V.
Limitations of the Models and Areas for
Future Research
The
NSM and NDM are built on a theoretical
foundation supported by empirical research.
Still, efforts to update and enhance both
models faced numerous challenges—many
due to data limitations. Below, we describe
limitations of the two models and suggest
areas for research that could address
these limitations. Such research could
improve the theoretical underpinnings
of the models and improve the precision
of key parameters in the model.
The
NSM and the NDM are independent models.
The NDM makes projections without considering
the potential supply of nurses and vice
versa. The future nurse workforce, in
reality, will be influenced by the combination
of supply and demand. A rising demand
for nursing services at a time when supply
is flat or falling
will place upward pressures on nurse wages.
This rise in wages would increase the
number of new graduates, increase employment
participation rates, and delay retirement
for some nurses—all actions that will
increase supply. Local shortages, on the
other hand, could increase nurse wages
locally contributing to local increases
in the number of nurse graduates and an
increase in the number of nurses migrating
to that locality. Rising nurse wages will
also place downward pressures on demand
for nurses.
Both
models use the SSRN to estimate the number
of RNs employed in the base year. The
NSM uses the 2000 SSRN to estimate supply
of RNs by age, education level, and State.
The NDM uses the 1996 SSRN to estimate
number of FTE RNs by setting and State.
Because the precision of estimates is
proportional to sample size, the RN supply
and employment estimates for the base
year become less precise the smaller the
unit of aggregation. Consequently, the
base year starting values and projections
for future years are less precise the
smaller the unit of analysis. For example,
estimates of demand for RNs in a particular
setting within a State likely will be
less precise than the State-level estimates,
which in turn likely will be less precise
than the national-level estimates.
One
criticism of many attempts to model nurse
demand is the limited consideration of
important determinants of nursing demand
(e.g., see Dumpe, Hermon, and Young [1998]
and Prescott [2000]). Projections models
such as the NDM and NSM are scaled-down
versions of complex systems. Data and
resource limitations prevented building
models that include a wider array of determinants
to better model the complexities of RN
supply and demand. Consequently, many
determinants of RN supply and demand are
excluded from these models. Still, these
models attempt to account for the major
trends affecting RN supply and demand
and project future supply and demand under
a set of assumptions that constitutes
an educated guess at whether current trends
will continue.
Regarding
the NDM, we use State-level data to estimate
the relationship between demand for RNs
and its determinants. One consequence
of using State-level data is that relatively
few degrees of freedom exist for estimating
the regression equations. Future efforts
might investigate the use of alternative
approaches or lower levels of data aggregation
to estimate the relationship between healthcare
use and its determinants and between staffing
intensity and its determinants.
Additional
research could provide estimates of key
parameters that improve the accuracy of
the models and make the models more flexible
policy tools. The NSM, for example, was
built with the capacity to model the RN
supply implications of changes in nurse
wages, working conditions, tuition costs,
and number of nursing school faculty.
The empirical research has yet to be conducted
to estimate the parameters necessary to
use these features.
The
NSM models only the supply of RNs and,
unlike the NDM, fails to consider LPNs
and nurse aides. The adequacy of the LPN
supply holds implications for both the
supply of and demand for RNs. On the demand
side, employers have some ability to substitute
between RNs and LPNs—taking into consideration
legal and practical constraints. On the
supply side, some LPNs seek further training
to become RNs. Using the 2000 SSRN, we
estimate that approximately 9.5 percent
of the RN workforce, or 257,784 RNs, were
employed as LPNs before starting their
basic nurse education. The RN and LPN
workforces are competing for the same
candidates, many of whom could become
either RNs or LPNs. Consequently, policies
designed to recruit more RNs could have
the unintended consequence of reducing
the LPN supply.
Parts
of both models are static. In the NSM,
for example, the probability of cross-State
migration is based on historical patterns
that fail to consider the current shortage
of RNs in each State. The NDM has limited
ability to model substitution between
types of nurses and between nurses and
other healthcare workers. The NDM does
model substitution between RNs and LPNs
if their relative wages change, but future
research might look at other ways to incorporate
substitution effects. Similarly, the NDM
has limited ability to capture the interaction
of healthcare settings. For example, some
settings might be viable substitutes (e.g.,
home health versus nursing facilities),
while other settings might be complementary
(e.g., increased use of outpatient services
leading to increased use of home health
services).
In
summary, the NSM and NDM constitute powerful
tools for projecting RN supply and demand
under alternative sets of assumptions.
The models help quantify the growing shortage
of RNs as an aging population increases
demand for nursing services at the same
time an aging RN workforce and difficulties
attracting new entrants to the nursing
profession portend relatively little growth
in the national RN supply. |