Estimating the Costs of Nursing Care
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 nursing 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 of providers 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 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 historical 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 for the entire facility would be inequitable to providers and could create access
barriers for some Medicaid patients.
As an alternative to traditional cost estimation methods, one might consider using the
engineering technique to estimate nursing costs. For example, the labor cost of each type of
service could be estimated as the product of an estimate of the time required to perform each
service times the estimated wage rate per unit of time. Multiplying this figure by an estimate of
the frequency of service provides an estimate of the aggregate 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 on costs of service "packaging" is allowed for.
Nursing 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
Chapter 9
Presented by Suong Jian & Liu Yan, MGMT Panel , Guangdong University of Finance.
- 256 -
example, if 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.
Table 9.2 here
To illustrate a regression-based approach to nursing cost estimation, consider a
hypothetical analysis of variable nursing costs conducted by the Southeast Association of
Hospital Administrators (SAHA). Using confidential data provided by 40 regional hospitals,
SAHA studied the relation between nursing costs per patient day and four typical categories of
nursing services. These annual data appear in Table 9.2 The four categories of nursing services
studied include shots, intravenous (IV) therapy, pulse taking and monitoring, and wound
dressing. Each service is measured in terms of frequency per patient day. An output of 1.50 in
the shots service category means that, on average, patients received one and one-half shots per
day. Similarly, an output of 0.75 in the IV service category means that IV services were
provided daily to 75% of a given hospital's patients, and so on. In addition to four categories of
nursing services, the not-for-profit or for-profit status of each hospital is also indicated. Using a
"dummy" (or binary) variable approach, the profit status variable equals 1 for the 8 for-profit
hospitals included in the study and zero for the remaining 32 not-for-profit hospitals.
Table 9.3 here
Cost estimation results for nursing costs per patient day derived using a regressionbased
approach are shown in Table 9.3.
A. Interpret the coefficient of determination (R2) estimated for the nursing cost function.
B. Describe the economic and statistical significance of each estimated coefficient in
the nursing cost function.
C. Average nursing costs for the eight for-profit hospitals in the sample are only
$120.94 per patient day, or $3.28 per patient day less than the $124.22 average cost
experienced by the 32 not-for-profit hospitals. How can this fact be reconciled with
the estimated coefficient of -2.105 for the for-profit status variable?
Cost Analysis and Estimation
Presented by Suong Jian & Liu Yan, MGMT Panel , Guangdong University of Finance.
- 257 -
D. Would such an approach for nursing cost estimation have practical relevance for
publicly funded nursing cost reimbursement systems?
CASEa
ใครเก่งอังกฤษช่วยแปลหน่อยจร้า
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 nursing 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 of providers 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 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 historical 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 for the entire facility would be inequitable to providers and could create access
barriers for some Medicaid patients.
As an alternative to traditional cost estimation methods, one might consider using the
engineering technique to estimate nursing costs. For example, the labor cost of each type of
service could be estimated as the product of an estimate of the time required to perform each
service times the estimated wage rate per unit of time. Multiplying this figure by an estimate of
the frequency of service provides an estimate of the aggregate 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 on costs of service "packaging" is allowed for.
Nursing 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
Chapter 9
Presented by Suong Jian & Liu Yan, MGMT Panel , Guangdong University of Finance.
- 256 -
example, if 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.
Table 9.2 here
To illustrate a regression-based approach to nursing cost estimation, consider a
hypothetical analysis of variable nursing costs conducted by the Southeast Association of
Hospital Administrators (SAHA). Using confidential data provided by 40 regional hospitals,
SAHA studied the relation between nursing costs per patient day and four typical categories of
nursing services. These annual data appear in Table 9.2 The four categories of nursing services
studied include shots, intravenous (IV) therapy, pulse taking and monitoring, and wound
dressing. Each service is measured in terms of frequency per patient day. An output of 1.50 in
the shots service category means that, on average, patients received one and one-half shots per
day. Similarly, an output of 0.75 in the IV service category means that IV services were
provided daily to 75% of a given hospital's patients, and so on. In addition to four categories of
nursing services, the not-for-profit or for-profit status of each hospital is also indicated. Using a
"dummy" (or binary) variable approach, the profit status variable equals 1 for the 8 for-profit
hospitals included in the study and zero for the remaining 32 not-for-profit hospitals.
Table 9.3 here
Cost estimation results for nursing costs per patient day derived using a regressionbased
approach are shown in Table 9.3.
A. Interpret the coefficient of determination (R2) estimated for the nursing cost function.
B. Describe the economic and statistical significance of each estimated coefficient in
the nursing cost function.
C. Average nursing costs for the eight for-profit hospitals in the sample are only
$120.94 per patient day, or $3.28 per patient day less than the $124.22 average cost
experienced by the 32 not-for-profit hospitals. How can this fact be reconciled with
the estimated coefficient of -2.105 for the for-profit status variable?
Cost Analysis and Estimation
Presented by Suong Jian & Liu Yan, MGMT Panel , Guangdong University of Finance.
- 257 -
D. Would such an approach for nursing cost estimation have practical relevance for
publicly funded nursing cost reimbursement systems?
CASEa