Changing Demographics: 

Implications for 

Physicians, Nurses, and Other 

Health Workers 

Spring 2003 

U.S. Department of Health and Human Services 

Health Resources and Services Administration 

Bureau of Health Professions 

National Center for Health Workforce Analysis 

 
 
TABLE OF CONTENTS 

EXECUTIVE SUMMARY ..........................................................................................................................................i

1 INTRODUCTION ................................................................................................................................................1

2 AGING OF THE POPULATION.......................................................................................................................5

2.1 POPULATION FORECASTS .................................................................................................................................6

2.2 IMPLICATIONS OF AN AGING POPULATION FOR THE DEMAND FOR HEALTH WORKERS....................................7

2.2.1 Increasing Demand for Health Care Services .......................................................................................7

2.2.2 Increasing Demand for Health Workers..............................................................................................21

2.3 IMPLICATIONS OF AN AGING POPULATION FOR THE SUPPLY OF HEALTH WORKERS ......................................27

2.3.1 Physician Supply..................................................................................................................................28

2.3.2 Nurse Supply........................................................................................................................................31

2.4 IMPLICATIONS OF AN AGING POPULATION FOR THE ECONOMICS OF THE HEALTH CARE SYSTEM..................33

3 CHANGING RACIAL AND ETHNIC COMPOSITION OF THE POPULATION ...................................37

3.1 POPULATION FORECASTS ...............................................................................................................................38

3.2 IMPLICATIONS OF THE CHANGING RACIAL AND ETHNIC COMPOSITION OF THE POPULATION FOR THE 

DEMAND FOR HEALTH WORKERS ..................................................................................................................39

3.3 IMPLICATIONS OF THE CHANGING RACIAL AND ETHNIC COMPOSITION OF THE POPULATION FOR THE SUPPLY 

OF HEALTH WORKERS ...................................................................................................................................47

3.3.1 Physician Supply..................................................................................................................................49

3.3.2 Nurse Supply........................................................................................................................................55

4 GEOGRAPHIC LOCATION OF THE POPULATION ................................................................................57

4.1 POPULATION PROJECTIONS AND REGIONAL GROWTH PATTERNS ..................................................................57

4.2 EVOLVING TRENDS IN URBANIZATION...........................................................................................................59

4.3 URBAN DEMOGRAPHY AND THE EFFECTS ON PHYSICIAN LOCATIONS ...........................................................62

5 MODELING THE IMPACT OF CHANGING DEMOGRAPHICS ON THE FUTURE DEMAND FOR 

HEALTH PROFESSIONALS...........................................................................................................................63

5.1 PHYSICIAN AGGREGATE REQUIREMENTS MODEL..........................................................................................64

5.1.1 Modeling Physician Requirements ......................................................................................................68

5.1.2 Modeling Requirements for Physical Therapists, Optometrists, and Podiatrists ................................76

5.2 NURSING DEMAND MODEL............................................................................................................................79

6 SUMMARY AND CONCLUSIONS.................................................................................................................89

REFERENCES ..........................................................................................................................................................95 

Executive Summary 

EXECUTIVE SUMMARY 

The size and characteristics of the future health workforce are determined by the complex 

interaction of the health care operating environment, economic factors, technology, regulatory 

and legislative actions, epidemiological factors, the health care education system and 

demographics. Efforts over the past several decades to model the supply of and demand for 

health workers show there is a lack of consensus on the relationship between the health 

workforce and its determinants, the future values of many of these determinants, and forecasters’ 

assumptions. 

The Workforce Analysis Branch of the Bureau of Health Professions (BHPr), Health Resources 

and Services Administration (HRSA), commissioned a report synthesizing the literature on one 

set of factors that will have a profound impact on the future health workforce—changing 

demographics—and discussing its implications for the health workforce. In addition, BHPr 

commissioned the update of two requirements forecasting models: the Physician Aggregate 

Requirements Model (PARM) and the Nursing Demand Model (NDM). The major findings of 

the literature and these two demand models are the following. 

Population Aging 

• If health care consumption patterns and physician productivity remained constant over time, 

the aging population would increase the demand for physicians per thousand population from

2.8 in 2000 to 3.1 in 2020. Demand for full-time-equivalent (FTE) registered nurses per 

thousand population would increase from 7 to 7.5 during this same period. 

• In 2000, physicians spent an estimated 32 percent of patient care hours providing services to 

the age 65 and older population. If current consumption patterns continue, this percentage 

could increase to 39 percent by 2020. 

• The aging of the health workforce raises concerns that many health professionals will retire 

about the same time that demand for their services is increasing. Furthermore, the declining 

proportion of the population age 18 to 30 raises concerns regarding the ability to attract a 

sufficient number of new health workers. 

 i
Executive Summary 

• The rise in health care expenditures associated with the rapid increase in the elderly 

population will likely place additional pressures on the Medicaid and Medicare programs, as 

well as private insurers, to control health care costs. Such measures would likely decrease the 

demand for and supply of health professionals. 

• The aging population could result in rising average patient acuity, which could in turn require 

higher nurse and physician staffing levels. One countervailing trend is that tomorrow’s 

elderly might have lower disability rates than today’s elderly, controlling for age, because of 

improvements in economic resources, education levels, lifestyle, public health, and medical 

technology. 

Increasing Racial and Ethnic Diversity 

• The literature suggests that Hispanics and non-whites have different patterns of health care 

use compared to non-Hispanic whites. Disparities in access to care account for part of the 

difference in utilization. 

• Demand for health care services by minorities is increasing as minorities grow as a 

percentage of the population. Between 2000 and 2020, the percentage of total patient care 

hours physicians spend with minority patients will rise from approximately 31 percent to 40 

percent. 

• Minorities are underrepresented in the physician and nurse workforce relative to their 

proportion of the total population. As minorities constitute a larger portion of the population 

entering the workforce, their representation in the physician and nurse professions will 

increase. The U.S. will increasingly rely on minority caregivers. 

• Minority physicians have a greater propensity than do non-minority physicians to practice in 

urban communities designated as physician shortage areas. An increase in minority 

representation in the physician workforce could improve access to care for the population in 

some underserved areas. 

 ii
Executive Summary 

Geographic Location of the Population 

• Geographic variation in population growth rates and in determinants of health worker 

demand and supply highlight the importance of developing forecasting models that can make 

State-level and sub-State level forecasts. 

• Although an increasing proportion of the U.S. population resides in urban areas, a substantial 

proportion of the population will continue to reside in rural areas. Many of these rural areas 

are currently designated as physician shortage areas. 

• Pockets of urban areas will continue to have a high concentration of minorities. Many of

these areas are currently designated as physician shortage areas. Efforts to increase the 

supply of health professionals in these areas must deal with economic, cultural and language 

considerations. 

Forecasting the Impact of Changing Demographics and Other Factors on Physician 

Requirements 

The PARM forecasts requirements for allopathic (MD) and osteopathic (DO) physicians 

providing patient care in 19 specialties as well as physicians in non-patient-care activities. 

Requirements are demand-based and rely on current and forecasted patterns of health care use, 

physician staffing patterns, and medical insurance prevalence rates. We consider forecasts under 

five scenarios (Exhibit ES.1). 

• Scenario 1, Status Quo, forecasts physician requirements under the assumption that patterns 

of health care use, medical insurance coverage, and physician productivity remain constant 

over time. Under this scenario, total requirements for physicians would increase from

approximately 781,300 in 2000 to 1,038,200 in 2020 (a 33 percent increase). 

• Scenario 2, Baseline, is our best estimate of demand for physicians based on changing 

demographics and projected trends in the other factors (e.g., insurance coverage and 

economic considerations). Under this scenario, physician requirements would increase to 

996,400 in 2020 (a 28 percent increase). 

 iii
Executive Summary 

• Scenario 3, Universal Coverage, assumes that the entire U.S. population has medical 

insurance. Under this scenario, the uninsured population is placed into the insured fee-forservice and health maintenance organization (HMO) settings based on the current proportion 

of the insured population in each of those two settings. Under this scenario, total demand for 

physicians would have been an estimated 817,600 in 2000, increasing to an estimated 

1,092,400 (a 40 percent increase over the 2000 baseline level). 

• Scenario 4 is universal health care coverage with 100 percent of the population enrolled in a 

health maintenance organization. Under this scenario, total requirements would have been an 

estimated 781,900 in 2000, increasing to an estimated 1,059,900 in 2020 (a 36 percent 

increase over the 2000 baseline level). 

• Scenario 5, Non-minority Rates, assumes that minorities have rates of medical insurance 

coverage similar to non-Hispanic whites within each demographic group defined by age and 

sex. Under this scenario, demand for physicians would have been an estimated 802,400 in 

2000, increasing to an estimated 1,072,000 in 2020 (a 37 percent increase over the 2000 

baseline level). 

Exhibit ES.1 Forecasted Physician Requirements

Scenario 2000 2020

1: Status Quo 781,282 1,038,234 

2: Baseline 781,282 996,387 

3: Universal Coverage 817,615 1,092,381 

4: 100 percent HMO 781,889 1,059,907 

5: Non-minority Rates 802,356 1,072,048 

The PARM also forecasts requirements for three non-physician specialties: physical therapy, 

podiatry, and optometry. Based on available data and studies, the requirements for all three 

professions are projected to increase, between 2000 and 2020, at rates equal to or slightly greater 

than the growth in population. 

 iv
Executive Summary 

Forecasting the Impact of Changing Demographics and Other Factors on Nurse 

Requirements 

The NDM forecasts demand-based requirements for FTE registered nurses (RNs), licensed 

practical nurses (LPNs), nurse aides and home health aides (NAs). Although the NDM forecasts 

requirements at the State level, in this report we present only national-level forecasts (Exhibit 

ES.2). Under a baseline scenario, which represents the forecasts most likely to occur based on 

changing demographic and projected trends in other determinants of nurse demand, total 

requirements for FTE RNs would increase from approximately 2 million in 2000 to 2.8 million 

in 2020 (a 41 percent increase). Requirements for FTE LPNs would increase from 618,000 in 

2000 to 905,000 in 2020 (a 46 percent increase). There would also be 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 results 

from strong growth in demand for RNs in hospital outpatient settings as technological 

innovations and managed care trends shift patients from inpatient to outpatient care. The fastest 

growth in demand will occur in nursing facilities and home health. Under a status quo scenario 

where patterns of per capita health care use and nurse staffing remain constant over time, the 

requirement for nurses and nurse aids increases at a slower rate than under the baseline scenario. 

Exhibit ES.2 Forecasted FTE Nurse Requirements

Baseline Scenario 

Status Quo 

Scenario 

2000 2020 2020

Registered nurses 2,001,198 2,822,388 2,505,747 

Licensed practical nurses 617,946 905,159 787,329 

Nurse aides and home health 

aides 1,545,722 2,323,518 1,983,582 

Findings from the PARM and NDM, as well as the literature review, provide important insights 

on the impact of changing demographics on the health workforce. This report also identifies 

 v
Executive Summary 

areas for additional research such as (a) factors changing the per capita use of health care 

services, (b) the paucity of information on the relationship between race/ethnicity and the supply 

of health workers, and (c) the need for models that can forecast demand for and supply of health 

workers at smaller geographic units of aggregation (e.g., at the sub-State level). 

 vi
Section 1: Introduction

1 INTRODUCTION 

The size and characteristics of the future health workforce are determined by the complex 

interaction of the health care operating environment, economic factors, technology, regulatory 

and legislative actions, epidemiological factors, the health care education system and 

demographics. Efforts over the past several decades to model the supply of and demand (or 

“requirements”) for health workers show there is a lack of consensus on the relationship between 

the health workforce and its determinants, the future values of many of these determinants, and 

forecasters’ assumptions.1

 Furthermore, past forecasts of impending surpluses and shortages of 

health professionals often failed to materialize, leading to the general consensus that a much 

better understanding is needed about the dynamics affecting the supply of and demand for health 

professionals. 

The Workforce Analysis Branch of the Bureau of Health Professions (BHPr), Health Resources 

and Services Administration (HRSA), commissioned a report synthesizing the literature on one 

set of factors that will have a profound impact on the future health workforce—changing 

demographics. In addition, BHPr commissioned the updating of two requirements forecasting 

models: the Physician Aggregate Requirements Model (PARM) and the Nursing Demand Model 

(NDM). 

This report discusses findings from the literature review of the implications of important 

demographic trends for the health workforce. In addition, this report presents findings from the 

NDM and PARM to quantify the impact of changing demographics on demand for allopathic 

(MD) and osteopathic (DO) physicians, registered nurses (RNs), licensed practical nurses 

(LPNs), nurse aides and home health aides (NAs), physical therapists, optometrists, and 

podiatrists. This report also presents forecasts from the PARM and NDM for several scenarios 

with different assumptions regarding the future health care operating environment, the 

productivity of doctors and nurses, and other factors. 

 

1

 See, for example, recent articles by Snyderman, Sheldon and Bischoff (2002), Weiner (2002), Grumbach (2002) 

and Reinhardt (2002) commenting on recent physician workforce projections by Cooper et al. (2002). Prescott 

(2000) discusses the lack of consensus as it pertains to modeling the nurse workforce. 

1 
Section 1: Introduction

Although the demographic trends discussed here have implications for the entire health 

workforce, the discussion in this report is heavily tilted towards the physician and nursing 

professions. Reasons for this focus include the dominance of these professions in the health 

workforce literature, the focus on these professions by government commissions and policy 

makers, and the availability of the PARM and NDM for forecasting requirements for physicians 

and nurses. 

Demographics are a major determinant of the size and characteristics of the future health 

workforce, and demographic trends can be extrapolated with reasonable accuracy one or two 

decades into the future. In addition to the growth in size of the U.S. population in future decades, 

three demographic trends have profound implications for the future health workforce: 

• First, the population is aging and the size of the elderly population will increase substantially. 

An aging population will place greater demands on the health care system at the same time 

that many health professionals will be retiring. Also, as the population ages there will be a 

continuing shift in the type and setting of services provided. 

• Second, the population is becoming more racially and ethnically diverse. Concerns that 

minorities are underrepresented in the health workforce have both equity implications for 

people who need health care services and efficiency implications for the health care system. 

As minorities constitute a larger proportion of persons entering the workforce, the U.S. 

population will increasingly rely on minority health workers for their care. 

• Third, the population is shifting geographically and a significant portion of the U.S. 

population will continue to reside in areas with persistent shortages of health workers. These 

trends highlight the need for forecasting models that can make State-level and sub-State-level 

forecasts of health worker supply and demand. 

Other demographic trends with implications for the future supply of and demand for health 

workers include changes in fertility patterns, family size and composition, longevity, 

immigration, and overall health of the population. These trends are discussed within the context 

of the three major trends discussed above. 

2 
Section 1: Introduction

In both the PARM and NDM, requirements are defined as the number of health professionals 

demanded based on the level of health care services that society is willing to purchase given 

population needs and economic considerations. Other authors have used “need” to define 

requirements, where need is based on the analyst’s assessment of what constitutes an adequate 

supply of health workers, independent of society's willingness or ability to purchase services. 

Using the PARM and NDM, we forecast future demand for health care services and the derived 

demand for 19 physician specialties, nurses, and the other health workers listed previously. We

forecast a “status quo” scenario that assumes no change in per capita health care utilization 

patterns, health worker productivity, and health worker staffing patterns. Under such a scenario, 

between the years 2000 and 2020, changing demographics would cause an estimated 30 percent 

increase in inpatient days, a 20 percent increase in outpatient visits, and a 17 percent increase in 

emergency department visits at general, short-term hospitals. Inpatient days at non-general and 

long-term hospitals would increase by an estimated 33 percent; the number of nursing facility 

residents would increase by 40 percent; the number of home health visits would increase by 36 

percent; and the number of visits to physicians’ offices would increase by 23 percent. 

The change in demand for health care services would increase requirements for physicians by 

approximately 33 percent, although the increase in requirements would vary by medical 

specialty. For example, requirements for cardiologists would increase by an estimated 52 percent 

while requirements for pediatricians would increase by an estimated 11 percent. Requirements 

would increase approximately 28 percent for RNs, 30 percent for LPNs, and 33 percent for nurse 

aides (including home health aides).

Although demographics are a dominant determinant of the demand for health workers, other 

important factors are the characteristics of the future health care system, economic 

considerations, technological advances, and population needs. A detailed discussion of these 

trends is outside the scope of this project; however, the extant literature in this area is relatively 

3 
Section 1: Introduction

large.2 Using the PARM and NDM, we forecast future requirements for selected health care 

professions under alternative scenarios regarding the future health care operating environment. 

The baseline scenario in both the PARM and NDM produce the forecasts that are most likely to 

occur based on changing demographics and projected trends in the factors listed above (e.g., 

trends in insurance coverage and economic considerations). The baseline forecasts for physician 

requirements are slightly lower than under the status quo scenario (28 percent growth between 

2000 and 2020 instead of 33 percent growth), and the change in requirements for individual 

physician specialties is quite different in some cases. Under the NDM’s baseline scenario, 

requirements for RNs grow faster than under the status quo scenario (41 percent growth between 

2000 and 2020 instead of 28 percent growth), reflecting different assumptions about changes in 

average patient acuity levels and other factors. Under the baseline scenario, total requirements 

for LPNs, nurse aides, and home health aides rise faster than forecasts under the status quo 

scenario. 

The remaining sections in this report discuss the implications for the health workforce of the 

aging population (Section 2), the changing racial and ethnic composition of the population 

(Section 3), and population geographic location (Section 4). Each of these sections presents 

information on the demographic trend, discusses the implications of the trend on demand for 

health care services and derived demand for health workers, and discusses the implications for 

the supply of health workers. Section 5 describes the recently updated PARM and NDM and 

presents findings from these models. Section 6 summarizes the main findings of this effort and 

discusses areas for additional research.

 

2

 The report: The Impact of the Restructuring of the U.S. Health Care System on the Physician Workforce and

Vulnerable Populations (The Lewin Group, 1998), contains a literature review that discusses many of these trends. 

4 
Section 2. Aging of the Population

2 AGING OF THE POPULATION 

Major Findings: 

• If health care consumption patterns and

physician productivity remained constant

over time, the aging population would 

increase the demand for physicians per 

thousand population from 2.8 in 2000 to

3.1 in 2020. Demand for full-timeequivalent RNs per thousand population 

would increase from 7 to 7.5 during this

same period. 

• In 2000, physicians spent an estimated 32 

percent of patient care hours providing 

services to the age 65 and older 

population. If current consumption 

patterns continue, this percentage could 

increase to 39 percent by 2020. 

• The aging of the health workforce raises

concerns that many health professionals 

will retire about the same time that 

demand for their services is increasing. 

Also, the elderly population will grow at

faster rate than the working-age 

a

population. 

• 

the

e

ase the

supply of health 

professionals. 

The rise in health care expenditures 

associated with the rapid increase in

elderly population will likely place 

pressures on the Medicaid and Medicar

programs to control health care costs. 

Such measures would likely decre

demand for and

Increased longevity and the aging of the baby boom generation will contribute to a substantial 

increase in the size of the elderly population during the 

next few decades as well as the aging of the overall 

population. Four major implications of an aging 

population on the health workforce are the following. 

One, because the elderly have both greater and different 

health care needs than the non-elderly, the rapid growth 

in size of the elderly population could substantially 

increase overall demand for health care services and 

consequently the derived demand for health workers. 

Occupations and settings that disproportionately serve 

the elderly will experience the largest growth. If health 

care consumption patterns and physician productivity 

remained constant over time, the aging population 

would increase the demand for physicians per thousand 

population from 2.8 in 2000 to 3.1 in 2020. Demand for 

full-time-equivalent (FTE) RNs per thousand population 

would increase from 7 to 7.5 during this same period. 

Two, physicians will spend an increasing proportion of 

their time treating the elderly. Our analysis of multiple 

health care use databases suggests that in 2000 

physicians spent an estimated 32 percent of total patient 

care hours providing services to the age 65 and older 

population. If current patterns continue, this percentage could increase to 39 percent by 2020. 

Three, the health workforce is aging along with the general population. As health professionals in 

the baby boom generation retire and as the pool of potential entrants to the health workforce (i.e., the 

population age 18 to 30) declines as a percentage of the total population, there is concern that the 

future supply of health professionals will be inadequate to meet demand. 

5 
Section 2. Aging of the Population

Four, the expected increase in health care expenditures attributed to the growing elderly population 

will likely place pressures on the Medicaid and Medicare programs to control health care costs. The 

ratio of working-to-retired Americans will likely decrease, placing budget pressures on other 

government programs that compete with funding for Medicaid and Medicare. Economic pressures to 

curb the growth in health care costs could result in policies to reduce the demand for and supply of 

health workers. 

2.1 Population Forecasts 

Census Bureau population projections show significant shifts in the age distribution (Exhibit 2.1) 

with the number of elderly increasing in absolute size and as a proportion of the total population 

(Exhibit 2.2). The number of elderly, defined as the “age 65 and over” population, will grow by over 

50 percent between 2000 and 2020, and by an estimated 127 percent by 2050. Furthermore, the 

relative size of the elderly population is projected to increase from 12.6 percent of the population in 

2000 to an estimated 16.5 percent in 2020. Between 2030 and 2050, one in five Americans will be 

elderly. 

The most rapidly growing demographic group among age categories is the “oldest elderly.” This 

group is sometimes defined differently by researchers, but the most common definitions are the 

population age 75 and over, age 80 and over, and age 85 and over.3

 In 2000, there were 

approximately 16.6 million people age 75 and over, 9.2 million people age 80 and over, and 4.2 

million people age 85 and over. By 2020, the number of people in these age groups could reach 22

million, 13 million, and 7 million, respectively. 

 

3

 Two factors that contribute to researchers using different age breaks to define the oldest elderly are (1) differences in

use of health care services, and (2) small sample size among the oldest elderly when using survey data.

6 
Section 2. Aging of the Population

Exhibit 2.1. Age Distribution of U.S. Population 

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90+

Age

P

erc

e

ntage of Total P

opulation

2000

2020

2050

Source: U. S. Census Bureau middle series population projections (Day, 1996). 

Exhibit 2.2. Projections of U.S. Elderly Population 

Year Mean Age

Population 65+ 

(in millions) 

% of Population 

65+ 

%increase from 2000 

in 65+ population 

2000 36.5 34.71 12.6 --

2005 37.2 36.17 12.6 4.2 

2010 37.8 39.41 13.2 13.5 

2020 39.0 53.22 16.5 53.3 

2030 39.9 69.38 20.0 99.9 

2040 40.3 75.23 20.3 116.8

2050 40.3 78.86 20.0 127.2

Source: U. S. Census Bureau middle series population projections (Day, 1996). 

2.2 Implications of an Aging Population for the Demand for Health workers 

2.2.1 Increasing Demand for Health Care Services 

The greater medical needs of the elderly, combined with access to health care services through 

Medicare and Medicaid, have resulted in much higher per capita use of health care services for the 

elderly compared to the non-elderly. On a per capita basis, the elderly have more hospital inpatient 

7 
Section 2. Aging of the Population

days, outpatient visits, and emergency department visits. Relative to the non-elderly, they also have 

more home health visits per capita and are more likely to be in a long-term care facility. 

To illustrate these points, consider Exhibits 2.3 through 2.8 that contain estimates of per capita 

health care use by age, sex, and urban or rural location for six health care settings modeled in the 

NDM. The most profound differences in per capita utilization exist across age groups; however, 

there are also important differences in per capita utilization by sex and by urban or rural location. 

Many of the following estimates are for 1996, the base year in the NDM, although more recent data 

are available for some settings. 

An analysis of the 1996 Health Cost Utilization Project (HCUP) database finds that with the 

exception of the age 0-4 population, the number of inpatient days in general, short-term hospitals per 

1,000 population increases substantially with age for both men and women, in both rural and urban 

areas (Exhibit 2.3). Analyses of other patient-level databases such as the National Hospital 

Ambulatory Medical Care Survey (NHAMCS), the National Home and Hospice Care Survey 

(NHHCS), and the National Nursing Home Survey (NNHS) produced estimates of per capita health 

care utilization in different settings for the eight age groups used in the NDM, by sex, and by urban 

or rural location. These are shown in Exhibits 2.4 through 2.8. 

Exhibit 2.3. Inpatient Days in General, Short-term Hospitals (per 1,000 population) 

Rural Urban

Age Category Female Male Female Male

0-4 years 430 449 789 838

5-17 years 57 45 79 81

18-24 years 276 83 280 141

25-44 years 218 134 327 242

45-64 years 307 317 470 633

65-74 years 919 1,049 1,187 1,640

75-84 years 1,871 2,137 1,985 2,468

85 years and above 3,052 3,826 2,734 3,302

Source: Analysis of the 1996 HCUP database with an adjustment so that rates applied to the population in 1996 

equaled total inpatient days reported by the American Hospital Association (AHA). See Dall and Hogan (2002). 

8 
Section 2. Aging of the Population

Exhibit 2.4. Outpatient Visits in General, Short-term Hospitals (per 1,000 population) 

Rural Urban

Age Category Female Male Female Male

0-4 years 1,472 2,967 985 3,519

5-17 years 783 1,838 651 1,548

18-24 years 954 3,418 592 876

25-44 years 931 2,472 485 1,290

45-64 years 1,464 2,818 833 1,793

65-74 years 2,365 2,593 2,671 2,152

75-84 years 4,841 1,933 4,033 1,896

85 years and above 5,081 1,709 5,734 1,685

Source: Analysis of the 1996 NHAMCS database with an adjustment so that rates applied to the population in 

1996 equaled total non-emergency, outpatient visits reported by the AHA. See Dall and Hogan (2002). 

Exhibit 2.5. Emergency Department Visits in General, Short-term Hospitals (per 1,000 population) 

Rural Urban

Age Category Female Male Female Male

0-4 years 825 426 754 476

5-17 years 422 204 369 211

18-24 years 620 376 534 286

25-44 years 432 284 364 259

45-64 years 346 211 335 190

65-74 years 471 248 468 237

75-84 years 681 313 730 328

85 years and above 953 457 1,298 557

Source: Analysis of the 1996 NHAMCS database with an adjustment so that rates applied to the population in 

1996 equaled total emergency visits reported by the AHA. See Dall and Hogan (2002). 

9 
Section 2. Aging of the Population

Exhibit 2.6. Inpatient Days in Non-General and Long-term Hospitals (per 1,000 population) 

Rural Urban

Age Category Female Male Female Male

0-4 years 0 0 24 33

5-17 years 0 1 17 25

18-24 years 2 2 27 56

25-44 years 4 4 64 85

45-64 years 23 19 169 198

65-74 years 131 145 411 514

75-84 years 221 284 695 664

85 years and above 234 201 773 806

Source: Analysis of the 1996 HCUP database with an adjustment so that rates applied to the population in 1996 

equaled total inpatient days reported by the AHA. See Dall and Hogan (2002). 

Exhibit 2.7. Home Health Visits (per 1,000 population) 

Rural Urban

Age Category Female Male Female Male

0-17 years 420 400 427 406

18-44 years 232 169 403 190

45-64 years 1,497 1,367 1,180 702

65-74 years 8,032 5,230 5,332 3,570

75-84 years 22,211 13,327 12,607 9,485

85 years and above 33,507 29,117 17,534 13,429

Source: Analysis of the 1995 NHHCS database with an adjustment so that rates applied to the population in

1998 equaled estimates of total home health visits paid for by Medicare, Medicaid and other sources in 1998. 

See Dall and Hogan (2002). 

Exhibit 2.8. Nursing Home Residents (Residents per 1,000 population) 

Urban & Rural

Age Category Female Male

0-44 years 0.2 0.2

45-64 years 2.6 1.0

65-74 years 14.5 6.9

75-84 years 52.4 32.0

85 years and above 194.4 187.0

Source: Analysis of the 1997 National Nursing Home Survey (NNHS). See Dall and Hogan (2002). 

10 
Section 2. Aging of the Population

Not only does per capita use of health care services within a delivery setting increase with age, but 

also the type of services used by the elderly (and the mix of health professionals who provide these 

services) differs from those of the non-elderly. To capture these differences in type of services 

received, the PARM uses physician-patient encounters in hospital inpatient and outpatient settings, 

in non-hospital office settings, and in other settings (e.g., nursing homes and home health) to 

forecast future demand for physician services by medical specialty.4

 Even within a specialty, the 

types of services demanded might differ by age. For example, eye diseases such as cataracts and 

glaucoma are much more prevalent in the older population (White et al., 2000). Consequently, as the 

population ages, optometrists will likely see a shift in the type of services provided. 

An important question for modeling requirements for physicians and other health workers is whether 

these caregivers spend different amounts of time per encounter with the elderly relative to the nonelderly. Two databases used to update the PARM—the National Ambulatory Medical Care Survey 

(NAMCS) and the National Hospital Ambulatory Care Survey (NHAMCS) Outpatient File—contain 

information on the amount of time physicians spent with patients during each encounter. To increase 

sample size, we combined the 1997, 1998, and 1999 NAMCS, and we combined the 1997, 1998, and 

1999 NHAMCS. We tested the hypothesis that patient demographic characteristics and insurance 

status are determinants of the amount of time physicians spend per visit with patients in doctors’ 

offices and hospital outpatient settings. We tested this hypothesis by estimating a series of 

 

4

 The nature of a physician-patient encounter, as well as the length of the encounter, can vary substantially by medical 

specialty and delivery setting. Physician surveys, reported in the annual AMA publication Physician Socioeconomic 

Statistics, reveal that physicians typically spend more time per encounter with patients in hospital-based visits versus

office visits that are not hospital-based. Encounters that involve surgical procedures often last two to five times longer, 

on average, than visits that do not involve surgical procedures. Consequently, the PARM forecasts demand for each 

physician specialty by health care setting, and the hospital inpatient setting is subdivided by whether or not a surgical 

procedure was performed. 

 The PARM’s use of physician-patient encounters differs from the workload measures used in other workforce models. 

For example, some models use physician per population ratios while other models use patient visits or hospital 

inpatient days. Estimates of total encounters can differ substantially from estimates of patient visits or inpatient days 

for the following reasons: 

1) A patient might report one visit to a doctor’s office or emergency room but might have zero, one, or multiple 

encounters with physicians during that visit. For example, a physician assistant or an advanced practice nurse might 

see the patient in which case no physician-patient encounter occurs. Or, a physician might see the patient and refer 

the patient to a colleague during the same visit in which case there are two or more physician-patient encounters that 

take place. 

2) In hospital inpatient settings, a physician might visit with a patient one or more times while the physician makes his 

or her rounds. Furthermore, the patient might receive visits from multiple physicians during the day. 

11 
Section 2. Aging of the Population

regressions, using the ordinary least squares (OLS) criterion, with length of time as the dependent 

variable and dummy variables that indicate patient characteristics and insurance status as the 

exogenous variables. The dummy variables take on the value of 1 if the patient has that 

characteristic, and take on the value of 0 if the patient does not have that characteristic. We

estimated separate regressions for each medical specialty. 

The regression results showed each of the exogenous variables (age, sex, race/ethnicity, and 

insurance status) to have a significant impact on the dependent variable (time per encounter) for 

some specialties but not for others. Even when statistically significant, the impact was in many cases 

quite small, less than two minutes per encounter. One caution when interpreting the regression 

results is that the R-squared statistic for every regression is extremely low, indicating that the 

exogenous variables in the model explain only a small proportion of the overall variation in length of 

time physicians spend with patients. The relatively large residual variance makes it more difficult to 

find a statistically significant relationship. Also, for some specialties the number of patients in a 

particular demographic group is small which reduces the precision of the estimates for those 

demographic groups. 

Exhibit 2.9 contains the regression results for encounters in doctors’ offices, and Exhibit 2.10

contains the results for encounters in hospital outpatient settings. The column labeled AVG reports 

the average minutes per encounter for the reference group (non-Hispanic, white females age 55-64, 

insured in a fee-for-service arrangement). The other columns represent the marginal impact of the 

demographic characteristic or insurance status on minutes of physician time per encounter. Shaded 

boxes indicate marginal impacts, relative to the reference category, that are statistically different 

from zero at the 0.05 level of significance. 

To illustrate, consider the first specialty: general and family practitioners. The average time spent 

with the reference group is 18.36 minutes per encounter in doctors’ offices (Exhibit 2.9). Time spent 

with men is just 6 seconds shorter than time spent with women, on average, after controlling for age, 

race/ethnicity, and insurance status. General and family practitioners spend, on average, 2.43 fewer 

minutes per encounter with patients age 0-17 and 1.08 fewer minutes per encounter with patients age 

18-34 compared to the reference group of patients age 55-64. Both of these differences in average 

minutes per encounter are statistically different from zero at the 0.05 level of significance. General 

12 
Section 2. Aging of the Population

and family practitioners also spend 0.91 fewer minutes per encounter with African Americans and 

0.53 fewer minutes per encounter with other minorities, relative to non-Hispanic whites, although 

only the estimate for African Americans is statistically different from zero. Time spent with patients 

in a health maintenance organization (HMO) is 0.81 minutes less than time spent with patients 

insured in a fee-for-service arrangement, while the time spent with uninsured patients is 0.74 

minutes greater than that spent with patients covered under fee-for-service. Neither of these 

differences is large, however, and of the two, only the former is statistically different from zero. 

With respect to the other specialties shown in Exhibit 2.9, major regression effects noted are as follows: 

Sex. - Only orthopedic surgery and other surgical specialties show statistically significant differences 

for men and women. The time per encounter is in both cases greater for men than it is for women: an

additional 0.66 minutes, on average, for orthopedic surgery, an additional 3.86 minutes for other 

surgical specialties. 

Age. - Of the sixteen specialties shown, ten display significant age effects with respect to at least one 

age group. General and family practitioners, for example, spend significantly fewer minutes per 

encounter with patients under 35; internal medicine (IM) subspecialists spend significantly fewer 

minutes per encounter with patients over 74; etc. Most of these effects, however, although statistically 

significant, are no more than a minute or two, with the following exceptions: physicians in other

medical specialties spend over three minutes more per encounter with children under 18 while 

physicians in other surgical specialties spend almost seven minutes less per encounter with patients in 

that age group. 

Race/ethnicity. - Significant race/ethnicity effects are evident for ten of the specialties shown. African 

Americans spend significantly fewer minutes per encounter with physicians in four specialties (general 

and family practice, internal medicine subspecialties, cardiovascular disease, and other patient care) and 

significantly more minutes per encounter with ob/gyn's. Patients in the "other" minority category spend 

significantly fewer minutes per encounter with physicians in three specialties (general internal 

medicine, pediatrics, and psychiatry) and significantly more minutes per encounter with physicians in

another three (other medical specialties, emergency medicine, and other patient care). The added 14.51 

minutes per encounter for "other" minority patients seen by emergency medicine physicians is

particularly noteworthy. 

13 
Section 2. Aging of the Population

Insurance status. - A marked insurance effect is also evident. HMO patients spend significantly fewer 

minutes per encounter with physicians in four specialties (general and family practice, pediatrics, 

orthopedic surgery, and other patient care) and significantly more minutes per encounter with 

physicians in four other specialties (IM subspecialties, cardiovascular disease, other surgical specialties, 

and psychiatry). Of these differences, only those for other surgical specialties (plus 3.82 minutes) and 

other patient care (minus 2.61) exceed 2 minutes. Somewhat surprisingly, there are no specialties for

which uninsured patients receive fewer minutes per encounter, on average, than the reference group, 

whereas there are six specialties for which they receive more minutes on average. Those six are

pediatrics, other medical specialties, general surgery, ophthalmology, other surgical specialties, and 

psychiatry. The added time per encounter, on average, is particularly great for physicians in other

surgical specialties (an additional 11.44 minutes) and psychiatry (an additional 7.95). 

In addition to these observations, applicable to encounters in doctors’ offices, observations of a similar

nature are noted with respect to time spent in hospital outpatient clinics (Exhibit 2.10). General and

family practitioners are seen to spend 24.06 minutes per encounter, on average, with members of the 

reference group. They spend slightly less time per encounter with men, less time with younger 

patients, more time with African Americans, less time with patients in the “other” minority category, 

more time with patients in HMOs, and less time with the uninsured. None of these differences, 

however, is statistically different from zero at the 0.05 level of significance. 

14 
Section 

Exhibit 2.9. Minutes of Physician Time Spent with Patients in Doctors' Offices 

(by Patient Characteristics and Insurance Status)

Marginal Impact (deviation from average minutes)

Sex Age Race/Ethnicit

PARM Classification Avg. Male Female 0-17 18-34 35-54 55-64 65-74 75+ White AA O

General Primary Care General & family practice 18.36 -0.10 -2.43 -1.08 -0.43 -0.54 0.14 -0.91 General internal medicine 20.18 -0.29 -0.62 -0.02 0.31 0.16 0.55 -0.52 Pediatrics 15.97 -0.23 a a a a a 0.25 Medical Specialties IM subspecialties 23.77 -0.81 -3.03 -1.24 1.43 -0.51 -2.13 -2.58 Cardiovascular diseases 19.86 -0.15 3.28 0.76 0.73 1.85 0.94 -1.69 Other medical specialties 15.68 -0.08 3.04 1.08 1.69 -0.71 -0.18 0.46 SurgeryGeneral surgery 18.05 0.17 -1.92 -1.35 -0.25 -0.71 -0.79 -0.00 Obstetrics & gynecology 18.10 b -0.57 -2.21 0.12 0.10 0.91 0.97 Otolaryngology 17.21 -0.05 -1.76 0.03 -0.13 -0.08 1.15 0.73 Orthopedic surgery 15.77 0.66 0.14 0.96 0.27 -0.26 0.34 0.86 Urology 16.80 0.42 0.89 2.13 1.34 0.06 -0.32 -0.90 Ophthalmology 17.11 0.33 1.56 0.08 0.11 0.10 -1.06 0.92 Other surgical specialties 15.15 3.86 -6.54 -4.37 0.09 -3.73 -0.18 0.41 Other Patient CareEmergency medicine 7.15 2.83 -1.80 2.28 3.77 3.04 1.69 21.51 Psychiatry 34.14 0.70 0.18 0.13 0.77 2.01 -3.17 -0.04 Other specialties 25.31 -0.23

Reference Category 

0.08 1.33 1.08

Reference Category 

1.93 0.39

Reference Category 

-3.38 

Source: Analysis of the 1997, 1998, and 1999 NAMCS. Note: Shaded boxes indicate marginal impacts, relative to the reference category, that are statistically different from zero a

significance. a The large majority of patients seen by pediatricians are age 17 and younger, so the sample size of adults se

insufficient to obtain reliable estimates by age group. b This physician specialty saw no patients with this characteristic. 

15
Section 

Exhibit 2.10. Minutes of Physician Time Spent with Patients in Hospital Outpatient Clin

(by Patient Characteristics and Insurance Status) 

Marginal Impact (deviation from average minutes)

Sex Age Race/Ethnicit

PARM Classification Avg. Male Female 0-17 18-34 35-54 55-64 65-74 75+ White AA O

General Primary Care General & family practice 24.06 -0.33 a -1.96 -0.54 0.80 1.21 1.01 General internal medicine 26.23 -1.25 a -1.48 -1.98 -2.56 -0.53 -0.00 Pediatrics 25.83 -1.14 a a a a a -0.89 Medical Specialties IM subspecialties 26.85 -0.38 a -0.34 -0.15 1.22 -0.43 -1.36 Cardiovascular diseases 25.88 0.66 -2.06 4.18 0.95 2.87 0.31 -1.16 Other medical specialties 25.28 -1.36 -3.98 -3.25 -1.28 1.75 2.50 1.76 0SurgeryGeneral surgery 22.97 0.59 -2.50 -1.46 0.65 0.64 -2.80 -1.10 Obstetrics & gynecology 21.76 b -2.43 -3.04 -0.04 2.36 -1.24 0.98 Otolaryngology 17.10 1.00 -1.64 -1.12 1.64 1.89 4.23 2.38 Orthopedic surgery 27.01 -2.36 b -3.90 1.27 2.98 -1.56 0.31 Urology 22.54 2.33 -3.73 -4.92 -4.26 -0.72 0.92 0.94 Ophthalmology 24.21 0.48 -2.38 1.47 -0.70 4.48 7.05 2.25 Other surgical specialties 24.67 -0.28 1.34 -2.51 -1.48 1.61 3.53 0.18 Other Patient CarePsychiatry 27.06 -0.04 2.68 2.05 0.07 1.37 4.08 1.47 Other specialties 23.32 0.80

Reference Category 

b -2.44 -1.12

Reference Category 

-3.12 -2.21

Reference Category 

3.65 

Source: Analysis of the 1997, 1998, and 1999 NHAMCS. Note: Shaded boxes indicate marginal impacts, relative to the reference category, that are statistically different from zero a

significance. a The specialty imputation method identified the physician of patients age 0-17 with general primary care diag

diagnoses as pediatricians, and identified the physicians of adults with these diagnoses as general/family practitioners or in

internal medicine or an IM subspecialty. b The imputation method identified no patients with this characteristic for this spe

16
Section 2: Aging of the Population 

Under a status quo scenario where per capita patterns of health care use within a defined 

demographic group are assumed to remain constant over time, future demand for health care 

services can be extrapolated by estimating the size of the population in each demographic group 

and applying the corresponding per capita utilization rates. Analyses to update the NDM found 

that under such a scenario the growth and aging of the population between 2000 and 2020 would 

contribute to a 30 percent increase in inpatient days at general, short-term hospitals; a 20 percent 

increase in non-emergency outpatient visits to hospitals; a 33 percent increase in inpatient days at 

non-general and long-term hospitals; a 17 percent increase in emergency department visits; a 36 

percent increase in home health visits; and a 40 percent increase in nursing home residents. 

Estimates from the PARM suggest that visits to physician offices would increase by 23 percent 

under this status quo scenario. 

Although recent history is often the best predictor of future health care utilization rates, many 

analysts argue that future rates might differ from current patterns because: 

• The needs of the population are changing (even after controlling for demographics); 

• The health care operating environment is constantly changing; 

• Economic considerations may make current utilization trends unsustainable as the size of the 

elderly population increases; 

• New diseases could emerge; and 

• Technological advances will change how and where services are provided. 

A detailed analysis of the impact on the future health workforce of changes to the health care 

operating environment and technological advances is beyond the scope of this effort; however, 

Section 5 contains forecasts from the PARM and NDM for scenarios that rely on different 

assumptions regarding the future health care operating environment and other determinants of 

the demand for health care providers. A report entitled: The Impact of the Restructuring of the 

U.S. Health Care System on the Physician Workforce and on Vulnerable Populations (The 

Lewin Group, 1998) examines several emerging trends in the health care system and discusses 

their implications for the future physician workforce. 

The impact of advances in science and medicine on demand for health care services and the 

productivity of health care providers will differ by medical specialty and delivery setting. 

 17
Section 2: Aging of the Population 

Advances could increase workforce demand in some settings or specialties while decreasing 

demand in other settings or specialties. For example, technological advances are making 

outpatient surgery a viable alternative to inpatient surgery, and this is contributing to the 

decrease in inpatient days and the increase in outpatient visits. Yashar (2000) reports that 

improvements in surgical instruments have transformed how ocular surgery is performed and 

that ambulatory surgery is becoming the norm for most ocular surgery. 

Similarly, Balaban (1998) states that technological improvements and efforts to contain costs 

have contributed to the trend where bone marrow transplants are performed on an outpatient 

basis with following-up ambulatory visits. Gelijns and Fendrick (1993) provide other examples 

such as cholecystectomy and cardiac catheterization where minimally invasive surgical 

procedures have shifted many of these procedures from an inpatient to an outpatient setting. 

This trend is occurring in many medical specialties and is likely to continue over the next few 

decades. Hospitalization will still occur when treating the more severe cases; consequently, while 

total inpatient days are expected to decline at acute care hospitals, average patient acuity is likely 

to rise and this could affect staffing patterns. In addition, the development of new medications 

could also reduce future demand for some health care services, and thus demand for some health 

professionals. Advances in science and medicine are contributing to higher life expectancy. Over 

the past 100 years, life expectancy has doubled. Increased longevity will contribute to greater 

demand for health care over the long run. 

An important question for projecting future demand for health professionals as the population 

ages is whether current utilization rates for the elderly accurately represent future utilization rates 

for that group. Much of this debate centers on the oldest elderly, who have the highest per capita 

utilization of health care services. In addition to advances in science and medicine and 

improvements in public health, there are important differences between today’s elderly and 

tomorrow’s elderly that could lead to lower per capita utilization in the future. These differences 

include changes in lifestyle of the rising elderly cohort, such as improved diet and exercising, 

higher educational attainment, and greater economic resources. 

 18
Section 2: Aging of the Population 

The consensus is that higher education and greater economic resources, which are highly 

correlated, will contribute to improvements in the health status of the rising elderly cohort 

because both education and economic resources contribute to a healthier lifestyle. 

Greater economic resources allow individuals to purchase the inputs to better health via more 

nutritious food, increased or better preventive care, improved information, and more effective 

pharmaceuticals. Freedman and Martin (1998) find that better educated elderly are more likely to 

comply with physicians’ instructions, which leads to fewer complications. Manton, Corder, and 

Stallard (1997) find that people with higher levels of education are less likely to be disabled 

when controlling for age and other characteristics. 

The extant literature finds that disability rates among the elderly have been declining slightly, 

resulting in a decline in use of some health care services. 

• Bishop (1999) reports that per capita use of nursing home services has declined over the past 

decade. Possible explanations cited include lower disability rates among the elderly, the rise 

in alternative health care services such as home- or community-based care, economic 

considerations, changes in the health care operating system, changes in government programs 

such as Medicare and Medicaid, and other factors cited above. 

• Manton et al. (1997) find that disability rates among older Americans are declining slightly. 

Using data from the National Long-term Care Surveys, these authors find that in 1994 an 

estimated 21.3 percent of the age 65 and older population were chronically disabled. If 

disability rates had remained at their 1982 levels, an estimated 24.9 percent of older 

Americans would have been chronically disabled in 1994, an imputed difference of 3.6 

percentage points. 

• Freedman and Martin (2000) used data from the Supplements on Aging to the 1984 and 1994 

National Health Interview Surveys to examine trends in chronic conditions and functional 

limitations of Americans 70 years and older. They report that the percentage of older 

Americans with functional limitations relating to seeing, lifting, carrying, climbing, and 

walking declined between 1984 and 1994. 

 19
Section 2: Aging of the Population 

• Bonifazi (1998) analyzed the number and needs of nursing home residents in 1995 compared 

to 1977. He finds that a smaller percentage of older Americans are entering nursing homes—

41 per thousand in 1995 compared to 47 per thousand in 1977—despite the aging of the 

elderly population. Part of this decline is attributed to the increase in alternative care settings 

such as outpatient care and home health care. 

Declining disability rates among the elderly could help reduce the projected high growth in 

demand for nursing home care. In addition, the growth in community-based care could further 

reduce per capita demand for institutionalized care. As elderly with less severe health problems 

opt out of nursing homes for home- and community-based care, the health care needs of the 

average nursing home resident rises. Hence, future demand for nurses and other health workers 

in nursing homes could rise proportionately faster than the growth in nursing home residents as 

the population ages. 

In community-based settings, the impact of declining disability rates is unclear. On the one hand, 

declining disability rates might decrease demand for services. On the other hand, declining 

disability rates could shift care from an institutional setting to a community- or home-based 

setting. 

Alecxih (2001) finds that the increase in the size of the elderly population will likely overwhelm

other factors that might influence the future demand for medical care from the elderly. Alecxih 

examined the potential impact of socioeconomic trends on demand for long-term care, including 

declining disability rates, increased availability of informal support networks, and a more highly 

educated elderly cohort. She estimates that demand for long-term care will more than double by 

2050 because of the increasing size of the elderly population. Stuki and Mulvey (2000) estimate 

that by 2030, when the last of the baby boomers reach age 65, an estimated 6 million elderly 

could be at risk of institutionalization because of severe impairments. 

Although the literature suggests numerous factors that could reduce per capita demand for health 

care services from tomorrow’s elderly compared to today’s elderly, Glied and Stabile (1999) 

provide an example of one factor that could cause health care utilization rates for the elderly to 

rise in coming years. These authors predict that private insurance coverage among the nearelderly (i.e., persons ages 61-64) will drop by 4.5 percent by 2005 because of trends relating to 

 20
Section 2: Aging of the Population 

the labor market behavior of the elderly and the reduced propensity of employers to offer 

medical insurance. Although the proportion of the population age 61 to 64 employed full time

increased between 1989 and 1997, the authors report that older workers have been affected by 

the nationwide decline in private medical insurance coverage. The leading edge of the baby 

boom generation is just now entering the phase where they are not yet eligible for Medicare and 

are, for the most part, relying on their current or past employer (if retired) to obtain medical 

insurance. Declining rates of medical coverage among the near-elderly could result in a decline 

in preventive care with long-term implications for this group as they age. 

2.2.2 Increasing Demand for Health Workers 

Who will provide for the health care needs of the future elderly and where will they receive care? 

Currently, the elderly are cared for by services paid for by Medicare, Medicaid, private insurers, 

and out-of-pocket. In addition, many elderly rely on an informal network of unpaid workers—

usually family members. 

Several demographic trends could change the mix of people and institutions providing care to the 

elderly. As discussed above, declining disability rates among the elderly, controlling for age, 

might allow more elderly to remain in their homes or in other community-based settings. This 

would place fewer demands on providers of institutional care, but would increase demand for 

home-based services provided by home health aides, nurses, physical therapists, and other paid 

professionals. This could also increase demand for unpaid providers even while several trends 

suggest that in the future the elderly will have a smaller network to rely on for informal, longterm care. Consider the following factors that could reduce the future supply of unpaid health 

care providers. 

• First, increased longevity means that the adult children of some elderly will themselves be 

elderly. In future years, it might be common for a 70-year old to care for his or her 90-yearold parent. The physical demands of caring for a disabled parent might be too great for many 

elderly children, which could increase demand for home- and community-based care as well 

as institutionalized care.

 21
Section 2: Aging of the Population 

• Second, baby boomers had relatively small families compared to earlier generations, so they 

will have fewer children to provide unpaid care than today’s elderly. 

• Third, Stuki and Mulvey (2000) note that baby boomers had higher divorce rates than today’s 

elderly, and research by Schone and Pezzin (1999) finds that divorced parents are less likely 

than widowed parents to receive long-term care from their adult children.

• Fourth, women traditionally have provided the bulk of unpaid care for elderly parents and the 

proportion of women in the workforce has increased during recent decades. Providing longterm care to an elderly parent or family member might require many of these women (or 

men) to leave the workforce or to reduce the number of hours worked. An estimated 40 

percent of people who provide care to a severely-impaired, older parent or family member 

are employed, and a significant number of these caregivers are forced to adjust their work 

schedule or take a leave of absence (NAC and AARP, 1997). A higher proportion of women 

in the workforce makes it more expensive for family members to care for their disabled 

parents or relatives, but also makes it financially easier to purchase services from home

health agencies and institutional care providers. 

As the aging population demands more health care services, the demand for health workers will 

increase. Demand will grow faster for those specialties that disproportionately serve the elderly 

population. For example, Angus et al. (2000) discuss the implications of the growing elderly 

population on projected demand for physicians in adult critical care and pulmonary medicine. 

The authors report that two-thirds of all inpatient pulmonary days are incurred by patients age 65 

and older. The projected growth in demand for services in these areas leads the authors to predict 

a growing shortage of physicians in adult critical care and pulmonary medicine during the next 

two decades. 

Using the PARM, one can estimate the proportion of time physicians spend with patients in 

different age groups. In this model, as discussed previously, the average length of time that 

physicians spend per visit with patients in physicians’ offices and hospital outpatient settings 

varies by patient demographic characteristics and insurance status. In the other settings modeled 

in the PARM, the assumption is made that physician time per encounter is independent of patient 

age, sex, race/ethnicity, and insurance status. 

 22
Section 2: Aging of the Population 

Currently, physicians spend an estimated 16 percent of patient-care hours providing services to 

children under age 17, 15 percent with the age 18-34 population, 26 percent with the age 34-54 

population, 11 percent with the age 55-64 population, 14 percent with the age 65-74 population, 

and 18 percent with the age 75 and older population (Exhibits 2.11 and 2.12). These estimates 

combine differences in health care needs and size of the population in each age group, as well as 

differences in physician time per visit in settings where this information is available. 

As expected, the proportion of time physicians spend with elderly patients will increase as the 

population ages and the elderly comprise a larger proportion of the population. Consider a 

scenario where physician productivity, staffing levels, and health care use patterns within a 

demographic group remain constant over time at their 1999 levels. In 2020, physicians would be 

spending an estimated 39 percent of total patient-care hours providing services to the age 65 and 

older population compared to an estimated 32 percent in 2000. Today, the 35-54 age group, 

which closely corresponds with the baby boom generation, consumes an estimated 26 percent of 

total patient-care hours. In 20 years, baby boomers will be in the 55-74 age group and will 

consume approximately 34 percent of total patient-care hours. The impact of the increasing age 

of the population on the percentage of total patient care hours spent with each age group is 

shown below for physicians in general primary care (Exhibit 2.13), other medical specialties 

(Exhibit 2.14), surgery (Exhibit 2.15) and other patient care (Exhibit 2.16). 

 23
Section 

Exhibit 2.11. Estimated Percentage of Physician’s Time Spent Providing Care to Patients, by Ag

Age Category, 2000 Age Ca

Specialty 1-17 18-34 35-54 55-64 65-74 75 + 1-17 18-34 35-54 

Total Patient Care Physicians (MDs and DOs) 16 15 26 11 14 18 14 12 20 General Primary Care 29 11 22 10 12 16 25 9 17 General & Family Practice 13 16 29 12 13 17 10 14 22 General Internal Medicine 3 11 28 14 18 25 2 9 20 Pediatrics 100 0 0 0 0 0 100 0 0 Medical Specialties 6 10 26 15 20 23 5 8 19 IM Subspecialties 2 9 28 16 21 23 2 7 20 Cardiovascular Diseases 2 3 18 17 27 33 1 2 12 Other Medical Specialties 17 16 29 12 12 15 14 13 23 Surgery 7 23 27 11 15 17 6 20 20 General Surgery 7 12 27 15 19 20 6 10 20 Obstetrics/Gynecology 4 56 31 5 3 2 4 55 27 Otolaryngology 22 15 27 10 11 14 19 14 21 Orthopedic Surgery 9 14 25 12 16 24 7 11 18 Urology 6 9 21 15 23 26 4 7 14 Ophthalmology 9 7 20 14 22 28 7 6 14 Other Surgical Specialties 4 9 29 16 21 21 3 7 20 Other Patient Care 11 16 31 11 13 18 9 13 24 Anesthesiology 10 18 43 10 8 11 9 16 35 Emergency Medicine 11 15 20 12 19 24 9 12 15 Radiology 24 27 26 7 7 8 22 25 22 Pathology 13 12 21 11 17 26 10 9 16 Psychiatry 1 10 42 12 16 18 1 8 33 Other Specialties 6 15 33 12 14 20 5 12 25 

Non-Physician Specialties

Physical Therapy 20 17 33 12 10 9 17 15 26 Optometry 16 19 31 12 11 10 14 17 24 Podiatry 9 13 29 14 16 20 7 11 22 Total U.S. Population (Thousands) 26 24 29 9 7 6 24 22 25 

Source: These forecasts from the Physician Aggregate Requirements Model assume no change over time in per capita util

productivity or mix, or the health care operating environment. Note: percentages might not sum to 100 percent due to round

 24
Section 2: Aging of the Population 

Exhibit 2.12: Distribution of Total Patient Care Hours, by Patient Age: 

Total Active Physicians in Patient Care 

0%

5%

10%

15%

20%

25%

30%

0-17 18-34 35-54 55-64 65-74 75 +

Age

Percentage of Patient Care Hours

2000 2020

Exhibit 2.13: Distribution of Total Patient Care Hours, by Patient Age: 

General Primary Care Physicians

0%

5%

10%

15%

20%

25%

30%

35%

0-17 18-34 35-54 55-64 65-74 75 +

Age

Percentage of Patient Care Hours

2000 2020

 25
Section 2: Aging of the Population 

Exhibit 2.14: Distribution of Total Patient Care Hours, by Patient Age: 

Primary Care Subspecialty Physicians 

0%

5%

10%

15%

20%

25%

30%

0-17 18-34 35-54 55-64 65-74 75 +

Age

Percentage of Patient Health Care Hours

2000 2020

 26
Section 2: Aging of the Population 

Exhibit 2.15: Distribution of Total Patient Care Hours, by Patient Age: 

Physicians in Surgical Specialties 

0%

5%

10%

15%

20%

25%

30%

0-17 18-34 35-54 55-64 65-74 75 +

Age

Percentage of Patient 

Care Hours

2000 2020

Exhibit 2.16: Distribution of Total Patient Care Hours, by Patient Age: 

Physicians in Other Patient Care Specialties 

0%

5%

10%

15%

20%

25%

30%

35%

0-17 18-34 35-54 55-64 65-74 75 + Age

Percentage of Patient Care Hours

2000 2020

2.3 Implications of an Aging Population for the Supply of Health Workers 

Demographic trends in the health workforce will mirror many of the trends in the overall population. 

In many health care occupations, there are a significant number of baby boomers that will retire just 

as demand for their services is increasing. This is especially true in nursing. An emerging nursing 

 27
Section 2: Aging of the Population 

shortage is likely to be exacerbated starting in approximately 2010 as a large portion of the nurse 

workforce nears retirement. In occupations where some analysts argue there is a current surplus—

e.g., specialist physicians—the growth in demand for services and retirement from the physician 

workforce could eliminate any surplus and could even result in shortages. A large majority of the 

relevant workforce supply literature focuses on physicians and registered nurses, with much less 

published on other health workers. 

2.3.1 Physician Supply 

Forecasting the future supply of physician services involves attempting to predict the future rate of

entrance to and exit from the profession, and predicting the productivity of these physicians while 

they are in the workforce. The age distribution of both the U.S. population and the current physician 

workforce is an important determinant of the size and characteristics of the future workforce. The age 

distribution of the U.S. population affects the rate of new entrants to the profession, while the age 

distribution of the physician workforce affects rates of exit and average level of physician 

productivity. Productivity is defined here as the average number of patient hours per physician per 

year. Physicians, like many professionals who invest heavily in their training, remain active in their 

professions throughout a working career of 30 or more years. The literature suggests that the rate at 

which physicians exit the workforce or reduce their workload is highly related to age—especially as 

physicians approach retirement age. 

American Medical Association (AMA) publications show the number of active physicians in 

different age groups. Of those physicians under 65 years of age in the AMA MasterFile in 1999, 18.9 

percent were under age 35, 32.4 percent were age 35-44, 31 percent were age 45-54, and 17.8 percent 

were age 55-64 (Exhibit 2.17). The age distribution varies substantially by reported primary medical 

specialty, possibly reflecting when a specialty was officially founded (Exhibit 2.18). For example, 

47.4 percent of general practitioners and 40.1 percent of radiologists were age 55-64, while only 10 

percent of emergency physicians and 10.5 percent of family practitioners were in this age group. In 

thoracic surgery, approximately half the physicians are under age 35 and the other half are almost 

entirely age 35-44. There are very few physicians over age 44 who report thoracic surgery as their 

primary specialty. Some specialties, such as general surgery, have a relatively flat age distribution, 

with approximately 1/4th of physicians in each of the four age groups. Specialties with a high 

 28
Section 2: Aging of the Population 

percentage of physicians nearing retirement are especially vulnerable to a rapid decrease in number of 

active physicians. Not only is an adequate supply of new physicians important to consumers, but an 

adequate supply is important to retiring physicians who desire to see established practices continue to 

flourish. 

Exhibit 2.17. Age Distribution of the Current Physician Workforce 

Under 35 Years

19%

35-44 Years

32%

45-54 Years

31%

55-64 Years

18%

Source: American Medical Association, Physician Characteristics and Distribution in the 

U.S., 2001-2002 Edition. 

 29
Section 2: Aging of the Population 

Exhibit 2.18. Percent Distribution of the Physician Workforce Under Age 65, by Age Group, in 1999 

Specialty Under 35 Years 35-44 Years 45-54 Years 55-64 Years 

Total 18.9 32.4 31.0 17.8

Aerospace Medicine 2.9 28.5 41.5 27.1

Allergy & Immunology 5.8 31.4 38.0 24.8

Anesthesiology 13.2 42.4 29.2 15.2

Cardiovascular Disease 9.4 35.5 36.1 18.9

Child Psychiatry 8.4 34.5 36.3 20.8

Colon/Rectal Surgery 7.8 36.5 35.8 19.9

Dermatology 16.7 31.0 31.6 20.7

Diagnostic Radiology 18.7 34.2 30.9 16.2

Emergency Medicine 23.0 31.9 35.0 10.0

Family Practice 22.9 34.7 31.8 10.5

Forensic Pathology 6.6 32.3 35.7 25.4

Gastroenterology 9.6 37.3 35.8 17.3

General Practice 1.1 13.9 37.6 47.4

General Preventive Med. 7.2 31.0 37.4 24.3

General Surgery 24.1 26.9 26.9 22.1

Internal Medicine 24.8 33.0 29.5 12.7

Medical Genetics 13.2 31.9 34.0 20.8

Neurology 12.6 32.7 36.5 18.3

Neurological Surgery 18.6 28.7 27.6 25.1

Nuclear medicine 9.0 24.7 37.7 28.6

Obstetrics/Gynecology 19.2 30.1 30.6 20.1

Occupational Med. 0.6 27.7 45.2 26.5

Ophthalmology 12.6 31.5 31.2 24.7

Orthopedic Surgery 17.1 29.8 29.7 23.4

Otolaryngology 18.0 29.8 27.4 24.8

Pathology-Anat/Clin 12.2 31.7 32.6 23.6

Pediatrics 27.0 32.3 27.0 13.7

Pediatric Cardiology 13.9 42.2 26.6 17.3

Physical Med/Rehab 18.2 42.7 26.3 12.8

Plastic Surgery 7.5 32.2 35.1 25.1

Psychiatry 11.2 28.1 34.7 26.0

Pulmonary Diseases 11.3 36.5 37.3 14.9

Radiology 7.8 27.1 25.0 40.1

Radiation Oncology 13.6 37.8 29.5 19.0

Thoracic Surgery 50.4 49.2 0.4 0.0

Urological Surgery 13.5 27.6 29.9 29.0

Other 1.8 20.1 40.1 38.1

Source: American Medical Association, Physician Characteristics and Distribution in the U.S., 2001-2002 Edition

 30
Section 2: Aging of the Population 

As health professionals age, they typically reduce their hours worked in patient care—especially 

professionals approaching retirement age who might view a reduced workload as an alternative to 

retirement. Although we identified no recent studies showing working patterns of physicians over 

their career, a survey of optometrists by Abt Associates (White, Doksum and White, 2000) finds that 

hours spent in patient care decline with age (Exhibit 2.19). The trend is especially evident among 

male optometrists. From age 30 to retirement, average hours spent in patient care drops slowly but 

steadily. Average hours worked by female optometrists declines slightly when these women are in 

their 30s and 40s, possibly resulting from a reduced workload to care for children, but then increases 

in their 50s until retirement. The spike in hours by female optometrists in the 65-69 age group could 

be an anomaly due to small sample size. 

Exhibit 2.19. Average Number of Hours Optometrists Spend in Patient Care per Work Week 

Hours Spent in Patient Care 

Age Group Men Women 

25 to 29 41.6 40.4 

30 to 34 43.0 37.5 

35 to 39 42.3 35.6 

40 to 44 41.7 34.2 

45 to 49 41.2 35.4 

50 to 54 39.8 37.1 

55 to 59 38.6 37.0 

60 to 64 37.2 35.2 

65 to 69 33.3 42.3 

70+ 28.5 27.1

Source: Project Hope Census of Optometrists (White, Doksum and White, 2000), Table 2. 

2.3.2 Nurse Supply 

The aging of the nurse workforce and the inability to attract new entrants are often cited as major 

contributors to an impending nurse shortage.5 Factors contributing to the aging of the nurse 

population include the large number of baby boomers who entered the profession in the 1970s and 

1980s, declining enrollment in nursing programs, retention difficulties, and a higher average age of 

new graduates from nursing programs. 

 31
Section 2: Aging of the Population 

Findings from the 2000 Sample Survey of Registered Nurses (HRSA, 2001) indicate that between 

1980 and 2000 the percentage of RNs under the age of 40 fell from approximately 53 percent to 32 

percent. Buerhaus, Staiger and Auerbach (2000) discuss this phenomenon and the implications of an 

aging RN workforce. The authors report that between 1983 and 1998 the average age of RNs in 

hospitals increased by 5.3 years. During the same period, the average age of the entire RN workforce 

increased 4.5 years, from 37.4 to 41.9. The General Accounting Office (GAO, 2001) estimates that by 

2010, approximately 40 percent of the RN workforce will be age 50 or older. 

The primary cause of an aging RN workforce is the failure to attract young workers (especially 

women) into the profession. The changing age distribution of the population will make it more 

difficult to attract young workers into nursing in future years. The American Association of Colleges 

of Nursing reports that enrollments in entry-level baccalaureate programs in nursing have declined

every year between 1995 and 2000. Enrollees to these programs have declined by 21 percent between 

1995 and 2000, while graduates have declined by 16.5 percent. The GAO estimates that the ratio of 

working-age women (age 18 to 64) to the age 85 and older population will decline over time from

approximately 40:1 in 2000, to 22:1 in 2030, and to 15:1 in 2040. This finding has important 

implications for the future supply of all health professions. 

Buerhaus, Staiger and Auerbach analyzed the relationship between age and RN workforce 

participation for a cohort (defined by birth year) of the population. RNs typically enter the profession 

in their early 20s to early 30s, and the number of full-time equivalent (FTE) RNs from a population 

cohort increases through age 45 as many RNs finish their schooling and pass out of their child rearing 

years. Between ages 45 and 55, the number of FTEs from a cohort remains fairly stable, but then 

begins to decline as RNs retire or reduce hours worked. 

Although the demographics of the current nurse workforce will have a great impact on the nurse 

workforce of the future, the large proportion of nurses who will be retiring during the next 10 years 

will not necessarily result in a shortage. Economic theory suggests, and history has shown, that wages 

will adjust, making shortages and surpluses a short-term phenomenon. However, it does suggest that 

 

5

 The extant literature on this topic is vast, but two recent publications include a study by the General Accounting Office 

(2001) and Nevidjon and Erickson (2001). 

 32
Section 2: Aging of the Population 

the real wages of nurses will increase. This in turn will attract new entrants, gradually reducing wages 

to “normal” levels. 

There is less literature on the demographics of licensed practical nurses and nurse aides. LPNs and 

nurse aides tend to be younger than RNs. Indeed many LPNs and nurse aides see becoming RNs as a 

means to better oneself professionally. The duties performed by LPNs and nurse aides are often 

physically demanding which limits the ability of some older people to serve in this capacity. Because 

LPNs and nurse aides require less time to train than RNs, the supply of these nurses can react more 

quickly to market conditions. 

As an aging population demands more services from an increasingly older nurse workforce, some

employers of nurses might look outside the U.S. to countries with younger populations. Many of 

these countries that could potentially export nurses might themselves have nurse shortages, in which 

case an inadequate supply of nurses in the U.S. could reduce the availability of care in other 

countries. Cheryl Peterson, director of international nursing at the American Nurses Association, 

states: “I’m always telling people in developing countries, ‘You don’t want the U.S. shortage to 

worsen because we’ll grab up all of the world’s poor nurses.’”6

2.4 Implications of an Aging Population for the Economics of the Health Care System 

Health care spending constitutes almost one-eighth of our Gross Domestic Product(Heffler, 2001). 

Because such a large portion of the Nation’s resources is spent on health care, the economics of the 

health care system are closely intertwined with the national economy. Changing demographics will 

have a significant impact on both the U.S. economy and the economics of the health care system. 

The Congressional Budget Office (1997) estimates that total national spending on health care could 

double between 1996 and 2008 to nearly $2 trillion. Ginzberg (1999) projects that annual 

expenditures for health care could top $4 trillion by 2025, and this, says Ginzberg, “could turn out to 

be a serious underestimate given the steep increase in the number of elderly, who make much greater 

use of health care services than the below-65 population (p. 58).” 

 

6

 As reported in the Wall Street Journal article: Shortage of Nurses Hits Hardest Where They Are Needed the Most: Nurse 

Shortage Shows How Labor Markets Go Global (Zachary, 2001, p. A12). 

 33
Section 2: Aging of the Population 

Stucki and Mulvey (2000) report that by 2030, when the last of the baby boomers reaches age 65, the 

cost to provide personal care, adult day care, and assisted living to the elderly could quadruple to an 

estimated $193 billion. Nursing home expenditures paid by Medicaid could rise 360 percent to $134 

billion (in 1996 dollars) between 2000 and 2030 (Mulvey and Stucki, 1998). 

If retirement patterns remain unchanged, the ratio of working to retired Americans will continue to 

decline as the population ages. Pizer, Frakt and Kidder (2000) project that by 2005 the ratio of 

workers to retirees will be 5:1, and this ratio could fall to 2.75:1 by 2050. This means that a smaller 

proportion of the population will be supporting the needs of the elderly. 

The Medicaid and Medicare programs will compete with other programs, such as Social Security, 

that serve the elderly. As the size of the elderly population grows, resulting in an increase in the 

number of Medicare and Medicaid eligibles, the resulting increase in government outlays for health 

care services could compel the government to reduce expenditures by 

• reducing benefit levels, 

• restricting eligibility, 

• increasing out-of-pocket expenditures by increasing premiums or co-pays, and 

• reducing reimbursements to health care providers. 

On the other hand, the elderly will constitute a growing voting bloc that could attempt to retain 

current benefits or even expand benefits. 

Tarlov (1995) states that the consensus outlook of future demand for health care services is that 

“service quantity and price will be set at economically absorbable levels determined by employeremployee willingness to pay and by politically acceptable government budgets for health care (p. 

1560).” Ginzberg anticipates that cost pressures will result in radical changes in the health care 

system during the early part of the 21st century. Ginzberg anticipates that Medicare will provide 

beneficiaries access to “essential” health care services, but not to high-cost hospitals and expensive 

procedures. 

Actions to reduce spending could reduce demand for health workers. The impact would vary 

substantially by medical specialty and delivery setting, with providers of expensive services likely to 

see the greatest impact on demand for their services. In addition, attempts to reduce health care 

 34
Section 2: Aging of the Population 

spending through lower reimbursement rates to health care providers could, in the long run, reduce 

the supply of health workers. Caro and Kaffenberger (2001) find that reductions in Medicare 

payments for nursing home care and home health services resulting from the Balanced Budget Act of 

1997 pushed many long-term care providers out of business, thus reducing the demand for nurses and 

other health workers in those settings. 

 35
Section 2: Aging of the Population 

 36
Section 3: Changing Racial and Ethnic Composition of the Population

3 CHANGING RACIAL AND ETHNIC COMPOSITION OF THE POPULATION 

Major Findings: 

• Minorities have different patterns of 

health care use compared to nonminorities. Disparities in access to 

care account for part of the difference 

in utilization.

• Demand for health care services by 

minorities is increasing as minorities 

grow as a percentage of the 

population. Between 2000 and 2020, 

the percentage of total patient care 

hours physicians spend with minority 

patients will rise from approximately 

31percent to 40 percent. 

• Minorities are underrepresented in the 

physician and nurse workforce 

relative to their proportion of th

population. As minorities constitute a 

larger portion of the population 

entering the workforce, their 

representation in the physician and 

nurse professions will increase. The 

U.S. will increasingly rely on minority 

caregivers. 

e total

• Minority physicians have a greater 

propensity than do non-minority 

physicians to practice in urban 

communities designated as physician

shortage areas. An increase in 

minority representation in the 

physician workforce could improve 

access to care for the population in 

some underserved areas. 

Advocates for increased minority representation in the health workforce argue that increasing the 

number of minority physicians will improve access to care for minorities and vulnerable, 

underserved populations. These advocates argue that 

increased representation of minorities in the health 

workforce not only will increase equity, but will also 

improve the efficiency of the health care delivery 

system. 

This section explores the changing racial and ethnic 

composition of the population and its implications for 

the future demand for and supply of health 

professionals. The four main findings are the 

following. 

First, Hispanics and non-whites have different patterns 

of health care use compared to non-Hispanic whites. 

Some of the disparities in use can be attributed to 

differences in access to care. The literature suggests 

that cultural differences regarding appropriate use of 

health care services also help explain differences in 

health care use. 

Second, as minorities increase as a percentage of the 

U.S. population, the percentage of total health care 

services provided to minority patients will also 

increase. In 2000, physicians spent an estimated 31 

percent of patient-care hours providing services to 

minorities. By 2020, physicians will spend an 

estimated 40 percent of patient-care hours with 

minority patients. 

 37
Section 3: Changing Racial and Ethnic Composition of the Population

Third, minorities are underrepresented in the physician and nurse workforces relative to their 

proportion of the total population, and are overrepresented in lower-paying health professions such 

as nurse aides and home health aides. As minorities constitute a growing percentage of the 

working-age population, their representation in the professional health workforce will naturally 

rise. The U.S. will increasingly rely on minority caregivers. 

Fourth, the literature suggests that minority physicians have a greater propensity than do nonHispanic white physicians to practice in urban communities designated as physician shortage 

areas. An increase in minority representation in the physician workforce could improve access to 

care for the population in some underserved areas. 

3.1 Population Forecasts 

The latest census figures highlight the fact that the United States is becoming increasingly racially 

and ethnically diverse. Furthermore, higher birth rates among racial and ethnic minority groups, 

relative to non-Hispanic whites, and immigration suggest that this trend will continue. Exhibit 3.1

contains population forecasts used in the PARM that show the current and projected distribution of

the population across the three race/ethnic groups modeled in the PARM. Whereas non-Hispanic 

whites constituted approximately 69 percent of the population in 2000, they will constitute an 

estimated 61 percent of the population in 2020. Between 2000 and 2020, African Americans (both 

Hispanic and non-Hispanic) will increase from approximately 12.3 percent to 13.1 percent of the 

population; all other minorities (including Hispanic whites) will increase from approximately 19 

percent to 26 percent of the population. Growth in the Hispanic population is the major contributor 

to growth in the minority population. 

Exhibit 3.1. Population Distribution by Race 

Year Non-Hispanic White African American All Other 

2000 69.1% 12.3% 18.6%

2005 67.1% 12.5% 20.4%

2010 64.8% 12.7% 22.5%

2015 62.8% 12.9% 24.3%

2020 60.8% 13.1% 26.1%

Source: Modified version of Census Bureau middle series projections.

 38
Section 3: Changing Racial and Ethnic Composition of the Population

Racial and ethnic minority populations are unevenly distributed geographically. The proportion of 

a State’s population that is minority varies substantially by State, and minorities are 

disproportionately located in inner cities. 

3.2 Implications of the Changing Racial and Ethnic Composition of the Population for the 

Demand for Health Workers 

The extant literature explores the degree to which and reasons why 

race and ethnicity may affect health care use. Differences between 

racial and ethnic groups in use of a wide range of health care 

services have been documented in the literature. Much of these 

utilization differences are attributed to differences in access to care 

and cultural differences regarding the use of health care services. 

A better understanding of differences in health care utilization by 

race and ethnicity, the causal factors of these differences, and

whether these differences will persist in the future allows for better 

predictions of future demand for health workers. 

“Whether representing

actual differences or a 

constellation of factors 

that affect health and 

health status, race and 

ethnicity are important 

determinants of health 

patterns in the United 

States.” (Sondik et al., 

2000)

Below is a sample of the literature that describes differences in health care utilization by race or

ethnicity. 

• Mueller, Patil and Boilesen (1998) analyzed data from the 1992 National Health Interview 

Survey (NHIS) and found racial disparities in use of physician services even after controlling 

for factors such as insurance status, geographic location and other patient characteristics. The 

disparity in use of physician services by race was not statistically different from zero for those 

patients living in urban areas, but the disparity was statistically different from zero for patients 

living in rural areas. Insurance status and location (urban versus rural) are greater determinants 

of use of physician services than is patient race. 

• Hargraves, Cunningham and Hughes (2001) found small differences in access to care and 

health care use of non-Hispanic whites and minorities enrolled in managed care plans. Whereas 

approximately 78 percent of non-Hispanic whites have a regular provider, only 74 percent of 

Hispanics and African Americans have a regular provider. Whites have slightly higher use of 

 39
Section 3: Changing Racial and Ethnic Composition of the Population

specialists. In their last physician visit, 28 percent of non-Hispanic whites saw a specialist 

compared to 26 percent for African Americans and 22 percent for Hispanics. 

• Burns et al. (1996) use Medicare claims from ten States to examine differences in 

mammography use between elderly African American and white women. They find that 

African American women had lower use rates than white women across all levels of primary 

care. These authors cite additional research that finds that physicians are more likely to 

encourage elderly white women to obtain mammograms than elderly African American 

women, highlighting concerns about provider attitudes. 

• Peterson et al. (1994) analyzed the use of cardiac procedures of men treated at Veterans Affairs 

Medical Centers. These authors find that African Americans are less likely than their white 

counterparts to undergo selected cardiac procedures. The authors suggest several reasons for 

the differences in treatment, including: (1) differences in severity, (2) consumer preferences, 

and (3) differences in how providers may weigh the risk and benefit of invasive procedures 

differently for African Americans than for whites.

• Todd et al. (1993) studied analgesic use in emergency departments and find that ethnicity was 

a strong predictor of the lack of use of analgesics. 

• Mitchell et al. (2000) analyzed Medicare inpatient data to compare differences between 

African Americans and whites in the use of diagnostic and therapeutic services for 

cerebrovascular disease. These authors control for differences in factors such as health care 

needs and ability to pay. Still, they find that “black patients were significantly less likely to 

receive non-invasive cerebrovascular testing, cerebral angiography, or carotid endarterectomy 

compared to white patients (p. 1413).” 

Not all studies find differences by race or ethnicity in use of health care services. For example, 

Horner et al. (1997) found no differences by race and ethnicity in the use of inpatient rehabilitation 

services for elderly stroke victims after adjusting for differences in patient risk. 

Access to affordable medical insurance is often cited as a major determinant of access to care. 

People in racial and ethnic minority groups in 1999 were more than twice as likely as 

nonminorities to be uninsured. The Census Bureau estimates that, in 1999, 89 percent of non-

 40
Section 3: Changing Racial and Ethnic Composition of the Population

Hispanic whites had some form of medical insurance while only 79 percent of African Americans 

and 67 percent of Hispanics were insured.7

 These statistics are important because the literature has 

established a link between access to care and health status (e.g., Drake and Lowenstein, 1998). 

Specifically, people without medical insurance tend to receive less preventative care and have 

higher rates of hospitalization for potentially avoidable problems. Drake and Lowenstein note that 

in California during the year of their study (1993), approximately 14 percent of African Americans 

and 37 percent of Latinos were uninsured, compared to 12.5 percent of whites. 

An analysis of the 1999 NHIS found that 9 percent of non-Hispanic whites, 16.4 percent of 

African Americans, and 26.3 percent of other minorities (including Hispanic whites) were without 

health insurance on the date surveyed in 1999. The PARM divides the population into three 

insurance categories: insured in a fee-for-service arrangement, insured in an HMO, and uninsured. 

Exhibit 3.2 shows that the proportion of each racial/ethnic group in an HMO is relatively similar,

controlling for age and sex, but the percentage insured in a fee-for-service arrangement and 

uninsured vary substantially by race/ethnicity. 

Language and cultural differences also are cited as factors affecting health care utilization. With 

the growing population of Hispanics in the U.S. and immigration from non-English speaking 

countries, language is playing an increasingly important role in the provision of health care 

services. Consider the following findings in recent studies. 

• Kravitz et al. (2000) found that Spanish-speaking patients who visited the General Medicine 

and Family Practice Clinics at the UC Davis Medical Center were less likely to follow up with 

recommended laboratory studies compared to English-speaking patients. In addition, patients 

needing a translator required more physician time per visit. The authors applied regression 

models to estimate the impact of language on physician time per visit. They found that 

Spanish- and Russian-speaking patients averaged 9.1 and 5.6 additional minutes of physician 

time, respectively, compared to English-speaking patients after controlling for other 

determinants of physician time per visit. 

 

7

 US Census, http://www.census.gov/hhes/hlthins/hlthin99/hi99tc.html

 41
Section 3: Changing Racial and Ethnic Composition of the Population

Exhibit 3.2. Percent Distribution of the Population by Demographic Group Across Three 

Insurance Categories

Non-Hispanic White African American All Other 

Age Insurance Male Female Male Female Male Female

FFS 59 59 59 61 48 48

0-17 HMO 34 34 30 27 29 29

Uninsured 7 7 11 12 23 23

FFS 48 52 39 46 30 35

18-34 HMO 32 33 29 33 28 31

Uninsured 21 15 32 21 42 34

FFS 55 56 44 45 35 39

35-54 HMO 35 35 37 36 36 36

Uninsured 11 9 19 18 29 25

FFS 61 63 57 59 43 48

55-64 HMO 32 30 28 25 37 31

Uninsured 7 7 15 16 20 21

FFS 85 87 82 82 85 85

65-74 HMO 15 13 18 18 15 15

Uninsured 0 0 0 0 0 0

FFS 89 89 87 93 88 93

75+ HMO 11 11 13 7 12 7

Uninsured 0 0 0 0 0 0

FFS 60 63 52 56 41 46

HMO 30 29 30 29 30 30

Uninsured 10 8 18 15 28 24

FFS 61 54 44

HMO 30 30 30

All Ages 

Uninsured 9 16 26

Source: Analysis of the 1999 NHIS. 

• Derose and Baker (2000) analyzed survey data for 465 Spanish-speaking Latinos and 259 

English speakers of various ethnicity who presented to a public hospital emergency department 

in Los Angeles. The survey asked participants to assess their English-speaking ability; indicate 

the number of visits to a physician during the prior three months; and provide information on 

the participants’ health status, socioeconomic status, and demographic characteristics. The 

authors found that of participants who had at least one visit to a doctor during the previous 

three months, those with limited English proficiency had 22 percent fewer visits, on average, 

compared to participants with good-to-excellent English proficiency. The study controlled for 

patient characteristics that could be correlated with the use of physician services such as health 

conditions and insurance status. In practice, therefore, language and communication may be 

significant barriers to access to care.

 42
Section 3: Changing Racial and Ethnic Composition of the Population

In addition to differences across racial groups and English/non-English speakers in access to and 

use of health care services, there are significant differences in measures of health status that affect 

the type of care demanded. Keppel, Pearcy, and Wagener (2002) find that compared to nonHispanic whites, many minority populations have higher infant mortality rates, higher rates of 

infants with low birth weight, higher age-adjusted rates of heart disease death, higher rates of 

tuberculosis, and disparities in many other measures of health care. 

Freiman (1998) argues that the relationship between race or ethnicity and demand for health care 

services is a complex function of cultural, socioeconomic, and other considerations. Consequently, 

Freiman concludes that separate demand equations should be estimated for people in different 

racial or ethnic groups. To support his conclusions, Freiman presents findings from a multiple 

regression analysis of the 1987 National Medical Expenditure Survey where statistical tests 

performed indicate significant differences in the estimated coefficients of demand equations—

estimated separately for non-Hispanic whites, African Americans, and Hispanics—that control for 

important determinants of health care use. 

The PARM provides insight on the proportion of patient care hours that physicians spend 

providing care to patients in three race/ethnic groups. These estimates, like those described for 

people in different age categories in the preceding section, are based on patterns of health care use, 

the size of the population in each demographic group, and the average amount of time physicians 

spend with patients per encounter. In physicians’ offices and in hospital outpatient settings, the 

average time spent per visit can differ by patient depending on the patient's demographic 

characteristics and insurance status. In the other settings, however, there are insufficient data to test 

the hypothesis that physician time per visit is independent of patient demographics and insurance 

status. Note that differences in the age and sex distribution of the population, by race, contribute to

differences in the proportion of patient care hours spent with patients of different races. 

In 2000, physicians spent approximately 69 percent of patient care hours with non-Hispanic 

whites, 13 percent with African Americans, and 18 percent with other minorities (Exhibits 3.3 and 

3.4). Although the proportion of total patient care hours approximated the proportion of the 

population in each racial group, the distribution of hours varied by physician specialty. African 

Americans, who constituted approximately 12 percent of the U.S. population in 2000, used a 

 43
Section 3: Changing Racial and Ethnic Composition of the Population

disproportionately higher percentage of total patient care hours of emergency medicine physicians 

(38 percent), obstetrician/ gynecologists (17 percent), and pediatricians (16 percent). They 

received proportionately fewer hours from “other” surgical specialties (8 percent) and general 

surgeons (9 percent). The population in the “other” race/ethnicity category, which constituted 

approximately 19 percent of the total population in 2000, received a relatively larger proportion of 

radiology (31 percent) and pathology (29 percent) services, but a relatively smaller proportion of 

patient care hours from urologists (11 percent), ophthalmologists (11 percent), and general and 

family practitioners (13 percent). 

If the distribution of insurance status for non-Hispanic whites were applied to other racial 

minorities, the total demand for physicians in 2000 would have risen significantly (see Section 5, 

Scenario 5) but the percentage of patient care hours by racial group would have remained 

relatively unchanged. The percentage of total physician patient care hours spent with non-Hispanic 

whites would decline by two percentage points while the percentage spent with African Americans

and other minorities would rise by one percentage point for each group. For most specialties, the 

change in percent of time spent with patients in each race/ethnicity group changes by less than two 

percentage points. The largest change is for obstetrics/gynecology services. Under this scenario, 

the percentage of hours spent with non-Hispanic white patients would fall by three percentage 

points while the percentage of hours spent with patients in the “other” category (which includes 

Hispanics) would rise by three percentage points. 

If health care utilization patterns and physician productivity patterns remain constant over time, in 

2020 physicians will be spending approximately 14 percent of patient care hours with African 

Americans and 26 percent of hours with patients of other minority groups, again percentages 

roughly comparable to each group's share of the total population. 

Physical therapists, optometrists, and podiatrists are seen to spend a disproportionate amount of 

time with non-Hispanic whites relative to their share of the population (Exhibit 3.4). While the 

gap for African Americans is small (and non-existent in the case of podiatrists), the gap for other 

minority groups was large in 2000 and projected to remain so in 2020. 

 44
Section 3: Changing Racial and Ethnic Composition of the Population

Exhibit 3.3: Distribution of Total Patient Care Hours, by Patient Race: 

Total Active Physicians in Patient Care 

69%

18%

13%

60%

26%

14%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Non-Hispanic

White

African American Other

Race

Percentage of Patient Care Hours

2000 2020

 45
Section 3: Changing Racial and Ethnic Composition of the Population

Exhibit 3.4. Estimated Percentage of Patient Care Hours, by Race of Patient 

a a

Specialty

NonHispanic 

White

African 

American All Other

NonHispanic 

White 

African 

American

Total Patient Care Physicians (MDs 

and DOs) 69 13 18 60 14 26

72 13 15 63 14 24

 GP & FP 10 13 69 11 20

 General Internal Med. 72 14 63 15 23

 Pediatrics 61 16 51 17 32

Medical Specialties 71 13 16 13 25

 IM Subspecialties 71 13 16 62 25

 Cardiovascular Diseases 73 11 15 64 12

 Other Medical Specialties 70 13 17 60 13 26

Surgery 71 12 17 62 12 26

70 9 22 59 

2000 2020

All Other

General Primary Care 

78

14

23

62 

13

24

 General Surgery 9 32

 Obstetrics/Gynecology 66 17 17 57 18 25

 Otolaryngology 75 11 14 67 12 21

 Orthopedic Surgery 72 11 17 62 11 27

 Urology 78 11 71 12 17

 Ophthalmology 78 10 11 71 11 18

 Other Surgical Specialties 73 8 19 62 8 30

Other Patient Care 64 15 21 53 15 32

 Psychiatry 73 11 16 62 11 26

 Anesthesiology 66 14 21 56 13 31

 Emergency Medicine 47 38 16 39 39 22

 Radiology 56 14 31 45 12 43

 Pathology 60 11 29 48 10 42

 Other Specialties 67 13 20 57 13 30

 Non-Physician Specialties 

 Physical Therapy 80 10 10 74 12 15

 Optometry 80 10 11 73 11 16

 Podiatry 78 12 10 71 14 15

Total U.S. Population 69 12 19 61 13 26

11

a These forecasts from the Physician Aggregate Requirements Model assume no change over time in per 

capita utilization, physician productivity or mix, or the health care operating environment. 

Note: percentages might not sum to 100 percent due to rounding. 

 46
Section 3: Changing Racial and Ethnic Composition of the Population

3.3 Implications of the Changing Racial and Ethnic Composition of the Population for the 

Supply of Health Workers 

One of the five major recommendations of the Pew Health Professions Commission is to “ensure 

that the health profession workforce reflects the diversity of the nation’s population.” (O’Neil et 

al., 1998, p. iv). Currently, minorities are underrepresented in the physician and registered nurse 

workforce. The Pew Commission and numerous others argue that increasing minority 

representation in the health workforce is not only a commitment to diversity, but will also improve 

the health care delivery system. The two main arguments that diversity improves health care 

delivery are (1) minority health professionals express a greater propensity than do non-minority 

professionals to practice in underserved areas, and (2) health professionals who share the same

culture and language with the patients they serve can provide more effective care (see, for 

example, Trevino, 1994). Much of the literature on willingness to practice in underserved areas 

pertains to physicians. 

Supply models generally do not have a race/ethnicity component. Possible reasons include data 

limitations and the lack of priority this topic has received. Consequently, our understanding of the 

relationship between supply of health workers and race/ethnicity consists of snapshots of the racial 

and ethnic distribution through surveys and periodic efforts to survey health workers regarding the 

relationship between race/ethnicity and workforce issues (e.g., workforce participation, retention, 

and productivity). The following are important factors and questions to consider regarding the 

relationship between race/ethnicity and the supply of health workers: 

1. Minorities have historically been underrepresented in higher-paying health care occupations 

and overrepresented in lower-paying health care occupations relative to their percentage of the 

U.S. population. As minorities constitute an increasing proportion of the population entering 

the workforce for the first time, minority representation in higher-paying health care 

occupations will naturally increase. As a result, the U.S. will increasingly rely on minority 

health workers. 

2. One area where additional research is needed is whether lifetime labor force participation 

patterns of health workers differ by race or ethnicity. For example, minorities have historically 

had a shorter life span than non-minorities, although in recent years longevity of minorities has 

 47
Section 3: Changing Racial and Ethnic Composition of the Population

increased to more closely resemble longevity of whites. Do differences in longevity affect 

retirement rates?

3. Does race or ethnicity affect the health worker’s choice of profession or medical specialty? 

4. Does race or ethnicity affect the education and training opportunities of persons desiring to 

enter a health care profession? 

5. Empirical research suggests that physician race and ethnicity are significant determinants of

where the physician will practice, and minority physicians have a greater propensity than do 

non-minority physicians to practice in underserved locations. 

6. Does physician race or ethnicity affect the quality of care that patients receive? A large body of 

literature explores the issue of “cultural competence,” which is that health professionals can 

provide more effective and efficient services if they are sensitive to their patients’ cultural 

background. 8 A recent literature review analyzed over 120 works in the field of cultural 

competence in health care to develop a measurement profile for cultural competence in health 

care delivery settings (The Lewin Group, 2001). Several articles reviewed discuss the need to 

emphasize the value of diversity and the importance of involving people from diverse 

backgrounds in the decision-making process of what care to provide to underrepresented 

groups. Much of the literature discusses the importance of educating health workers to be 

sensitive to differences in the needs of their patients resulting from differences in culture. 

 

8

 Cross et al. (1999) define cultural competence as “a set of congruent behaviors, attitudes, and policies that come

together in a system, agency, or among professionals and enable that system, agency, or those professionals to work

effectively in cross-cultural situations.” It should be noted that culture is defined by more than race and ethnicity, it 

also encompasses economic and social factors. Often, race and ethnicity are correlated with these economic and 

social factors, which can obscure the relationship between health care and race or ethnicity. For example, Kington 

and Smith (1997) analyzed the relationship between socioeconomic status and racial and ethnic differences in the

prevalence of diabetes, heart conditions, hypertension, and arthritis. They find that socioeconomic status plays a 

greater role in explaining racial and ethnic differences in individuals’ ability to function once someone is ill, rather 

than explaining the differences in the probability of becoming ill. 

 48
Section 3: Changing Racial and Ethnic Composition of the Population

Brown and Nichols-English (1999) discuss the implications of patient diversity for pharmacists. 

People of different cultures—which they broadly defined by race and ethnicity, language, 

socioeconomic group, family structure, and geographic location—have different perceptions, on 

average, of health care issues. Their perceptions might differ in the following: “(1) [the 

constitution of] disease and its causation; (2) appropriate health-care-seeking behavior; (3) the 

quality and usefulness of medical encounters; (4) effective approaches to healing, including both 

conventional and alternative practices; and (5) the role of family in health care (p. 61).” Brown and 

Nichols-English discuss the importance of educating pharmacists on providing culturally 

competent care to reduce drug-related problems—e.g., noncompliance, adverse effects, and suboptimal dosing. 

3.3.1 Physician Supply 

Relative to the overall population, minorities are underrepresented in the physician workforce for 

all races and Hispanic ethnicity with the exception of the population of Asian descent. Exhibit 3.5 

shows the distribution of the physician workforce by race and ethnicity in 1999. For those 

physicians whose race and ethnicity is recorded in the AMA master file, 75.4 percent are nonHispanic white, 3.6 percent are African American, 4.9 percent are Hispanic, 12.6 percent are 

Asian, 0.1 percent are American Indian or Alaskan Native, and the remaining 3.5 percent are of 

various other races. 

 49
Section 3: Changing Racial and Ethnic Composition of the Population

 Exhibit 3.5. Race Distribution of the Physician Workforce, 1999 

Black

3.6%

Hispanic

4.9%

Asian

12.6%

Other

3.5%

White

75.4%

Source: American Medical Association, Physician Characteristics and Distribution 

in the U.S., 2001-2002 Edition. 

The racial and ethnic composition of the physician workforce, however, varies substantially by 

specialty (Exhibit 3.6). The percent non-Hispanic white ranges from a high of 91.1 percent in 

aerospace medicine to a low of 65.2 percent in physical medicine and rehabilitation. Specialties 

with the highest representation of physicians of Asian descent are physical medicine and 

rehabilitation (20.6 percent), internal medicine (17.9 percent) and radiation oncology (17.4 

percent). African Americans have the highest representation in general preventive medicine (6.3 

percent), obstetrics/gynecology (6.2 percent) and pediatrics (4.8 percent). Hispanics have the 

highest representation in general practice (7.9 percent), child psychiatry (7.0 percent) and 

pediatrics (6.7 percent). 

A visual inspection of the specialties where physicians spend relatively more (less) time with 

African American and other minority patients (Exhibit 3.4) finds that these specialties tend to have

higher (lower) minority representation in the physician workforce. The three specialties, for 

example, shown in Exhibit 3.4 to have spent the greatest percentage of time with African 

American patients in the year 2000 were emergency medicine, obstetrics/gynecology, and 

pediatrics; from Exhibit 3.6, we see that each of these specialties had in 1999 an above-average 

 50
Section 3: Changing Racial and Ethnic Composition of the Population

representation of African American physicians compared to the workforce at large (4.1, 6.2, and 

4.8 percent respectively, compared to an overall average of 3.6 percent for all specialties 

combined). The two specialties shown in Exhibit 3.4 to have spent the lowest percentage of time 

with African American patients in the year 2000 were general surgery and other surgical 

specialties, groups characterized in Exhibit 3.6 by a below-average representation of African 

Americans. Similar observations apply, with some exceptions, to Hispanics and other minorities. 

The exceptions are as follows: (a) radiologists spent a large percentage of time with “other 

minority” patients (31 percent) despite the fact that other minorities constituted a distinctly belowaverage percentage of the radiologist workforce (12.7 percent as against an overall average of 21 

percent), and (b) cardiologists spent a low percentage of time with other minority patients (15 

percent) despite the fact that other minorities constituted an above-average percentage of the 

cardiologist workforce (26.3 percent compared to 21 percent for all specialties combined). 

Advocates for increased representation of minorities in the physician workforce cite both equity 

and efficiency reasons. One equity issue cited is providing greater access to care for minority 

populations who are disproportionately in designated physician shortage areas. Defining a 

“physician shortage area” as an area with fewer than 30 office-based primary care physicians per 

100,000 population, Komaromy et al. (1996) found that 57 percent of poor areas with a high 

percentage of African American and Latino residents could be classified as physician shortage 

areas. In comparison, Komaromy et al. found that only 13 percent of poor areas with a high 

percentage of non-Hispanic white residents could be classified as physician shortage areas. 

Intuitively, one might expect that poorer urban neighborhoods might naturally have fewer 

physicians per population. Komaromy et al. found, however, a stronger correlation between the 

physician supply and the proportion of residents in the community who are African American or 

Hispanic residents than the correlation between physician supply and an area’s average income

level. 

 51
Section 3: Changing Racial and Ethnic Composition of the Population

Exhibit 3.6. Percent Distribution of Physicians by Race and Ethnicity, in 1999 

Specialty

NonHispanic 

White 

African 

American Hispanic Asian Other

American Indian/

Alaskan Native

Total MDs 75.4 3.6 4.9 12.6 3.5 0.1

Aerospace Medicine 91.1 2.1 3.4 2.1 1.3 0.0

Allergy & Immunology 79.0 1.4 3.7 12.2 3.6 0.1

Anesthesiology 71.5 3.4 4.2 16.9 4.1 0.1

Cardiovascular Disease 71.1 2.4 4.9 15.2 6.2 0.1

Child Psychiatry 73.5 4.8 7.0 10.2 4.3 0.2

Colon/Rectal Surgery 81.6 1.8 5.1 9.8 1.7 0.0

Dermatology 87.4 2.5 2.7 5.9 1.4 0.0

Diagnostic Radiology 80.2 2.0 3.5 11.5 2.7 0.1

Emergency Medicine 82.4 4.1 4.2 7.3 1.8 0.1

Family Practice 79.2 4.1 5.4 8.8 2.3 0.2

Forensic Pathology 82.2 3.7 4.7 8.3 0.7 0.3

Gastroenterology 71.5 3.1 4.8 14.8 5.8 0.0

General Practice 75.5 2.2 7.9 13.6 0.7 0.0

General Preventive Med. 82.1 6.3 3.5 6.6 1.4 0.1

General Surgery 78.3 3.4 4.6 10.9 2.7 0.1

Internal Medicine 67.0 4.1 5.1 17.9 5.8 0.1

Medical Genetics 84.6 1.9 3.4 8.0 2.2 0.0

Neurology 72.8 1.9 5.2 14.1 5.9 0.0

Neurological Surgery 82.7 2.5 3.9 7.8 3.1 0.1

Nuclear medicine 71.0 2.0 5.7 16.8 4.4 0.0

Obstetrics/Gynecology 77.2 6.2 5.3 9.3 2.0 0.1

Occupational Med. 88.6 2.9 3.0 4.6 0.9 0.1

Ophthalmology 84.6 2.2 2.9 7.9 2.4 0.1

Orthopedic Surgery 88.7 2.4 2.4 4.7 1.7 0.1

Otolaryngology 84.3 2.0 3.2 8.7 1.7 0.0

Pathology-Anat/Clin 74.5 1.9 4.9 15.5 3.3 0.0

Pediatrics 68.6 4.8 6.7 15.7 4.0 0.1

Pediatric Cardiology 75.7 2.0 5.6 11.5 5.0 0.1

Physical Med/Rehab 65.2 4.4 6.2 20.6 3.7 0.1

Plastic Surgery 84.8 1.8 3.6 7.5 2.3 0.0

Psychiatry 75.0 3.2 5.6 12.7 3.4 0.1

Pulmonary Diseases 75.7 2.6 5.2 12.3 4.2 0.1

Radiology 85.7 1.4 2.2 8.8 1.7 0.0

Radiation Oncology 73.5 2.7 3.2 17.4 3.2 0.0

Thoracic Surgery 67.2 4.2 5.6 10.5 12.2 0.3

Urological Surgery 82.7 2.8 3.5 8.7 2.2 0.1

Other 88.2 2.0 3.4 5.5 0.9 0.0

Source: American Medical Association, Physician Characteristics and Distribution in the U.S., 2001-2002 Edition.

The Komaromy et al. study found that of many possible characteristics of a physician, the best 

predictor for whether the physician cared for a high percentage of African American patients was 

whether the physician was African American. After controlling for the proportion of African 

American residents in the community, this analysis indicated that the proportion of African 

American patients cared for by African American physicians was 25 percentage points higher than 

the average proportion of African American patients cared for by physicians of other races. Other 

 52
Section 3: Changing Racial and Ethnic Composition of the Population

variables, such as the ranking of the physician’s medical school, experience, and type of hospital 

had insignificant effects. The authors suggest that the personal choice of the physician is the most 

likely explanation for the phenomenon that African American physicians are more likely than nonHispanic white physicians to treat African American patients. Given that the ranking of the 

physician’s medical school is not significant in predicting the race of the physician’s patients, the 

authors conclude that top African American medical school graduates are themselves choosing to 

practice in poorer, predominantly minority areas.

A study by Moy and Bartman (1995) found that minority patients were more than 4 times as likely 

as non-Hispanic white patients to receive care from minority physicians. Moy and Bartman note 

that any solution that attempts to increase the proportion of minority physicians must also take into 

account the financial hardships they face. On average, minority physicians tend to treat lowerpaying uninsured and Medicaid patients. Moy and Bartman estimate Medicaid fees for physician 

services as averaging only 47 percent of private insurance fees. Because up to 29 percent of lowincome patients are receiving care from minority physicians, these physicians must bear a 

disproportionately higher share of the financial burden associated with poorer patients. Medicaid 

insured 45 percent of the patients seen by African American physicians and only 18 percent of 

patients seen by non-Hispanic white physicians. Hispanic physicians cared for more uninsured 

patients than physicians of other ethnic groups. On average, 9 percent of their patients were 

uninsured compared to 6 percent for non-Hispanic white physicians. 

Physicians whose clientele is composed of a high percentage of Medicaid and uninsured patients 

may also have a more difficult time securing managed care contracts. Bindman et al. (1998) 

studied the frequency of denials or terminations of managed care contracts experienced by primary 

care physicians. They found that physicians with higher proportions of uninsured patients were 4 

times more likely to have a contract terminated or denied. There was also a significant positive 

correlation between the number of uninsured patients a physician saw and the frequency of denials 

from managed care contracts for these physicians. Latino physicians had significantly lower odds 

of having more than 10 percent of their patients enrolled in a managed care plan: 23 percent of 

Latino physicians in group practice are in no way affiliated with an HMO. 

 53
Section 3: Changing Racial and Ethnic Composition of the Population

One reason for the imbalance, noted by Mackenzie et al. (1999), might be that solo practices are 

associated with lower levels of participation in managed care, and minority physicians tend to have 

solo practice settings. MacKenzie et al., through a survey of physicians who tended to treat 

managed care patients, found that 56 percent claimed they had difficulty referring patients of 

varied ethnic backgrounds to specialists who met those patients’ cultural needs. The author 

expresses guarded optimism that as the idea of cultural competency within managed care gains 

momentum, managed care organizations will become increasingly aware of the importance of

‘ethnic matching’. As a result, they may attempt to recruit ethnic minority physicians as a way to 

attract and retain ethnic minority members. 

In 2000, minorities constituted 27 percent of the population age 18-34—the age group that reflects 

the population entering the workforce. By 2020, minorities will constitute approximately 45 

percent of the age 18-34 population. An estimated 15 percent of the population in this age group 

will be African American, and 30 percent will be Hispanic or a non-African American minority. 

As minorities constitute a larger portion of the population from which new health workers are 

drawn, minority representation in the physician workforce will naturally rise. Also, as noted by 

Libby, Zhou, and Kindig (1997), organizations such as the Bureau of Health Professions, the 

Institute of Medicine, the Association of American Medical Colleges, and others have made 

racial/ethnic equity in the physician workforce a high priority. 

As shown by Libby et al., however, racial parity in the physician workforce will likely not occur in 

the next few decades, although some gains in parity will be made. For five race/ethnicity groups, 

these authors forecast the number of physicians per 100,000 population of the same race or 

ethnicity as the physician. Their projection model constrains the race-specific physician-topopulation ratios to converge over time to an equilibrium of 218 physicians per 100,000 population 

by adjusting the racial composition of first-year graduate medical education cohorts. The soonest 

that racial parity is reached, given projected demographics, is around 2040. 

In summary, the literature and changing demographics suggests that increasing minority 

representation in the physician workforce will improve access to care for minority and vulnerable 

populations. Minorities face financial obstacles to become physicians, and once they become

physicians may face greater financial obstacles than non-minority physicians because of practice 

 54
Section 3: Changing Racial and Ethnic Composition of the Population

location or other factors. Increased racial/ethnic diversity of the U.S. population over the next few 

decades will naturally increase minority representation in the physician workforce. 

3.3.2 Nurse Supply 

Estimates from the 2000 Sample Survey of Registered Nurses (HRSA, 2001) indicate that 

approximately 86.6 percent of RNs are non-Hispanic white, 4.9 percent are non-Hispanic African 

American, 3.5 percent are Asian; 2 percent are Hispanic; 0.5 percent are American Indian or 

Alaskan Native, 0.2 percent are Native Hawaiian or Pacific Islander, and 1.2 percent are of two or 

more racial backgrounds (see Exhibit 3.7). Among minorities, Hispanics and African Americans 

are underrepresented in the registered nurse workforce relative to their proportion in the overall 

population. 

Exhibit 3.7 Racial/Ethnic Distribution of the Registered Nurse Workforce in 2000 

86.6%

4.9%

2.0%

0.7%

3.5%

1.2%

69.1%

12.1%

12.5%

0.8%

3.6%

1.6%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Caucasian 

(non-Hispanic)

African American

(non-Hispanic)

Hispanic

American Indian,

Alaskan Native,

Hawaiian/Pacific Islander

Asian

Two or more races

Percentage

% RN Population % U.S. Population

 Source: 2000 Sample Survey of RNs (HRSA, 2001). 

 55
Section 3: Changing Racial and Ethnic Composition of the Population

The literature on the relationship between race or ethnicity and the supply of nurses is substantially

smaller than the corresponding literature for physicians. 

Sechrist, Lewis, and Rutledge (1999) report that the nurse workforce in California is becoming 

more ethnically diverse. Although minorities are underrepresented in the current nurse workforce 

in California, the racial and ethnic mix of nursing school entrants more closely parallels the 

diversity of California’s population. The authors report that minority students, however, are less 

likely to graduate from nursing programs than their non-Hispanic white counterparts. The authors 

make several recommendations to improve ethnic diversity of the nurse workforce including 

outreach efforts to increase the number of minorities in nursing programs. They cite an 

unpublished study by Martin-Holland et al. that looks at strategies to improve ethnic diversity in 

the nurse workforce. Specifically, the study looks at (1) strategies that have been successful in 

recruiting and retaining ethnically diverse students in nursing programs, (2) barriers to nursing 

program success for ethnically diverse students, and (3) activities incorporated into nursing 

programs to improve cultural sensitivity of nursing school graduates. 

In 2000, approximately 61 percent of the female population age 18-34—the main source of new 

nurses—was non-Hispanic white. By 2020, the percentage will have decreased. Only half of all 

women age 18-34 will be non-Hispanic white; African Americans and all other minorities 

(including white Hispanics) will constitute 16 percent and 33 percent, respectively, of the female 

population age 18-34. As minorities constitute a growing proportion of the female population in 

this group, minority representation in the nurse workforce will naturally rise. 

Furthermore, the growing nurse shortage in the U.S. has encouraged some employers to recruit 

foreign nurses. Recruiting foreign nurses will increase the diversity of the nurse workforce; 

however, many of the countries exporting nurses to the U.S. may themselves in turn face an 

inadequate supply of nurses.9

 

9

 See, for example, the Wall Street Journal article: Shortage of Nurses Hits Hardest Where They Are Needed the Most: 

Nurse Shortage Shows How Labor Markets Go Global (Zachary, 2001). 

 56
Section 4: Geographic Location of the Population

4 GEOGRAPHIC LOCATION OF THE POPULATION 

Major Findings: 

• Geographic variation in population 

growth rates and determinants of 

health worker demand and supply 

highlight the importance of developing 

forecasting models that can make 

State-level and sub-State level 

forecasts. 

• Although an increasing proportion of 

the U.S. population resides in urban 

areas, a substantial proportion of the 

population will continue to reside in 

rural areas. Many of these rural areas 

are currently designated as physician 

shortage areas. 

• Pockets of urban areas will continue

to have a high concentration of 

minorities. Many of these areas are 

currently designated as physician 

shortage areas. 

Discussion of the adequacy of the health care workforce is often framed in the context of a 

maldistribution of workers. An inadequate supply of health workers is often a local or regional 

phenomenon, frequently accompanied by surpluses 

elsewhere. Consequently, national forecasts of supply 

and demand can mask inadequacies of supply at the local 

level. 

Trends in geographic location of the population that have 

important implications for the future health care

workforce include the following. First, there is 

substantial variation in population growth and other 

factors that affect the supply of and demand for health 

professionals. This phenomenon highlights the 

importance of models that can forecast at the State and 

local level. 

Second, a significant proportion of the population will 

continue to reside in rural areas and have less access to 

health care services than the population residing in urban 

areas. 

Third, some urban areas will continue to have a high concentration of minorities. These areas are 

often characterized as having fewer economic resources per capita, greater health care needs, and 

less access to health care services than surrounding areas. 

4.1 Population Projections and Regional Growth Patterns 

According to the U.S. Census Bureau (Campbell, 1997), all regions of the country will grow 

over the next 25 years, with the West and the South growing at the fastest rate (Exhibit 4.1). As 

the population continues to rapidly grow in these regions, the demands for health care will also 

increase. 

 57
Section 4: Geographic Location of the Population

Exhibit 4.1 Population Projections by Region

Projections by Year (thousands of resident population) 

Region 1995 2000 2005 2010 2015 2020 2025

Northeast 51,466 52,107 52,767 53,692 54,836 56,103 57,392

Midwest 61,804 63,502 64,825 65,915 67,024 68,114 69,109

South 91,890 97,613 102,788 107,597 112,384 117,060 121,448

West 57,596 61,413 65,603 70,512 75,889 81,465 87,101

Total 262,755 274,634 285,981 297,716 310,133 322,742 335,050

Source: United States Census Bureau (Campbell, 1997). 

The uneven regional growth of the population has both short-term and long-term ramifications 

for the health workforce. Regions of the country that experience rapid growth in population 

could experience temporary shortages of some health professionals, such as physicians, who 

might be less mobile than the population at large. Efforts by some localities to recruit specific 

growth industries—e.g., high-tech industries—without a balanced approach to recruit health 

professionals could cause a short-term strain on the local health care infrastructure. Areas of the 

United States that are already experiencing physician shortages and that are high-growth areas 

might see more severe short-term inadequacies in the health workforce. For example, the Census 

Bureau estimates that Texas will be one of the fastest growing States over the next 20 years. 

However, according to the Bureau of Primary Health Care, Texas currently has one of the 

highest number of physician shortage areas in the country, understandable in view of its size. 

Not only does this trend appear in Texas, but many smaller southern States also face a 

combination of high growth and a large number of shortage areas. 

Regional differences in physicians per population and nurses per population do not necessarily 

reflect inadequacies in the health care workforce. As discussed previously, demand for health 

care services is highly correlated with the age distribution of the population, and there is 

substantial geographic variation in the age distribution of the population. For example, the 

proportion of the population age 65 and older is much greater in Florida (18), West Virginia (17) 

and North Dakota (15) than it is in Alaska (5), Utah (8) and Colorado (9). 

In addition, there exists substantial variation in other determinants of demand for health care 

services such as the characteristics of the health care operating environment, economic 

conditions, and lifestyle. Douglass (1995) projected the future supply of family physicians on a 

State-by-State basis and found substantial regional variation in physician supply and needs. One 

implication of the uneven population growth and geographic variation in the determinants of 

 58
Section 4: Geographic Location of the Population

supply and demand is the need to develop forecasting models that can forecast at the State or 

sub-State level. 

The NDM forecasts demand for nurses at the State level. Preliminary demand forecasts 

compared to current and future supply forecasts show substantial variation across States in the 

adequacy of the nurse workforce—both now and in the future (Dall and Hogan, 2002). 

4.2 Evolving Trends in Urbanization 

Although the proportion of the U.S. population living in metropolitan areas will continue to 

grow, a large proportion of the population will continue to live in rural areas. A substantial body 

of literature describes the inadequacies of the physician workforce in rural areas, and over 65 of 

the Health Professional Shortage Areas (HPSAs) are in rural areas. 

Between 1990 and 2000, the population in metropolitan areas increased by nearly 14 percent, 

whereas the population in non-metropolitan areas grew by only 10 percent (Exhibit 4.2). One 

reason for this phenomenon is a matter of classifications: geographic regions formerly designated 

as rural areas are becoming more metropolitan and were re-designated as metropolitan areas. 

Another reason is immigration: immigrants disproportionately settle in metropolitan areas. A 

third reason is migration from rural to urban areas, although this effect has been small. The 

Census Bureau (March 2001) reports that net migration out of rural areas totaled only 137,000 

between 1998 and 2000. 

The "metropolitanization" of the country could help alleviate the problems of an inadequate 

supply of physicians in some rural locations as the population in these areas increases above the 

threshold required to support a more comprehensive health workforce. 

 59
Section 4: Geographic Location of the Population

Exhibit 4.2 Population Growth by Metropolitan Status and Size 

Population 

Population Size April 1, 1990 April 1, 2000

Percent 

Change 

1990-2000 

2000 

share of

total

United States 248,709,873 281,421,906 13.2 100.0

Total Metropolitan 198,402,980 225,981,676 13.9 80.3

 5 million or greater 75,874,152 84,064,274 10.8 29.9

 2 – 5 million 33,717,876 40,398,283 19.8 14.4

 1- 2 million 31,483,749 37,055,342 17.7 13.2

 250,000 – 1 million 39,871,391 45,076,105 13.1 16.0

 250,000 or fewer 17,455,812 19,387,675 11.1 6.9

Non-Metropolitan 50,306,893 55,440,227 10.2 19.7

Source: United States Census Bureau. 

A substantial proportion of the population will continue to reside in rural areas during the 

foreseeable future. When modeling the supply of health professionals in rural and underserved 

areas, analysts might consider the following obstacles to increasing physician supply in these 

shortage areas, as reported in the literature. 

• Connor, Hillson and Krawelski (1995) suggest that physicians locate in areas with other 

physicians in order to benefit from the professional synergism that develops when there is an 

established population of physicians. Similarly, Brasure et al. (1999) found a general 

aversion to rural practice may exist among urban professionals, but there is less resistance to 

enter an underserved market once at least one health provider has settled there. Efforts to 

model the supply of physicians in underserved areas might identify “forerunner” specialties 

and analyze patterns of physician location. 

• Olchanski et al. (1998) found that the average age of physicians in rural areas of Virginia is 

increasing, raising concerns that physician shortages in these areas will be exacerbated when 

these physicians retire. Furthermore, he speculates that this phenomenon could be applicable 

to other parts of rural America. 

• Rabinowitz et al. (1999), in a study of rural physicians in Pennsylvania, found that one of the 

most critical factors in determining whether a physician will practice in a rural environment 

is the extent of the physician’s rural background. Models of physician supply might 

incorporate an urban/rural dimension that takes into account the propensity of physicians to 

practice in physician shortage areas based on the background and demographic 

characteristics of medical students and the existing physician workforce. 

 60
Section 4: Geographic Location of the Population

A disincentive to physicians choosing to practice in rural settings is lower earnings potential. For 

heavily-indebted physicians exiting medical school, practicing in suburban areas where there is 

greater economic activity can be more enticing than practicing in a rural area. 

Government and private organizations have implemented various programs and grants to 

encourage physicians to practice in underserved, rural areas. For example, the State of Illinois, 

along with the University of Illinois College of Medicine at Rockford, has implemented a 

program designed to improve the supply of physicians to these areas. According to Stearns et al. 

(2000), this program has been reasonably successful, with 69 percent of the graduates choosing 

to enter rural practices. Efforts to model physician supply might incorporate estimates of the 

impact of programs that try to influence where physicians will practice. Similarly, some States 

are offering grants to people in nursing programs who agree to work in rural or underserved 

areas for a specific length of time following graduation. 

Some researchers have argued that international medical graduates (IMGs) can be used to 

augment the physician workforce in underserved areas. Mick et al. (2000, 1999) have shown 

that the IMGs are more likely than U.S. medical graduates to locate in rural areas with high rates 

of infant mortality, fewer per capita economic resources, a high proportion of minorities, a 

disproportionate number of elderly, and low physician-to-population ratios. Baer et al. (1999) 

found that IMGs were also fulfilling an important role in community health centers. These 

centers tend to be located in physician shortage areas, so these researchers suggest that the role 

of IMGs is indispensable in the rural setting. As hospitals in rural areas close, the authors assert 

that community health center clinics are the most effective way for underserved populations to 

receive the health care they require and that IMGs help fill a ‘safety net’ role. 

Not all researchers agree that IMGs help alleviate physician shortages in underserved areas. A 

study conducted by Politzer, Cultice, and Meltzer (1998) found that the geographic distribution 

of physicians has become less even. The study also argued that IMGs, rather than helping to 

mitigate this trend, had in fact contributed to its severity. The authors state that the majority of 

IMGs choose not to work in areas with a physician shortage, and that the contributions others 

note are overstated. 

 61
Section 4: Geographic Location of the Population

4.3 Urban Demography and the Effects on Physician Locations 

Pockets of the population will continue to contain high concentrations of minorities. These 

pockets, generally located in urban areas, are often characterized by lower average levels of 

economic resources, greater average health care needs, and less access to health care services. 

COGME (1998) reports that although there appears to be an oversupply of physicians, most of 

the oversupply is located in affluent urban and suburban areas. Additionally, specialists are 

especially prone to locating in more affluent areas. The traditionally poor areas of the city exhibit 

a unique need, as they are often demographically independent from the more affluent areas in the 

same region. 

One of the most sensitive populations is the immigrant population, especially those with little or 

no English proficiency. Members of this population tend to locate in areas that traditionally 

consist of low-income households and are more likely to live in cities than non-metro areas. 

According to the 2000 census, 5.1 percent of foreigners live in rural areas, compared to 20.7 

percent of native-born people. This means that as immigration increases, there may be greater 

pressure placed on urban community hospitals, which typically serve more non-English speaking 

people (Gaskin and Hadley, 1999). According to Gaskin and Hadley, these hospitals face a 

higher level of physician and health care professional shortages, thus degrading the level of care 

provided to the underserved population. As immigration increases in the near future, this strain 

placed on the community hospitals may increase. 

In addition to the use of IMGs in rural areas, Mick has suggested that they may help relieve 

shortages in the urban areas as well. According to his study, IMGs tend to locate in less affluent 

areas within a city and are willing to work for a lower salary. Additionally, as discussed 

previously, some policy makers advocate increasing the efforts made towards recruiting 

minorities into the health care professions. They claim that these individuals may be willing to 

work in shortage areas, as well as being able to overcome some of the language barriers that exist 

in some of these areas (Trevino 1994, Komarmony et al., 1996). 

 62
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

5 MODELING THE IMPACT OF CHANGING DEMOGRAPHICS ON THE FUTURE

DEMAND FOR HEALTH PROFESSIONALS 

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. 

 63
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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. 

 64
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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 shaded boxes (i.e., boxes 2, 4, and 6) 

indicate areas of the PARM where the user can easily change the forecasting assumptions. 

 

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. 

 65
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

Exhibit 5.1 PARM Structure 

1. U.S. Population

(by age, sex, race)

2. Insurance Rates

% Fee-for-service

% HMO

% Uninsured

x

3. U.S. Population

(by age, sex, race, = insurance)

4. Encounters per Person

(by age, sex, race, x insurance, setting, specialty)

= 5. Total Encounters

(by age, sex, race,

insurance, setting, specialty)

x

6. Minutes per Encounter

(by age, sex, race,

insurance, setting, specialty) = 7. Total Required Minutes

(by age, sex, race,

insurance, setting, specialty)

8. Annual Patient Care

Minutes per Physician

(by setting, specialty) = 9. Total Requirements 

for Patient Care Activities

(by age, sex, race,

insurance, setting, specialty)

10. Adjust to include DOs and

Physicians not in Patient Care

(by specialty)

11. Model Calibration

(by specialty)

÷

12. Total Requirements

(by age, sex, race,

insurance, setting, specialty)

 66
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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

Year 

Race Sex Age 1999 2000 2005 2010 2015 2020

Men 0-17 22,737 22,628 22,042 21,315 21,067 21,143

18-34 21,373 21,223 21,069 21,375 21,641 21,174

35-54 29,670 29,974 29,654 27,994 25,887 24,596

55-64 9,027 9,231 11,341 13,104 14,371 14,675

65-74 6,871 6,846 6,894 7,854 9,817 11,599

75+ 5,141 5,255 5,686 6,019 6,367 7,341

Men Total 94,818 95,158 96,685 97,661 99,150 100,528

Women 0-17 21,501 21,399 20,851 20,152 19,902 19,965

18-34 21,003 20,842 20,597 20,842 21,116 20,672

35-54 29,896 30,214 29,996 28,385 26,258 24,909

55-64 9,594 9,796 11,931 13,730 14,967 15,241

65-74 8,233 8,132 7,919 8,789 10,750 12,485

75+ 8,887 9,011 9,352 9,318 9,395 10,184

Non-Hispanic White 

Women Total 99,113 99,395 100,646 101,216 102,389 103,456

Non-Hispanic White Total 193,931 194,553 197,332 198,877 201,539 203,984

Men 0-17 5,483 5,532 5,799 5,987 6,282 6,619

18-34 4,305 4,319 4,474 4,765 5,052 5,276

35-54 4,374 4,483 4,768 4,747 4,697 4,722

55-64 1,029 1,057 1,317 1,637 1,969 2,149

65-74 660 666 707 864 1,096 1,404

75+ 400 408 450 473 517 594

Men Total 16,252 16,465 17,515 18,472 19,613 20,763

Women 0-17 5,310 5,354 5,593 5,754 6,017 6,322

18-34 4,643 4,653 4,800 5,053 5,338 5,566

35-54 4,999 5,125 5,477 5,626 5,582 5,602

55-64 1,277 1,313 1,634 2,102 2,512 2,735

65-74 937 947 1,009 1,136 1,423 1,802

75+ 791 802 864 876 949 1,071

African American 

Women Total 17,957 18,193 19,376 20,547 21,821 23,096

African American Total 34,209 34,658 36,892 39,020 41,434 43,859

Men 0-17 8,676 8,899 10,050 11,092 12,317 13,752

18-34 8,340 8,453 9,160 9,063 10,324 11,398

35-54 6,221 6,488 7,627 10,078 10,741 11,450

55-64 1,298 1,357 1,786 2,408 3,051 3,710

65-74 761 791 945 1,224 1,608 2,111

75+ 418 443 585 764 963 1,226

Men Total 25,713 26,431 30,153 34,630 39,004 43,647

Women 0-17 8,269 8,482 9,577 10,570 11,739 13,100

18-34 7,390 7,546 8,439 9,077 10,303 11,348

35-54 6,288 6,543 7,618 9,524 10,354 11,259

55-64 1,452 1,520 2,015 2,709 3,339 3,943

65-74 976 1,009 1,176 1,486 1,941 2,495

75+ 642 681 901 1,182 1,455 1,810

Other (including 

Hispanic White) 

Women Total 25,017 25,780 29,725 34,549 39,130 43,955

Other Total 50,730 52,211 59,877 69,179 78,134 87,602

Total U.S. Population 278,870 281,422 294,100 307,075 321,107 335,444

Source: Modified version of Census Bureau middle series projections. 

 67
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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. 

 68
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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 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). 

 

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. 

 69
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

Exhibit 5.3 Impact of Changing Demographics on Requirements for Physicians: 

Status Quo Scenario 

Medical Specialty 2000 2005 2010 2015 2020

% Change 

2000 to 2020

Total Physicians (MDs and DOs) 781,282 831,447 891,687 959,996 1,038,234 33

Total Patient Care Physicians 733,342 780,266 836,594 900,574 973,840 33

General Primary Care 268,710 283,632 300,651 320,992 344,907 28

 GP & FP 109,571 115,583 122,512 130,358 139,252 27

 General Internal Med. 106,411 114,197 123,645 134,406 146,885 38

 Pediatrics 52,728 53,852 54,494 56,228 58,770 11

Medical Specialties 96,926 104,145 113,200 123,560 135,331 40

 IM Subspecialties 40,205 43,336 47,301 51,841 56,955 42

 Cardiovascular Diseases 20,828 22,675 25,143 28,172 31,690 52

 Other Medical Specialties 35,893 38,133 40,756 43,548 46,687 30

Surgery 161,160 171,133 183,519 197,706 213,196 32

 General Surgery 37,604 40,605 44,473 48,805 53,641 43

 Obstetrics/Gynecology 43,068 44,547 46,168 47,802 48,962 14

 Otolaryngology 9,839 10,326 10,877 11,520 12,248 24

 Orthopedic Surgery 23,225 24,804 26,736 28,965 31,596 36

 Urology 10,690 11,455 12,448 13,696 15,122 41

 Ophthalmology 18,876 20,099 21,650 23,643 25,972 38

 Other Surgical Specialties 17,858 19,296 21,167 23,276 25,655 44

Other Patient Care 206,545 221,355 239,224 258,315 280,405 36

 Psychiatry 44,495 46,877 49,340 51,537 54,116 22

 Anesthesiology 36,762 39,547 43,188 47,499 52,493 43

 Emergency Medicine 23,494 24,813 26,206 27,802 29,505 26

 Radiology 30,354 33,218 36,919 41,005 45,855 51

 Pathology 16,757 18,229 20,174 22,019 24,167 44

 Other Specialties 54,683 58,672 63,398 68,453 74,270 36

Non Patient Care 47,940 51,182 55,093 59,422 64,394 34

Total U.S. Population (Thousands) 281,422 294,100 307,075 321,107 335,444 19

 70
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

Exhibit 5.4 Forecasts of Physician Requirements Under the Status Quo Scenario 

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

2000 2005 2010 2015 2020

Total Physicians

Surgery Other Patient Care

Non Patient Care

General Primary Care Medical Specialties

• 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 

 71
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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 

Specialty 2000 2010 2015 2020

% Change 

2000 to 2020

Total Physicians (MDs and DOs) 781,282 823,465 874,019 931,208 996,387 28

733,342 772,936 820,389 874,069 935,248 28

General Primary Care 268,710 284,113 301,283 321,556 345,039

 GP & FP 109,571 115,576 122,428 130,168 138,846 27

 General Internal Med. 106,411 114,438 123,929 146,730 38

 Pediatrics 52,728 54,099 54,926 56,806 59,463 13

Medical Specialties 96,926 110,381 119,005 128,730 33

 IM Subspecialties 40,205 42,759 46,041 49,799 53,993 34

20,828 22,235 24,192 26,629 29,440 41

 Other Medical Specialties 35,893 37,856 40,149 42,577 45,297

Surgery 161,160 165,957 172,525 180,173 188,291 17

 General Surgery 37,604 38,974 40,943 45,378 21

 Obstetrics/Gynecology 43,068 43,721 44,495 45,260 45,567 6

 Otolaryngology 9,839 10,214 10,498 10,847 10

 Orthopedic Surgery 23,225 23,995 25,001 26,169 27,547 19

10,690 11,115 11,737 12,567 13,511 26

 Ophthalmology 18,876 19,746 20,915 22,491 24,378

 Other Surgical Specialties 17,858 18,402 19,219 20,102 21,064 18

Other Patient Care 206,545 220,016 236,199 273,187 32

 Psychiatry 44,495 46,925 49,329 51,398 53,782 21

 Anesthesiology 36,762 43,188 47,499 52,493 43

 Emergency Medicine 23,494 24,285 25,103 26,079 27,122 15

30,354 33,218 36,919 41,005 45,855 51

 Pathology 16,757 18,229 20,174 22,019 24,167

 Other Specialties 54,683 57,812 61,487 65,333 69,768 28

Non Patient Care 47,940 50,528 53,630 61,139 28

Total U.S. Population (Thousands) 281,422 294,100 307,075 321,107 335,444 19

2005

Total Patient Care Physicians 

28

134,583 

102,850

 Cardiovascular Diseases 

26

43,086 

10,003

 Urology 

29

253,334 

39,547

 Radiology 

44

57,140

 72
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

• 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 feefor-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). 

 73
Section 5: Modeling the Impact of Changing Demographics on the Future Deman

Exhibit 5.6 Forecasted Physician Requirements Under Five Scenarios

Scenario 1:

Status Quo 

Scenario 2:

Baseline 

Scenario 3:

Universal Coverage

Scenari

100HM

Specialty 2000 2020 2000 2020 2000 2020 2000 

Total Physicians (MDs and DOs) 1,038,234 781,282 996,387 817,615 1,092,381 781,889 1Total Patient Care Physicians 733,342 973,840 935,248 767,420 1,024,551 735,131 General Primary Care 268,710 344,907 268,710 345,039 281,421 362,692  GP & FP 109,571 139,252 109,571 138,846 112,652 143,979 107,348  General Internal Med. 106,411 146,885 106,411 146,730 112,347 154,794 121,979  Pediatrics 58,770 52,728 59,463 56,422 63,919 65,219 Medical Specialties 96,926 135,331 128,730 101,452 141,911 101,499 

IM Subspecialties 40,205 56,955 40,205 53,993 60,263 41,819 

 Cardiovascular Diseases 20,828 31,690 20,828 29,440 21,549 32,742  Other Medical Specialties 35,893 46,687 35,893 45,297 37,364 48,906 38,550 

781,282

733,342

294,546

52,728

96,926

42,540

21,130

Surgery 161,160 213,196 161,160 188,291 170,919 226,585 161,462  General Surgery 37,604 53,641 37,604 45,378 39,463 56,421 37,187  Obstetrics/Gynecology 43,068 48,962 45,567 48,880 56,185 44,818  Otolaryngology 9,839 12,248 10,847 10,294 12,856 7,985  Orthopedic Surgery 23,225 31,596 27,547 24,741 33,604 22,066  Urology 10,690 15,122 13,511 11,175 15,755 11,236  Ophthalmology 18,876 25,972 24,378 18,704 25,915 20,194  Other Surgical Specialties 17,858 25,655 21,064 17,663 25,850 17,977 Other Patient Care 206,545 280,405 273,187 213,627 293,363 177,624  Psychiatry 44,495 54,116 53,782 42,133 52,752 31,961  Anesthesiology 36,762 52,493 52,493 38,804 55,424 38,804  Emergency Medicine 23,494 29,505 27,122 23,606 30,114 13,086  Radiology 30,354 45,855 45,855 32,865 49,649 32,865  Pathology 16,757 24,167 24,167 17,989 26,011 17,989  Other Specialties 54,683 74,270 69,768 58,230 79,413 42,918 Non Patient Care 47,940 64,394 61,139 50,195 67,830 46,759 

43,068 

9,839 

23,225 

10,690 

18,876 

17,858 

206,545 

44,495 

36,762 23,494 

30,354 

16,757 

54,683 

47,940 

 74
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

Exhibit 5.7 Forecasts of Physician Requirements in 2000 Under Alternative Scenarios 

781,282 781,282

817,615

781,889

802,356

750,000

770,000

790,000

810,000

830,000

Status Quo Baseline Universal

Coverage

Universal HMO

Coverage

Non-minority

Rates

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

1,038,234

996,387

1,092,381

1,059,907

1,072,048

990,000

1,010,000

1,030,000

1,050,000

1,070,000

1,090,000

1,110,000

Status Quo Baseline Universal

Coverage

Universal HMO

Coverage

Non-minority

Rates

 75
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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: 

• 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

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

optometrists: 

• 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: 

• 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 

 

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. 

 76
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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 PartB 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. 

APMA also publishes data indicating the hours per week, weeks per year, and visits per week 

of the typical podiatrist. 

13

 

13 American Podiatric Medical Association, http://www.apma.org/faqgeneral.html.

 77
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

• 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 

Profession 2005 2010 2015 2020

% Change 

2000 to 2020

Physical Therapy 120,410 125,476 130,636 136,235 18

Optometry 30,468 31,825 34,900 36,576 20

Podiatry 14,066 14,916 15,910 17,030 28

Total U.S. Population (Thousands) 281,422 294,100 307,075 321,107

2000

142,065

33,270

13,320

335,444 19

Exhibit 5.10 Forecasted Requirements for Physical Therapists, Optometrists, and 

Podiatrists Under Alternative Scenarios

Physical Therapy Optometry

2000 2000 2020 2000

1: Status Quo 120,410 30,468 36,576 13,320

2: Baseline 120,410 30,468 39,326 13,320

3: Universal Coverage 126,163 32,233 38,735 14,034

4: 100HMO 137,111 36,793 46,781 17,258

5: Non-minority Rates 122,301

Podiatry

Scenario 2020 2020

142,065 17,030

165,360 18,410

149,291 17,935

171,790 23,297

145,128 30,925 37,286 13,536 17,391

 78
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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? 

Exhibit 5.11 visually depicts how the NDM combines input databases and forecasting equations to 

answer these four questions. The NDM contains two major components: (1) the data and equations 

to forecast future demand for health care services, and (2) the data and equations to forecast future 

nurse staffing patterns. 

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: 

 79
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

• 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 baseyear 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. 

 80
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

• Step 2, calibrate the model by calculating multiplicative adjustment factors that equate baseyear 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 

Trends in health care 

market conditions, 

economic conditions, 

patient acuity, etc. 

Population 

projections 

Health care use 

equations 

Nurse staffing intensity 

equations 

Health care use 

projections 

Per capita 

healthcare 

utilization 

Nurse demand projections

 81
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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 costcutting 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. 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). 

14

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 

 

14 Projections under this scenario assume no change in average patient acuity for hospital inpatient days, visits, and

nursing facility residents. 

 82
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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. 

 83
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

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

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

1996 2000 2005 2010 2015 2020

Number of FTE Nurses

Registered Nurses Licensed Practical Nurses Nurse Aides & Home Health Aides

 84
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

Exhibit 5.13 Forecasts of FTE Nurse Demand: Status Quo Scenario 

Base Year Forecasts

Setting 2000 2005 2010 2015

% Increase 

2000-2020

Registered Nurses

1,889,326 1,964,920 2,075,690 2,198,904 2,505,747 28

Hospitals 1,233,403 1,303,924 1,386,817 1,486,728

1996 2020

Total 2,342,782

1,165,688 1,599,109 30

 ST, inpatient 828,316 875,173 925,824 986,312 1,059,597 1,142,113 31

 ST, outpatient 69,281 73,068 75,981 79,209 83,036 87,170 19

 ST, emergency 88,699 92,989 99,918 104,133 108,633 17

 LT hospitals 179,392 192,173 221,377 239,962 261,193 36

Nursing facilities 147,722 160,442 204,611 221,091 38

Doctors’ offices 145,941 154,727 173,528 180,386 17

Home health 145,754 115,393 130,594 141,294 155,675 35

Occupational health 19,180 20,040 22,241 22,390 12

School health 53,628 57,638 61,060 62,244 8

Public health 94,194 99,758 107,337 111,491 115,785 16

Nurse education 43,322 44,982 53,477 57,101 27

Other health care 73,898 78,537 88,354 91,966 17

Licensed Practical Nurses

Total 604,151 644,026 687,281 734,242 787,329 30

Hospitals 163,768 175,206 188,937 204,385 33

 ST hospitals 144,262 154,288 166,317 179,821 32

 LT hospitals 18,288 19,506 20,919 22,620 24,564 34

Nursing facilities 231,831 253,290 274,568 298,689 41

Home health 45,178 48,151 52,150 57,491 36

Other health care 195,778 203,250 210,633 218,588 226,764 16

Nurse Aides/Auxiliaries

1,487,915 1,487,792 1,593,810 1,708,561 1,835,164 1,983,582 33

274,029 289,662 306,621 326,545 350,653 377,971 30

 ST hospitals 218,640 230,699 243,898 259,508 278,396 299,731 30

55,389 58,963 62,723 67,037 72,257 78,239 33

607,127 660,490 721,720 787,314 852,441 926,112 40

391,798 309,643 329,242 350,336 35

Other health care 214,961 244,366 253,193 262,322 15

96,265

205,854

174,860 189,857

160,804 166,904

122,744

20,984 21,826

59,657 60,419

103,520

47,520 50,279

81,678 84,872

578,444

145,405 154,105

128,269 135,817

17,135 

194,670 211,863

53,633 42,405

184,736 

Total

Hospitals

 LT hospitals

Nursing facilities 

Home health 378,876 417,177

227,997 236,228

Source: The Nursing Demand Model: Development and Baseline Forecasts (Dall and Hogan, 2002). 

 85
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

Exhibit 5.14. Forecasts of FTE Nurse Demand: Baseline Scenario 

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

1996 2000 2005 2010 2015 2020

Number of FTE Nurses

Registered Nurses Licensed Practical Nurses Nurse Aides & Home Health Aides

 86
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

Exhibit 5.15 Forecasts of FTE Nurse Demand: Baseline Scenario 

Base Year Forecasts

Setting 1996 2000 2005 2010 2015 2020

% Increase 

2000-2020

Registered Nurses

Total 1,889,326 2,001,198 2,160,980 2,346,388 2,568,253 2,822,388 41

Hospitals 1,165,688 1,239,539 1,324,818 1,427,876 1,555,591 1,698,873 37

 ST, inpatient 828,316 874,747 930,249 999,094 1,086,838 1,187,002 36

 ST, outpatient 69,281 83,451 95,861 110,395 126,381 142,024 70

 ST, emergency 88,699 90,335 92,217 94,487 97,317 100,399 11

 LT hospitals 179,392 191,007 206,491 223,900 245,054 269,448 41

Nursing facilities 147,722 172,750 197,005 224,006 252,922 286,355 66

Doctors’ offices 145,941 155,001 166,394 178,789 191,585 204,674 32

Home health 145,754 132,016 157,334 187,482 226,241 275,633 109

Occupational health 19,180 20,040 20,984 21,826 22,241 22,390 12

School health 53,628 57,638 59,657 60,419 61,060 62,244 8

Public health 94,194 99,758 103,520 107,337 111,491 115,785 16

Nurse education 43,322 45,918 49,590 53,781 58,769 64,468 40

Other health care 73,898 78,537 81,678 84,872 88,354 91,966 17

Licensed Practical Nurses

Total 578,444 617,946 675,190 740,928 905,159 46

Hospitals 145,405 150,608 158,326 167,709 179,544 193,135 28

 ST hospitals 128,269 131,885 138,116 145,745 155,373 166,383 26

 LT hospitals 17,135 18,723 20,210 21,964 24,171 26,752 43

Nursing facilities 194,670 223,334 254,114 289,038 326,761 371,020 66

Home health 53,633 48,226 59,501 73,548 91,398 114,240 137

Other health care 184,736 195,778 203,250 210,633 218,588 226,764 16

Nurse Aides/Auxiliaries

Total 1,487,915 1,545,722 1,702,803 1,880,368 2,083,860 2,323,518 50

Hospitals 274,029 284,514 300,794 320,992 347,215 377,580 33

 ST hospitals 218,640 222,834 232,918 245,653 262,564 282,325 27

 LT hospitals 55,389 61,680 67,877 75,339 84,652 95,255 54

Nursing facilities 607,127 692,415 781,447 881,493 988,620 1,113,474 61

Home health 391,798 340,796 384,333 433,517 494,831 570,142 67

Other health care 214,961 227,997 236,228 244,366 253,193 262,322 15

816,291

Source: The Nursing Demand Model: Development and Baseline Forecasts (Dall and Hogan, 2002). 

 87
Section 5: Modeling the Impact of Changing Demographics on the Future Demand for Health Professionals 

 88
Section 6: Summary and Conclusions 

6 SUMMARY AND CONCLUSIONS 

Current and future demographics play an important role in determining the demand for and 

supply of health workers. This report discusses three major demographic trends and discusses 

their implications for the future demand for and supply of health professionals. Both a literature 

review and forecasts from two recently updated requirements forecasting models provide insight 

on the impact of changing demographics on the future health workforce. The major findings are 

as follows: 

Aging of the Population 

The aging of the population and the subsequent increase in the size of the elderly population is 

perhaps the most important demographic trend that will affect the future health workforce. The 

aging of the population will increase the total amount of health care services demanded, will 

change the mix of services demanded, and will have profound economic implications that could

affect future coverage policies and the provider reimbursement system. Key findings and 

implications from this literature review and analysis of the PARM and NDM include the 

following: 

• If health care consumption patterns and physician productivity remained constant over time, 

the aging population would increase the demand for physicians per thousand population from

2.8 in 2000 to 3.1 in 2020. Demand for full-time-equivalent RNs per thousand population 

would increase from 7 to 7.5 during this same period. 

• In 2000, physicians spent an estimated 32 percent of patient care hours providing services to 

the age 65 and older population. If current consumption patterns continue, this percentage 

could increase to 39 percent by 2020. 

• The aging of the health workforce raises concerns that many health professionals will retire 

about the same time that demand for their services is increasing. Furthermore, the declining 

proportion of the population age 18 to 30 raises concerns regarding the ability to attract a 

sufficient number of new health workers. 

 89
Section 6: Summary and Conclusions 

• The rise in health care expenditures associated with the rapid increase in the elderly 

population will likely place additional pressures on the Medicaid and Medicare programs, as 

well as private insurers, to control health care costs. Such measures would likely decrease the 

demand for and supply of health professionals. 

• The aging population could result in rising average patient acuity, which could in turn require 

higher nurse and physician staffing levels. One countervailing trend is that tomorrow’s 

elderly might have lower disability rates than today’s elderly, controlling for age, because of 

improvements in economic resources, education levels, lifestyle, public health, and medical 

technology. 

Changing Racial and Ethnic Composition of the Population 

The changing racial and ethnic distribution of the population has important demand and supply 

implications for the future health workforce. Key findings and implications from this literature 

review and analysis of the PARM include the following: 

• The literature suggests that Hispanics and non-whites have different patterns of health care 

use compared to non-Hispanic whites. Disparities in access to care account for part of the 

difference in utilization. 

• Demand for health care services by minorities is increasing as minorities grow as a 

percentage of the population. Between 2000 and 2020, the percentage of total patient care 

hours physicians spend with minority patients will rise from approximately 31 percent to 40 

percent. 

• Minorities are underrepresented in the physician and nurse workforce relative to their 

proportion of the total population. As minorities constitute a larger portion of the population 

entering the workforce, their representation in the physician and nurse professions will 

increase. The U.S. will increasingly rely on minority caregivers. 

• Minority physicians have a greater propensity than do non-minority physicians to practice in 

urban communities designated as physician shortage areas. An increase in minority 

 90
Section 6: Summary and Conclusions 

representation in the physician workforce could improve access to care for the population in 

some underserved areas. 

Geographic Location of the Population 

The geographic location of the population determines where the health care needs of the 

population lie. Key demographic trends and their implications for the health workforce include 

the following: 

• Geographic variation in population growth rates and in determinants of health worker 

demand and supply highlight the importance of developing forecasting models that can make 

State-level and sub-State level forecasts. 

• Although an increasing proportion of the U.S. population resides in urban areas, a substantial 

proportion of the population will continue to reside in rural areas. Many of these rural areas 

are currently designated as physician shortage areas. 

• Pockets of urban areas will continue to have a high concentration of minorities. Many of

these areas are currently designated as physician shortage areas. 

• Pockets of urban areas will continue to have a high concentration of minorities. Many of

these areas are currently designated as physician shortage areas. Efforts to increase the 

supply of health professionals in these areas must deal with economic, cultural and language 

considerations. 

Modeling 

One way to better understand the potential implications of demographic and other trends on the 

demand for health professionals is through modeling of specific scenarios. Using forecasting 

models such as the PARM and NDM, one can determine the relationship between demographics 

and demand for health care services and, based on projections of future demographics, 

extrapolate future demand for health professionals. While there is general agreement that 

demographics can be extrapolated with sufficient accuracy for policy purposes, there is often 

disagreement on the future characteristics of other determinants of demand for health 

 91
Section 6: Summary and Conclusions 

professionals. Even modest changes in assumptions regarding the characteristics of the future 

health care operating system can result in large changes in projected demand for health 

professionals such as doctors and nurses. 

The literature review identified the following items to consider when modeling the impact of 

changing demographics on the demand for and supply of health professionals: 

• Current utilization patterns among the elderly might not be sustainable in the future given the 

expected explosion in Medicare and Medicaid spending. Ginzberg (1999) anticipates that 

within the next couple of decades Medicare will provide beneficiaries access to “essential” 

health care services, but not to high-cost hospitals and expensive procedures. Consequently, 

modeling efforts should consider scenarios where the Medicare and Medicaid programs place 

greater restrictions on access to expensive medical procedures and delivery settings, or where 

these programs reduce reimbursement rates to providers. 

• Numerous authors have found declining disability rates among the elderly over time which 

could lead to declining utilization rates for nursing home and other health care services (see, 

for example, Manton et al., 1997; Bonifazi, 1998; Bishop, 1999; and Freedman and Martin, 

1998 & 2000). 

• Freiman (1998) argues that the relationship between race or ethnicity and demand for health 

care services is a complex function of cultural, socioeconomic, and other considerations. 

Consequently, Freiman concludes that separate demand equations should be estimated for 

people in different racial or ethnic groups. 

• Several studies suggest that physicians locate in areas with other physicians in order to 

benefit from the professional synergism that develops when there is already an established 

population of physicians (e.g., Connor, Hillson and Krawelski, 1995; Brasure et al., 1998). 

Efforts to model the supply of physicians in underserved areas might identify “forerunner” 

specialties and analyze patterns of physician location. 

• Efforts to model physician supply might consider adding an urban/suburban/rural dimension 

to the model for the following reasons. One, there could be a systematic difference in the age 

distribution of physicians in these geographic locations. Two, the relationship between 

 92
Section 6: Summary and Conclusions 

supply and its determinants could be different in these geographic locations. Three, there is 

substantial policy interest in forecasting supply in underserved urban and rural areas. 

• Modeling the primary impact of changing demographics on the future health workforce is 

straightforward. What are less obvious are the secondary and tertiary impacts. For example, 

as the population ages and places greater demands on the health care system, how might the 

system react in terms of changing utilization or provider staffing patterns? Additional 

research in this area could improve supply and requirements forecasting models. 

Information on how demographic trends will affect the future demand for health care services, 

and consequently the derived demand for health workers, is important to the public debate. 

Forecasting models provide a tool for analysts to understand the likely impact of changing 

demographics and other factors on the future demand for health professionals, and on the 

adequacy of the supply of professionals to meet this demand. 

 93
Section 6: Summary and Conclusions 

 94
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