Changing Demographics and the Implications for Physicians, Nurses, and Other Health Workers

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Aging of the Population

1. Population Forecasts
2. Implications of an Aging Population for the Demand for Health Workers
1. Increasing Demand for Health Care Services
2. Increasing Demand for Health Workers
3. Implications of an Aging Population for the Supply of Health Workers
1. Physician Supply
2. Nurse Supply
4. Implications of an Aging Population for the Economics of the Health Care System

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-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. Also, the elderly population will grow at a faster rate than the working-age population. The rise in health care expenditures associated with the rapid increase in the elderly population will likely place pressures on the Medicaid and Medicare programs to control health care costs. Such measures would likely decrease the demand for and supply of health professionals.

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.

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] 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.

Exhibit 2.1. Age Distribution of U.S. Population

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 2.1. Age Distribution of U.S. Population (Text Only)

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

Exhibit 2.2. Projections of U.S. Elderly Population

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.

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)

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)

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)

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).

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

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)

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)

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

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] 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.

Exhibit 2.9. Minutes of Physician Time Spent with Patients in Doctors' Offices (by Patient Characteristics and Insurance Status)

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.

Exhibit 2.10. Minutes of Physician Time Spent with Patients in Hospital Outpatient Clinics (by Patient Characteristics and Insurance Status)

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.

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. 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.

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.
• 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 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.

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.

• 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-year-old 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.
• 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 long-term 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.

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).

Exhibit 2.11. Estimated Percentage of Physician's Time Spent Providing Care to Patients, by Age of Patient

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

Exhibit 2.12: Distribution of Total Patient Care Hours, by Patient Age: (Text Only) Total Active Physicians in Patient Care

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

Exhibit 2.13: Distribution of Total Patient Care Hours, by Patient Age: General Primary Care Physicians (Text Only)

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

Exhibit 2.14: Distribution of Total Patient Care Hours, by Patient Age: Primary Care Subspecialty Physicians (Text Only)

Exhibit 2.15: Distribution of Total Patient Care Hours, by Patient Age: Physicians in Surgical Specialties

Exhibit 2.15: Distribution of Total Patient Care Hours, by Patient Age: Physicians in Surgical Specialties (Text Only)

Exhibit 2.16: Distribution of Total Patient Care Hours, by Patient Age: Physicians in Other Patient Care Specialties

Exhibit 2.16: Distribution of Total Patient Care Hours, by Patient Age: Physicians in Other Patient Care Specialties (Text Only)