Summary by Michele Martinez
Ph.D. Program in Accounting
University of South Florida, Spring 2002
ABC Main Page | Healthcare Cost Main Page |
Overhead Related Main Page
Particularly in the health care industry, identification of cost drivers can be of benefit to all involved. Identification of overhead or indirect cost drivers can be the impetus for more efficient management of the national resources devoted to health care. As stated by the authors, this article focuses on the identification of cost drivers of the general service (nonrevenue earning) departments costs. These overhead costs are significant and can exceed 35% of total hospital costs.
Nonrevenue earning departments provide general services such as cafeteria and housekeeping, the costs of which are allocated to the revenue earning departments.
Complex organizations like hospitals are expected to benefit most from systems that utilized both volume and nonvolume-based cost driver information. However, on average hospitals do not have good quality information nor identified cost drivers.
For purposes of this paper complexity is defined by the authors as the number of services (breadth of complexity) and the intensity of individual services (depth of complexity).
Motivation
More accurate assignment of hospital overhead costs using appropriate cost drivers should make cost data more reliable for pricing land other purposes where each hospital is most concerned with its own cost parameters. In addition, the provision of cost driver information helps hospital managers determine the profitability of insourcing vs. outsourcing certain activities and monitor cost of nonvalue-added activities. However, due to the limitations of data availability, this study does not address these particular issues.
Contribution
Further step toward the identification of variables that drive the costs of U.S. hospital overhead activities.
Management accounting literature is replete with assertions and assumptions about cost behavior, however, little evidence exists, and thus this paper will add insight into how costs behave in hospitals.
Extension of earlier research in health care economics conducted prior to the emergence of the cost driver paradigm.
Complements prior cost driver research by the use of U.S. wide data and the simultaneous determination of hospital overhead costs, the number of services provided (breadth complexity), the intensity of individual services (depth complexity), and the use of proxy variables for volume and complexity cost drivers in a two-stage least squares model.
Develops a model in which hospitals choices of service breadth and depth are simultaneously determined with the level of overhead support costs.
Methodology
Numerous studies across different industries have suggested that volume and complexity are important drivers of overhead costs. Therefore, along the lines of previous studies this paper utilizes a regression model of overhead costs as estimated as a function of volume, capacity, complexity, and other variables for a sample of short-term acute care hospitals throughout the U.S.
Overhead costs are hypothesized to depend upon complexity as well as other factors. Complexity is represented by number of services and specialties provided by the hospital and average direct cost per ancillary specialty (depth of services provided). Consequently, these two variables, breadth and depth, proxy for service complexity.
2SLS models will be estimated on three separate equations: the overhead cost, the breadth of services, and the depth of services equations.
Sample
Broad cross-section of short-term acute care hospitals of varying sizes, service breadth and service depths. The authors assume that the hospitals in the sample chose service breadth and depth simultaneously with the level of overhead support for hospital services, if not simultaneous equation bias (endogeneity bias) is introduced, which effects parameter estimates in the overhead cost equation.
Overhead Costs and Hospital Transactions
Choice of proxy variables to proxy for hospital transactions complexity is restricted by the HCFA cross-sectional data set. Cost drivers that might more precisely proxy for specific logistical transactions, balancing transactions, quality transactions, and change transactions, are not available in the data set.
Logistical transactions are concerned with the reception and movement of hospitals materials.
Balancing transactions deal with the coordination of health care activities within the hospital.
Quality transactions are necessary to ensure patients receive quality treatment in all aspects of their hospital experience.
Change transactions serve to revise health care procedures and individual patient care.
Overall, hospital overhead costs may be caused by volume (number of patient days and number of discharges), capacity (number of available hospital beds), and complexity (number of medical services and depth of ancillary services). This paper investigates the significance of these cost drivers in determining hospital overhead costs, how they are structurally related and how the cost impacts these factors can be estimated in practice (page 287 lists and defines the specific equations used).
Data Collection
Hospital data was obtained from the HCFA, which collects data from Medicare Certified Hospitals. All hospitals are classified as general medical and surgical short-term facilities and included in the sample of 5,352 hospitals. After outlier analysis was conducted and missing data hospitals were eliminated, the final useable data set consisted of 5,306 short-term hospitals.
Equations
The overhead cost equation can be found on page 290. This equation utilized logs, which increases the R2 and controls for heteroscedasticity.
The breadth of services provided by hospitals equation can be found on page 294.
The average amount spent on ancillary services equation can be found on page 296.
Results
Hospitals with a greater number of services (breadth) and a greater number of ancillary specialties also tend to incur a higher level of support costs. A greater breadth of services is also associated with a greater depth of ancillary specialties.
Overhead cost regression results:
An emphasis on shorter patient stays will reduce overhead costs but to a lesser extent than a reduction in the total number of patients.
If hospital reimbursements are based on costs, the cost per patient needs to be considered as well as the cost of an additional day spent in the hospital.
Larger hospitals experience a smaller percentage increase in overhead costs than do smaller hospitals when adding a service.
Complexity regression results:
The greater the number of discharges, the greater the breadth of services provided.
A positive relationship between capacity and complexity, hospitals with a larger number of beds tend to have greater breadth and depth.
Breadth regression results:
Hospitals located in the major metropolitan areas and those that are a sole community hospital have slightly more specialties.
Depth regression results:
The amount hospitals spend on Medicare malpractice insurance per patient day is positively related to average cost per specialty.
Conclusion
Volume and complexity factors positively and significantly affect overhead costs in the hospital industry.
Both patient days and discharges are important volume drivers of overhead costs, but costs per patient associated with admittance etcetera are greater than costs associated with a patient spending an additional day in the hospital.
Depth and breadth of services are important drivers of overhead costs for strategic decision-making.
Region, financial organization and Medicare intensity were also shown to be important drivers of overhead costs.
Volume and other variables help determine the hospitals choice of service mix and intensity.
Caution should be exercised in interpreting this study as supportive of ABC since the form of the model used is nonlinear and inconsistent with the linear ABC model. (See page 310 for more on this point.)
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