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CASE STUDY – QUESTIONS
1. Build a regression model. You must decide which one of the
variables in the case is the dependent variable (justify your choice). You
must decide whether to use a simple or a multiple regression (justify
your choice). This includes justifying whether to include or not include
each of the other variables as independent variables in your regression
model. You must also decide whether to use a linear or non-linear
model (justify your choice). Do some research!
2. Estimate your regression model (excel is acceptable). In addition you
must describe and interpret each of the coefficient(s) of the independent
variable(s) and whether they have the expected signs. You must also
explain how well the model explains the variability in the dependent
variable.
3. Conduct the hypothesis tests shown in the class notes. No short cuts.
4. Details of the Case Study is Attached in the pdf file.
Cost estimation and cost containment are an
important concern for a wide range of for-profit
and not-for-profit organizations offering health-
care services. For such organizations, the accurate
measurement of costs per patient day (a measure
of output) is necessary for effective management.
Similarly, such cost estimates are of significant
interest to public officials at the federal, state,
and local government levels. For example, many
state Medicaid reimbursement programs base
their payment rates on historical accounting
measures of average costs per unit of service.
However, these historical average costs may or
may not be relevant for hospital management
decisions. During periods of substantial excess
capacity, the overhead component of average
costs may become irrelevant. When the facilities
are fully used and facility expansion becomes
necessary to increase services, then all costs,
including overhead, are relevant. As a result,
historical average costs provide a useful basis
for planning purposes only if appropriate
continued I
J
assumptions can be made about the relative
length of periods of peak versus off-peak facility
usage. From a public-policy perspective, a
further potential problem arises when hospital
expense reimbursement programs are based on
average costs per day, because the care needs
and nursing costs of various patient groups can
vary widely. For example, if the care received
by the average publicly supported Medicaid
patient actually costs more than that received by
non-Medicaid patients, Medicaid reimbursement
based on average costs would be inequitable to
providers and could create access barriers for
Medicaid patients.
As an alternative to accounting cost estimation
methods, one might consider using engineering
techniques to estimate nursing costs. For example,
the labor cost of each type of service could be
estimated as the product of an approximation of
the time required to perform each service and the
esÿnated wage rate per unit of time. Multiplying
this figure by an estimate of the frequency of
service gives an engineering estimate of the cost
of the service. A possible limitation to the accuracy
of this engineering cost estimation method is
that treatment of a variety of illnesses often
requires a combination of nursing services. To
the extent that multiple services can be provided
simultaneously, the engineering technique will
tend to overstate actual costs unless the effect of
service “packaging” is allowed for.
Cost estimation is also possible by means of
a carefully designed regression-based approach
using variable cost and service data collected
at the ward, unit, or facility level. Weekly labor
costs for registered nurses (RNs), licensed
practical nurses (LPNs), and nursing aides
might be related to a variety of patient services
performed during a given measurement period.
With sufficient variability in cost and service
levels over time, useful estimates of variable
labor costs become possible for each type of
service and for each patient category (Medicaid,
non-Medicaid, etc.). An important advantage of
a regression-based approach is that it explicitly
allows for the effect of service packaging on
variable costs. For example, ff shots and wound-
dressing services are typically provided together,
this will be reflected in the regression-based
estimates of variable costs per unit.
Long-run costs per nursing facility can be
estimated using either cross-section or time-series
methods. By relating total facility costs to the
service levels provided by a number of hospitals,
nursing homes, or out-patient care facilities
during a specific period, useful cross-section
estimates of total service costs are possible. If
case mixes were to vary dramatically according
to type of facility, then the type of facility
would have to be explicitly accounted for in the
regression model analyzed. Similarly, if patient
mix or service-provider efficiency is expected to
depend, at least in part, on the for-profit or not-
for-profit organization status of the care facility,
the regression model must also recognize this
factor. These factors plus price-level adjustments
for inflation would be accounted for in a time-
series approach to nursing cost estimation.
continued
Table 8.2 Nursing Costs per Patient Day, Nursing Services, and Profit Status for 40 Hospitals in Southeastern States
Nursing Care Costs Wound Profit Status (1 = For-profit,
Hospital Per Patient Day Shots IV Therapy Pulse Taking Dressing 0 = Not-for-profit)
1 300.92 0.29 0.51 3.49 0.27 0
2 283,65 0.15 0.59 3.32 0.62 0
3 329.65 0.26 0.85 3.05 0.29 1
4 343.71 0.23 0.67 2.26 0.57 0
5 389.03 0.47 0.79 2.43 0.68 0
6 299.01 0.41 0.86 2.48 0.66 1
7 437.97 0.42 0.90 3.81 0.49 0
8 284.74 0.25 0.63 2,96 0.34 0
9 404.65 0.50 0.93 2.27 0.60 0
10 293.70 0.27 0.67 2.51 0.61 0
11 264.58 0.38 0.62 2.93 0.39 0
12 299.71 0.14 0.76 2,17 0.37 0
13 323.81 0.16 0.91 2.07 0.47 0
14 434.45 0.27 0.98 3.17 0.68 0
15 374.17 0.48 0.87 3.45 0.33 0
16 304.61 0.43 0.61 2.96 0.30 0
17 354.46 0.09 0.82 3.17 0.54 0
18 340.21 0.12 0.71 3.96 0.41 0
19 348.13 0,04 0.76 3.39 0.70 0
20 353.52 0.41 0.84 2.61 0.41 0
21 309.01 0.22 0.83 2.62 0.47 I
22 406.12 0.32 0.56 4.00 0.59 0
23 309.04 0.40 0.84 3.46 0.50 I
24 406.94 0.29 0.72 3.85 0.65 0
25 317.12 0.14 0.91 2.74 0.29 0
26 369.23 0.15 0.80 3.73 0.72 I
27 300.31 0.31 0.75 3.12 0.26 0
28 402.08 0.37 0.72 2.76 0.73 0
29 434.40 0.40 0.96 2.72 0.72 0
30 272.48 0.35 0.62 2.31 0.37 0
31 316.30 0.08 0.68 3.80 0.33 0
32 351.71 0.34 0.63 3.58 0.53 0
33 359.90 0.46 0.89 3.44 0.46 0
34 331.36 0.39 0.54 3.40 0.56 0
35 335.54 O. 17 0.84 2.58 0.43 I
36 402.26 0.48 0.91 3.75 0.36 0
37 373.28 0.25 0.81 3.49 0.53 0
38 440.67 0.16 0.86 3.34 0,68 0
39 262.83 0.43 0.51 3.05 0.43 I
40 333.86 0.45 0.82 3.59 0.33 I
Average 344.98 0.30 0.76 3.09 0.49 0.20
1. Build a regression model. You must decide which one of the
variables in the case is the dependent variable (justify your choice). You
must decide whether to use a simple or a multiple regression (justify
your choice). This includes justifying whether to include or not include
each of the other variables as independent variables in your regression
model. You must also decide whether to use a linear or non-linear
model (justify your choice). Do some research!
2. Estimate your regression model (excel is acceptable). In addition you
must describe and interpret each of the coefficient(s) of the independent
variable(s) and whether they have the expected signs. You must also
explain how well the model explains the variability in the dependent
variable.
3. Conduct the hypothesis tests shown in the class notes. No short cuts.
