 2008–2016. Annals of Internal Medicine. 2020;172(4):240-247. doi:10.7326/M19-1834
Data limitations in MEPS, NPPES, and other sources present challenges in estimating total services provided by NPs and PAs in primary care. MEPS, for example, only records the provider with the highest degree seen. If a patient saw only an NP or PA during a primary care visit, the visit would have been recorded as a visit to an NP or PA. However, if a patient saw both a physician and an NP or PA, the visit only would have been recorded as a visit to a physician. Similarly, in billing records, many NPs and PAs might bill under the NPI of a physician in the office. The small number of visits in MEPS where an NP or PA was listed as the service provider prevented using regression analysis to model visits to NPs or PAs.
Because of these limitations in identifying which visits involved care provided by an NP or PA, we modeled growth in demand for NPs and PAs under the Status Quo scenario as the same percentage growth as demand for physicians. Thus, if the demand for primary care services resulted in total primary care physician demand growth of 10%, then demand for NPs and PAs is modeled to grow by 10% under the Status Quo scenario. This modeling assumption implies that the NP-to-physician and PA-to-physician staffing ratio remains constant over the projection horizon.
As discussed in the findings report, the supply of NPs and PAs is growing at a much faster rate than the growth in demand for primary care services. Per the Status Quo scenario, additional supply beyond that required to maintain the Status Quo can be used to:
- help offset projected shortfalls in physician demand
- increase access to primary care services by helping reduce barriers to receiving care
- improve the comprehensiveness of primary care services provided to patients
- Regarding the final bullet, a published 2012 study estimated that primary care physicians working alone could provide patients with only 55% of recommended chronic and preventive services due to time constraints during patient visits.17 The authors provided estimates of work that primary care physicians could delegated to other members of a primary care team, with the conclusion that additional availability of NPs and PAs could allow for more comprehensive services to be provided to patients while allowing physicians to care for a manageable-sized panel of patients.
- 17Altschuler J, Margolius D, Bodenheimer T, Grumbach K. Estimating a Reasonable Patient Panel Size for Primary Care Physicians With Team-Based Task Delegation. The Annals of Family Medicine. 2012;10(5):396-400. doi:10.1370/afm.1400