and race-specific utilization patterns. However, these projections forecast demand from all patients, not just older adults, and in most cases they assume that current utilization patterns will continue in the future, though some efforts also include projections under alternative scenarios in which practice or utilization patterns shift. For example, projections include the following:
The need for critical care services will rise, increasing the need for intensivists1 from 1,880 in 2000 to 2,600 by 2020 if current patterns of care continue. If utilization of critical-care physicians rises by one-third (which is, some suggest, a more appropriate level of use), approximately 4,300 intensivists would be required by 2020 (HRSA, 2006a).
Visits to oncologists for cancer are projected to increase from about 40 million to almost 60 million between 2005 and 2020 if current patterns of care continue. A 2 percent increase in the percentage of patients who see an oncologist and a 2 percent increase in the average visit-rates in the first 12 months post-diagnosis would result in 70 million visits in 2020 (AAMC, 2007).
If trends in emergency department visits among patients between the ages of 65 and 74 continue at current rates, the number of visits by these individuals would almost double from 6.4 million to 11.7 million by 2013 (Roberts et al., 2008).
Perhaps the most sophisticated models that project demand for health services from health professionals are those maintained by the National Center for Health Workforce Analysis at the Health Resources and Services Administration (HRSA). The Physician Aggregate Requirements Model (PARM) and Nursing Demand Model (NDM) project demand for services and providers based on current and forecasted patterns of health care use, staffing patterns, and insurance coverage. They consider provider-to-population ratios for population segments defined by age, sex, metropolitan/ non-metropolitan location, and type of insurance. An assumption for the baseline scenario is that these ratios are fixed (i.e., there is a constant insurance probability for each population group defined by age and sex). These ratios are then applied to population projections to estimate future demand (HRSA, 2003, 2006b).
Under a baseline scenario in which there is no change in per capita health care utilization patterns, provider productivity, or provider staffing patterns, changes in population characteristics would drive a 30 percent increase in hospital inpatient days, a 20 percent increase in outpatient visits,