Efforts to model the impact of changing demographics on the demand for and supply of health professionals incorporate many of the demographics trends discussed above as well as trends in economics, technology, the education system, regulation and legislative activities, the health care operating environment, and the ability to substitute between health professionals. Recent modeling efforts differ in level of sophistication, factors used to forecast future supply and demand, and assumptions made by analysts.

A consensus exists that the supply of physicians and nurses can be predicted with an adequate degree of accuracy even 10 or 20 years into the future (see, for example, Tarlov [1995] and Prescott [2000]). Previous efforts to model the requirements for health workers, on the other hand, have met with mixed success and often with controversy. As discussed above, efforts over the past two decades to model requirements show there is little consensus on how best to define requirements, the relationship between requirements and its determinants, the future values of many of these determinants, and forecasters' assumptions.

There is often disagreement regarding how requirements should be defined. For example, should requirements be defined by an assessment of the population's needs? Should requirements be based on demand and, if so, are current levels of employment accurate measures of demand? Should requirements be defined by benchmarking? For example, one could compare physician staffing levels to a level determined to be "efficient" (e.g., HMO staffing patterns). Alternatively, one could compare physician-per-population levels in the U.S. to levels in other countries. Or, should requirements be defined as some combination of demand, needs, and benchmarking? Despite these concerns and disagreements, supply and demand models are important tools to help analysts and policy makers understand the implications of trends and policies.

This section contains a brief description of two requirements forecasting models recently updated by BHPr-the Physician Aggregate Requirements Model (PARM) and the Nursing Demand Model (NDM)-and presents preliminary forecasts of the impact of changing demographics and other user-defined scenarios on requirements for the health professions in these two models. Both models define requirements as the number of health workers that the U.S. is likely to demand based on population needs and economic considerations.

Demographics, especially the growth in size of the elderly population, are the driving force behind most projections of future workforce requirements. Future demographics can be extrapolated with some degree of accuracy based on historical patterns of fertility rates, mortality rates and migration. The Census Bureau publishes its middle series projections that extrapolates future population levels based on expected fertility, mortality, and migration patterns. The Census Bureau last updated the series in 1996, and the middle series under-predicted the size of the 2000 population by approximately 6.8 million individuals (or 2.4 percent of the total population). The population projections used in the PARM and NDM are based on the Census Bureau's middle series projections, but incorporate adjustments based on recently released 2000 census data.

5.1 Physician Aggregate Requirements Model

The PARM combines projections of the future demand for health care services, by medical specialty and setting, with estimates of physician productivity to forecast future requirements. Exhibit 5.1 provides an overview of this process. For a more thorough description of the model and its capabilities see PARM User Guide and Technical Report (Dall, 2002). To calculate future demand for health care services, the PARM first combines population projections (Exhibit 5.2) by six age groups, three race/ethnicity groups, and sex (Box 1 of Exhibit 5.1) with estimates of the proportion of the population in each of three insurance categories (Box 2) to divide the population into 108 categories (Box 3). The six age categories are 0-17, 18-34, 35-54, 55-64, 65-74, and 75 years and older. The three race categories are non-Hispanic white, African American (Hispanic and non-Hispanic), and other (including white Hispanic). The three insurance categories are (1) the insured who receive services in a fee-for-service arrangement, (2) people enrolled in a health maintenance organization (HMO), and (3) the uninsured.

The PARM contains 22 categories of health professionals providing patient care. These categories consist of 19 physician specialties and three non-physician specialties (i.e., physical therapy, podiatry, and optometry). The process to forecast requirements is similar for both physicians and these three non-physician specialties, although the data sources differ.

The workload measures used in the PARM are physician-patient encounters in each of five settings: (1) doctors' offices, (2) hospital outpatient clinics and emergency departments, (3) hospital inpatient (hospital rounds), (4) hospital inpatient (surgery), and (5) other settings (e.g., nursing homes and home health). The PARM multiplies the number of people in each population category by its corresponding estimate of per capita physician-patient encounters (Box 4) to estimate total demand for physician services as measured by physician-patient encounters (Box 5). Estimates of total encounters in each setting (Box 5), multiplied by the average minutes physicians spend per encounter (Box 6), creates an estimate of total physician minutes required to provide patient care (Box 7). Note that the minutes per encounter include an adjustment for indirect patient care to capture time spent on tasks such as completing paperwork and reviewing patient histories.

Total required minutes (Box 7), divided by estimates of total annual patient care minutes per physician in each specialty (Box 8), creates forecasts of total physician requirements for patient care activities (Box 9). The data on physician-patient encounters and physician productivity come from the AMA annual survey and thus only include MDs. Consequently, an adjustment is made to the physician requirement counts to include DOs (Box 10). Data on the number of DOs in 1999, by specialty, come from the American Osteopathic Association. These numbers are inflated, using recent growth rates by DO specialty, to update the numbers to the base year of 2000. In addition, requirements for physicians in non-patient care activities (e.g., administration, teaching, and research) are calculated as a fixed percentage of physicians in patient care. Calibration adjustments are made to equate base year forecasts of actual physician supply with base year estimates of total requirements (Box 11), and this produces the refined forecasts of requirements for the 22 original specialties plus a category for physicians in nonpatient care activities. The base year for total MD counts is 2000.^[10] The base year counts of MDs come from the AMA's Physician Characteristics and Distribution in the US: 2002-2003 Edition. Active MDs whose specialty is unknown are distributed across the other specialties based on those specialties' proportion of total active physicians.The shaded boxes (i.e., boxes 2, 4, and 6) indicate areas of the PARM where the user can easily change the forecasting assumptions.

Exhibit 5.1 PARM Structure


Exhibit 5.2 U.S. Population Forecasts (in thousands)

Source: Modified version of Census Bureau middle series projections.

The base year for the PARM is 2000; however, data from 1996 to 2000 are pooled from some health care use databases to increase sample size. Data from the 1999 National Health Interview Survey (NHIS) are used to estimate the proportion of people in each demographic category among three possible insurance status groups.

5.1.1 Modeling Physician Requirements

To estimate per capita demand for physician services from each of the 108 population groups in the PARM, we first estimated the total amount of care that physicians in each specialty provide in each setting. We estimated these totals using AMA estimates for 1999 of the total number of MDs in each medical specialty primarily engaged in patient care, and data from the 1998 and 1999 AMA physician surveys that asked respondents the average number of weeks worked per year and average encounters (i.e., visits or surgical procedures) per week with patients. These data come from the 1999-2000 and 2000-2002 editions of the Physician Socioeconomic Statistics. Published statistics from the 1998 and 1999 surveys were averaged because sample sizes for some specialties are relatively small.

We used the following databases to determine the distribution of total patient-physician encounters across the 108 population subgroups:

• The 1997, 1998, and 1999 National Ambulatory Medical Care Survey (NAMCS) databases were pooled to analyze patient-physician encounters in physicians' offices.
• The 1997, 1998 and 1999 National Hospital Ambulatory Care Survey (NHAMCS) databases were pooled to analyze patient-physician encounters in hospital outpatient and emergency department settings.
• The 1997 and 1998 Health Care Cost and Utilization Project (HCUP) databases were pooled to analyze patient-physician encounters in hospital inpatient settings.
• The 1996 and 1998 National Home and Hospice Care Survey (NHHCS) databases were pooled to analyze patient-physician encounters in patients' homes.

As illustrated in Exhibit 5.1, we combine information on per capita demand for physician services obtained from an analysis of these databases with population forecasts and estimates of annual physician time spent in patient care to forecast future requirements for physicians.

Below we present forecasts of physical requirements under five scenarios. We selected these scenarios based on policies being advocated in the political arena and scenarios looked at in previous modeling efforts. In all of these scenarios, changing demographics-and in particular the aging of the population-are a major determinant of the projected increase in physician requirements between 2000 and 2020. Comparing the forecasts from a particular scenario to the forecasts from scenario 1 (which represents the status quo) indicates the impact upon physician requirements attributed to changing demographics and/or changes in forecasting assumptions.

• Scenario 1, the status quo, assumes that patterns of health care use, insurance distribution, physician staffing, and physician productivity remain constant over time similar to the patterns that existed in the late 1990s.^[11] As discussed above, the PARM assumes that an adequate supply of physicians existed in the base year (i.e., 2000). An over (or under) supply of physicians in the base year will result in an over (or under) estimate of requirements in future years. Patterns of health care use cover the period 1996 to 1999.Under this scenario, the number of physicians would increase from approximately 781,300 in 2000 to 1,038,200 in 2020, a 33 percent increase (Exhibits 5.3 and 5.4). At the same time, the U.S. population would increase by 19 percent, so that the ratio of physician per population would rise from 2.8 per thousand population in 2000 to 3.1 per thousand population in 2020. Medical specialties experiencing the largest percentage increases in demand between 2000 and 2020 are cardiovascular diseases (52 percent), radiology (51 percent), pathology (44 percent) and various surgical specialties (44 percent). Medical specialties experiencing the smallest percentage increases in demand are pediatrics (11 percent), obstetrics/gynecology (14 percent) and psychiatry (22 percent).

Exhibit 5.4 Forecasts of Physician Requirements Under the Status Quo Scenario

Exhibit 5.4 Forecasts of Physician Requirements Under the Status Quo Scenario (Text Only)

• Scenario 2, baseline, produces the requirements forecasts that are most likely to occur based on projected trends in managed care growth and the shifting of care from higher cost to lower cost settings. This scenario is comparable to the baseline scenario in the NDM, described later in Section 5.2, which assumes that HMO enrollment rates will increase by half a percentage point per year between 2000 and 2020 (with the gains in HMO enrollment coming from the population insured under a fee-for-service arrangement). In addition, this scenario assumes that each year, 2 of inpatient-based surgeries will shift to an outpatient setting. Regression analyses conducted to update the NDM find that for each 1 increase in the proportion of hospital-based surgeries performed on an outpatient basis, demand for inpatient days at acute care hospitals will decline by 0.47, outpatient visits will increase by 0.66, and home health visits will increase by 0.86. Using this information, the baseline scenario assumes a gradual decrease in per capita demand for inpatient days and surgery performed on an inpatient basis, and a gradual increase in outpatient visits and "other" visits. Exhibit 5.5 presents the forecasts for this scenario. Under this scenario, total requirements for physicians would increase by 28 percent between 2000 and 2020 to 996,400. Compared to the status quo scenario, there would be the same level of growth in general primary care specialties (28 percent), but slower growth in medical specialties (33 percent versus 40 percent), surgical specialties (17 percent versus 32 percent), and "other" patient care specialties (32 percent versus 36 percent).

Exhibit 5.5 Impact of Changing Demographics on Requirements for Physicians: Baseline Scenario

• Scenario 3, universal health care coverage, assumes that the entire U.S. population has medical insurance. Under this scenario, the PARM moves a portion of the uninsured population into the insured fee-for-service and HMO settings based on the current proportion of the insured population in each of those two settings. The primary motivation for this scenario is that some advocates for the uninsured would like to see the Government sponsor more initiatives to cover the uninsured. Under this scenario, total demand for physicians would have been an estimated 817,615 in 2000, and would increase to an estimated 1,092,400 in 2020-a 40 percent increase from current (2000) baseline and/or status quo levels (Exhibits 5.6, 5.7, and 5.8). (It should be noted that under the status quo scenario, although substantially short of universal coverage, the percentage of population with medical insurance will rise over time as the population ages and a larger proportion of the population becomes Medicare-eligible.)
• Scenario 4 is universal health care coverage with 100 of the population enrolled in a health maintenance organization. The motivation for this scenario is work performed by Weiner (1994) and others on requirements for physicians under a managed care environment. Under this scenario, total physician requirements would have been an estimated 781,900 in 2000 and would increase to 1,059,900 in 2020-a 36 percent increase from current levels (Exhibits 5.6, 5.7, and 5.8).
• Scenario 5, non-minority rates, assumes that minorities have similar rates of medical insurance coverage as non-Hispanic whites within each demographic group defined by age and sex. Under this scenario the percentage of the population uninsured, insured under a fee-for-service arrangement, and in an HMO applicable to non-Hispanic whites is applied to the other two race/ethnicity groups. The motivation for this scenario is equality across racial and ethnic groups in access to medical coverage. Under this scenario, demand for physicians would have been an estimated 802,400 in 2000, increasing to 1,072,000 in 2020-a 37 percent increase from current levels (Exhibits 5.6, 5.7, and 5.8).

Exhibit 5.6 Forecasted Physician Requirements Under Five Scenarios

Exhibit 5.7 Forecasts of Physician Requirements in 2000 Under Alternative Scenarios  

Exhibit 5.7 Forecasts of Physician Requirements in 2000 Under Alternative Scenarios (Text Only)

Exhibit 5.8 Forecasts of Total Physician Requirements in 2020 Under Alternative Scenarios

Exhibit 5.8 Forecasts of Total Physician Requirements in 2020 Under Alternative Scenarios (Text Only)

5.1.2 Modeling Requirements for Physical Therapists, Optometrists, and Podiatrists

The PARM also models requirements for physical therapists, optometrists, and podiatrists. These three specialties are modeled using the same approach as physicians, but rely on different data sources. The following data sources are used to model demand for physical therapists:

• Data from the 2000 Occupational Employment Statistics (OES), which are published by the Bureau of Labor Statistics (BLS), provide information on the total number of physical therapists in 2000. In addition, the BLS reports the total hours per week worked, and weeks per year worked, on average, for physical therapists:
• The American Physical Therapy Association estimates that physical therapists spend approximately 13.9 percent of their time in inpatient settings (Vector Research Inc., 1997). Multiplying this percentage by the estimate of the total number of physical therapists as published by the BLS produced an estimate of the number of FTE physician therapists working in inpatient settings.
• An analysis of the 1996 Medical Expenditure Panel Survey (MEPS) provided additional information on the distribution of visits with physical therapists by delivery setting, but the sample sizes were insufficient to estimate the distribution of visits across the 108 population groups in the PARM. Consequently, we pooled data from the 1998, 1999, and 2000 NHIS on people who reported a visit with a physical therapist to distribute our estimate of total physical therapist visits across the 108 population groups. ^[12] To estimate the distribution of visits to physical therapists, we analyzed the demographics and insurance status of NHIS survey participants who responded in the affirmative to the question of whether during the past 12 months they had seen or talked to any one of the following health workers: physical therapists, speech therapists, respiratory therapists, audiologists, or occupational therapists.

The following data sources and steps describe the approach used to forecast requirements for optometrists:

• A major source of data on optometrists is a paper by White, Doksum and White (2000) entitled "Workforce Projections for Optometry." These authors analyzed survey data and report information on the current size of the optometrist workforce, patient encounters and associated time requirements, and demographic characteristics of patients. In addition, these analysts provide estimates of the total hours per week worked in patient and non-patient care.
• One important data item not included in the White et al. paper was a breakdown of the hours spent by optometrists in different patient care settings. An examination of the 1998 BMAD (Part-B Medicare Annual Data) beneficiary file, which provides information on Medicare Part-B carriers, provided some information on the distribution of patients by practice setting. Although Medicare patients make up only a small percentage of total visits to optometrists, we used the distribution of practice setting from optometrists who saw Medicare patients to approximate the overall distribution of optometrists' time by delivery setting. This was a less than perfect remedy, but it does not alter the accuracy of the forecasts except with regard to practice setting. This is because the data on the actual productivity of the optometrists, which is based on minutes per visit and total patient care hours worked, comes directly from the White et al. survey. Productivity is assumed equal among all practice settings.
• Although the White et al. paper provides information on patient demographics, the information is insufficient to distribute total visits across the 108 population groups in the PARM. Like the analysis for physical therapists, we pooled data from the 1998, 1999, and 2000 NHIS on people who reported a visit to an eye doctor (including optometrists and ophthalmologists) to distribute our estimate of total optometrist visits across the 108 population groups. We used a similar approach to estimate base year visits to podiatrists and then extrapolate future requirements for podiatrists.
• The American Podiatric Medical Association (APMA) provides data on total visits to podiatrists per year, as well as the total number of FTE podiatrists in the current workforce. ^[13] American Podiatric Medical Association, http://www.apma.org/faqgeneral.html. APMA also publishes data indicating the hours per week, weeks per year, and visits per week of the typical podiatrist.
• To distribute total visits across the 108 population groups in the PARM, we pooled data from the 1998, 1999, and 2000 NHIS on people who reported a visit to a foot doctor.
• Finally, to create a distribution of visits over practice settings, BMAD Medicare data were analyzed in a similar fashion to that used for optometrists. The totality of these data sources proved sufficient to create baseline estimates for podiatrist visits by demographic group, and the order of the procedures undertaken was analogous to that used for optometrists.

Exhibit 5.9 shows the requirements projected for these three professions. In 2000, there were an estimated 120,410 physical therapists, 30,468 optometrists, and 13,320 podiatrists. Under the status quo scenario, the number of physical therapists, optometrists, and podiatrists will increase by 18 percent, 20 percent, and 28 percent, respectively, between 2000 and 2020. Exhibit 5.10 shows the projected requirements under the five scenarios described previously.

Exhibit 5.9 Impact of Changing Demographics on Requirements for Physical Therapists, Optometrists, and Podiatrists

Exhibit 5.10 Forecasted Requirements for Physical Therapists, Optometrists, and Podiatrists Under Alternative Scenarios

5.2 Nursing Demand Model

The Nursing Demand Model forecasts demand for RNs, LPNs and nurse aides by delivery setting and State through 2020 based on projected changes in demographics and other factors that affect patterns of health care use and nurse staffing. Below is a brief description of the recently revised NDM and preliminary forecasts that show the impact of changing demographics and other determinants on nurse demand. For a more detailed description of the NDM, the data used in the NDM, the assumptions that go into the model and the forecasts, see The Nursing Demand Model: Development and Baseline Forecasts (Dall and Hogan, 2002).

The NDM uses an eclectic approach to forecast demand that combines empirical analysis with input from health care experts regarding how the health care system operates and the role of nurses in the delivery of care. The purpose of the model is to forecast future demand for health care services in different delivery settings, and then to forecast the number of FTE RNs, LPNs, and nurse aides in each setting to meet the projected demand for nursing services. The NDM forecasts demand for nurses at the State level and then aggregates these numbers to obtain a national estimate. The NDM seeks to answer four questions:

1. What will be the future health care demands of the population?
2. Where will patients receive health care services?
3. What level of nursing services will patients require?
4. Who will provide these nursing services?

Modeling Demand for Health Care

The following steps produce forecasts for inpatient days in short-term (ST) and long-term (LT) hospitals, outpatient and emergency department visits in ST hospitals, nursing facility residents, and home health visits:

• Step 1, combine State-level population forecasts with national estimates of per capita health care utilization to extrapolate expected demand for health care services. For each of the six health care delivery settings modeled, there are 32 per capita utilization rates applied to 32 population strata divided into eight age categories, by sex, and by urban or rural location. The eight age categories are ages 0-4, 5-17, 18-24, 25-44, 45-64, 65-74, 75-84, and 85 and older. Then, apply per capita utilization rates from the base year (1996) to extrapolate demand for health care services. Demand is measured in terms of inpatient days, outpatient visits, and emergency department visits in hospitals; home health visits; and nursing facility residents. This first step controls for variation across States and over time in demographics.
• Step 2, adjust up or down these initial extrapolations of health care demand in each State and year based on projected changes in the health care operating environment, economic conditions, and the overall health of the population. This step creates a more refined forecast of future demand for health care services. The relationship between demand for health care and its determinants (e.g., HMO enrollment rates, changes in technology), after controlling for demographics, was estimated using multiple regression analysis.
• Step 3, calibrate the model by calculating multiplicative adjustment factors that equate base-year forecasts of health care demand with the base-year estimates of actual demand, and then apply these State-level adjustment factors to the forecasts.

Modeling Nurse Staffing Intensity

The following steps produce forecasts of staffing intensity measured in terms of FTE nurses per inpatient day, per visit, per nursing facility resident, or per population depending on the nurse type and setting modeled.

• Step 1, apply the projections of future health care market conditions and other determinants of staffing intensity (e.g., relative wages of RNs, LPNs, and nurse aides, patient acuity levels, and reimbursement rates for health care services) to the 22 forecasting equations-one for each nurse-type-by-setting combination-to create preliminary estimates of staffing intensity. These forecasting equations were estimated by regressing nurse staffing intensity on various determinants.
• Step 2, calibrate the model by calculating multiplicative adjustment factors that equate base-year forecasts of staffing intensity with the base-year estimates of actual staffing intensity, and then apply these State-level adjustment factors to the forecasts.

Combining estimates of future demand for health care services (e.g., demand for inpatient care in ST hospitals as measured in total inpatient days) with forecasts of future staffing intensity (e.g., FTE nurses per 1,000 inpatient days) creates the demand forecasts.

The majority of the forecasting equations were estimated using multiple regression analysis with State-level data from 1996 through 2000 (although most regression equations were estimated using a subset of these years based on data availability). Both theory and empirical analysis helped determine which exogenous variables to use in the forecasting equations. Three criteria considered in selecting variables are (1) a logical relationship between the exogenous variable and the dependent variable, (2) the impact of the exogenous variable on the dependent variable is statistically significant, and (3) forecasts of the exogenous variables are readily available or can be reliably extrapolated into the future.

The revised NDM runs as a stand-alone program to be run on a personal computer in a Windows environment. The model, like the PARM, allows the user to change assumptions regarding the future determinants of nurse demand.

Exhibit 5.11 Overview of the Nursing Demand Model

Data on nurse staffing levels during the base year come from multiple sources. The estimates of FTE RNs come from the 1996 Sample Survey of RNs. Estimates of LPNs and nurse aides come from the Bureau of Labor Statistics (BLS) Occupational Employment Statistics (OES), the American Hospital Association (AHA) annual survey, and the American Health Care Association (AHCA). Data that describe the current and future trends in the health care operating environment, patient acuity levels, economic conditions, etc., and that are used to forecast future health care utilization patterns and nurse staffing patterns come from publications from various government agencies and private organizations. The NDM assumes that the labor market for nurses was in equilibrium in 1996 (the base year) with the exception of hospitals. The NDM uses employment levels in 1996 as a demand-based measure of nurse requirements, but increases requirements for RNs in hospitals by 7 percent above employment levels. The reason for this adjustment is based on analyses of the 1992, 1996, and 2000 Sample Surveys of RNs that show a significant decrease in the proportion of RNs in hospitals between 1992 and 1996-possibly as a result of extensive cost-cutting measures and hospital mergers that occurred during the early 1990s (Dall and Hogan, 2002). Hospitals in many parts of the U.S. have been unable to fill vacant RN positions reopened after these turbulent times for RNs in hospitals.

The forecasts presented below show the increase in projected demand for nurses under a status quo scenario where there is no change in per capita health care utilization rates (within the 32 demographic groups) and no change in nurse staffing ratios (Exhibit 5.12). This scenario is comparable to the status quo scenario used to forecast physician requirements using the PARM. These projections simply show the impact of changing demographics on the demand for health care services.^[14] Projections under this scenario assume no change in average patient acuity for hospital inpatient days, visits, and nursing facility residents. Under this scenario, changing demographics will result in a projected 28 percent increase in demand for RNs between 2000 and 2020, a 32 percent increase in demand for LPNs, and a 37 percent increase for nurse aides. The areas with the largest percentage growth are those that predominantly serve the elderly: home health and nursing facilities (Exhibit 5.13).

Note that these forecasts of total nurse requirements under the status quo scenario are lower than The NDM baseline scenario forecasts which incorporate trends in factors other than changing demographics that affect future demand for nurses (Exhibits 5.14 and 5.15). The NDM's baseline forecast predicts an increase in total FTE RN requirements from 2 million in 2000 to 2.8 million in 2020 (a 41 percent increase), an increase in total FTE LPN requirements from 618,000 in 2000 to 905,000 in 2020 (a 46 percent increase), and an increase in FTE nurse aide and home health aide requirements from 1.5 million in 2000 to 2.3 million in 2020 (a 50 percent increase). Demand for nurses and nurse aides will continue to grow in hospitals during the next two decades, but at a slower rate than for the nursing professions as a whole. The exception is the strong growth in demand for RNs in hospital outpatient settings as technological innovations and managed care trends shift patients from inpatient to outpatient care.

Under the baseline scenario, the aging of the population and resulting increase in demand for geriatric care suggests large increases in demand for nurses and nurse aides in home health and nursing facilities. Demand for RNs, LPNs and NAs in home health is projected to increase by 109 percent, 137 percent, and 67 percent, respectively, between 2000 and 2020. Demand for RNs, LPNs and NAs in nursing facilities is projected to increase by 66 percent, 66 percent, and 61 percent, respectively, between 2000 and 2020.

Exhibit 5.12. Forecasts of FTE Nurse Demand: Status Quo Scenario

Exhibit 5.12. Forecasts of FTE Nurse Demand: Status Quo Scenario (Text Only)

Exhibit 5.13. Forecasts of FTE Nurse Demand: Status Quo Scenario

Exhibit 5.14. Forecasts of FTE Nurse Demand: Baseline Scenario

Exhibit 5.14. Forecasts of FTE Nurse Demand: Baseline Scenario (Text Only)

Exhibit 5.15 Forecasts of FTE Nurse Demand: Baseline Scenario

Executive Summary | Introduction | Aging of the Population | Changing Racial and Ethnic Composition of the Population | Geographic Location of the Population | Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals | Summary and Conclusions | References