Health Resources and Services Administration
Bureau of Health Professions
National Center for Health Workforce Analysis
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
bhpr.hrsa.gov/healthworkforce/
| TABLE OF CONTENTS |
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| 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. |
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| Population Aging | ||
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Increasing Racial and Ethnic Diversity |
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Geographic Location of the Population |
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Forecasting the Impact of Changing Demographics and Other Factors on Physician Requirements |
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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).
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| Exhibit ES.1 Forecasted Physician Requirements | ||||||||||||||||||||
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| 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. | ||
Forecasting the Impact of Changing Demographics and Other Factors on Nurse Requirements |
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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. |
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| Exhibit ES.2 Forecasted FTE Nurse Requirements | ||||||||||||||||||||||
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| 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 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). |
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| 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]
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.
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. |
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| 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:
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| 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.
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 large. [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. 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. |
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Increased longevity and the
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. 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. |
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| 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] 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. 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. |
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| Exhibit 2.1. Age Distribution of U.S. Population | ||||||||||||||||||||||||||||||||||||||||||||||
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| Exhibit 2.1. Age Distribution of U.S. Population (Text Only) | ||||||||||||||||||||||||||||||||||||||||||||||
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| Source: U. S. Census Bureau middle series population projections (Day, 1996). | ||||||||||||||||||||||||||||||||||||||||||||||
| Exhibit 2.2. Projections of U.S. Elderly Population | ||||||||||||||||||||||||||||||||||||||||||||||
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| 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 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. |
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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. |
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| Exhibit 2.3. Inpatient Days in General, Short-term Hospitals (per 1,000 population) | ||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 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). | ||||||||||||||||||||||||||||||||||||||||||||||||||||
| Exhibit 2.4. Outpatient Visits in General, Short-term Hospitals (per 1,000 population) | ||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 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) | ||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 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). |
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| Exhibit 2.6. Inpatient Days in Non-General and Long-term Hospitals (per 1,000 population) | ||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 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) | ||||||||||||||||||||||||||||||||||||||||||
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| 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) | |||||||||||||||||||||||
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| Source: Analysis of the 1997 National Nursing Home Survey (NNHS). See Dall and Hogan (2002). | |||||||||||||||||||||||
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| 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.
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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.
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 non-elderly. 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 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 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. 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. |
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| Exhibit 2.9. Minutes of Physician Time
Spent with Patients in Doctors' Offices (by Patient Characteristics and Insurance Status) |
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| 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 at the 0.05 level of significance. a The large majority of patients seen by pediatricians are age 17 and younger, so the sample size of adults seen by pediatricians is insufficient to obtain reliable estimates by age group. b This physician specialty saw no patients with this characteristic. |
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| Exhibit 2.10. Minutes of Physician Time
Spent with Patients in Hospital Outpatient Clinics (by Patient Characteristics and Insurance Status) |
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| 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 at the 0.05 level of significance. a The specialty imputation method identified the physician of patients age 0-17 with general primary care diagnoses or IM subspecialty diagnoses as pediatricians, and identified the physicians of adults with these diagnoses as general/family practitioners or internists in either general internal medicine or an IM subspecialty. b The imputation method identified no patients with this characteristic for this specialty. |
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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.
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. 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. |
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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. |
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.
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. |
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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 near-elderly (i.e., persons ages 61-64) will drop by 4.5 percent by 2005 because of trends relating to 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. |
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| 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, long-term care. Consider the following factors that could reduce the future supply of unpaid health care providers.
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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. |
Exhibit 2.11. Estimated Percentage of Physician's
Time Spent Providing Care to Patients, |
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| Source: 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Exhibit 2.12: Distribution of Total Patient
Care Hours, by Patient Age: Total Active Physicians in Patient Care |
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| Exhibit 2.12: Distribution of Total Patient
Care Hours, by Patient Age: (Text Only) Total Active Physicians in Patient Care |
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| Exhibit 2.13: Distribution of Total Patient
Care Hours, by Patient Age: General Primary Care Physicians |
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| Exhibit 2.13: Distribution of Total Patient
Care Hours, by Patient Age: General Primary Care Physicians (Text Only) |
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| Exhibit 2.14: Distribution of Total Patient
Care Hours, by Patient Age: Primary Care Subspecialty Physicians |
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| Exhibit 2.14: Distribution of Total Patient
Care Hours, by Patient Age: Primary Care Subspecialty Physicians(Text Only) |
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