Case Study - Health Care Services

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

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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.

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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.