Medical Cost Definitions and Terminology

To an economist, a “cost” is not the amount of money required to purchase a particular health care good or service but rather the consumption of societal resources required to produce that good or service, and deliver it to the consumer that could have been used for another purpose.² The term “opportunity cost” is used in the economics literature to indicate this particular meaning of cost. Society consumes resources to satisfy its wants, including those for food, housing, and recreation, as well as health care. However, because resources are ultimately finite, society cannot satisfy all wants and is obliged to choose from among the potential alternative uses of its resources. Economics provides a set of tools and approaches to assist with the decisions regarding what health care to produce, in what quantity, and for whom. The classic illustration of the constrained resources concept is the “guns-versus-butter” example from freshman economics. Resources expended in the production of weapons cannot also be applied to the production of food; therefore, in a world of limited resources, more weapons may mean less food. At the societal level, more health care may ultimately translate into less investment in education, transportation, housing, or other societal priorities.

Opportunity cost is a foundational concept but not a measurement tool. In most actual economic studies, accounting costs are measured as a surrogate for opportunity cost, in large part because the former are easier to measure. Accounting costs are the monetary prices we are more familiar with when we think about the concept of cost. In most businesses or industries, the market price of a product or service is equal to the cost of producing that item plus some amount of profit (typically reflecting a fair return on investment). In the U.S. medical sector, the discrepancy almost universally observed between prices (or charges) and costs (the true accounting cost of providing a given medical service) is largely attributable to “cost shifting,” a set of accounting practices designed to shift costs from a variety of sources (Table 61-1) onto whichever group of payers is most willing and able to absorb them. The net effect of these cost-shifting practices is to distort the relationship between U.S. medical prices or charges and medical resource consumption. U.S. medical charges are, for that reason, never a good surrogate for medical costs, and their use in research and policy evaluations should be avoided.

TABLE 61-1 Major Components of Hospital Charges for Medical Services

The concepts of marginal and incremental costs are commonly used in medical economics. Strictly speaking, marginal cost is defined as the cost of producing one additional (or one less) unit of product, such as a PCI or bypass surgery procedure. Marginal cost excludes costs that do not vary as a direct function of production (termed fixed costs), such as the cost of the interventional laboratory or operating room facilities. Although the notion of one more or one less test or procedure is useful for some types of (usually theoretical) economic analyses, the more practical questions usually focus on examining the costs of shifting groups of patients from one diagnostic or therapeutic strategy to another. For this type of analysis, the term incremental is often substituted for marginal. (Unfortunately, some leading medical economics researchers use “marginal” and “incremental” synonymously, whereas others do not, leading to potential confusion for those outside the field.) Incremental cost analysis is an important component of cost-effectiveness analysis and is central to the economic notion of cost as a measure of alternative uses of scarce resources introduced at the beginning of this chapter. In the next section, we consider how incremental costs are actually measured and some of the problems involved in translating economic theory into practice.

Induced costs (or savings) are the costs of the tests or therapies added or averted as a consequence of some initial management decision, resource use, or both. A few examples will make the concept clear. The institution of an aggressive program of intravenous thrombolytic therapy in patients with acute myocardial infarction (AMI) by a given hospital or physician practice group may be accompanied by a rise in the number of patients with major disabling strokes who need long-term care. That latter cost is induced by the initial therapeutic decision. In the same way, performance of PCI induces the cost of repeat revascularization procedures to treat symptomatic restenosis or stent thrombosis. Use of statin therapy for secondary prevention induces a cost savings over the long term owing to reduced need for cardiac procedures and hospitalizations.

Finally, the term indirect cost is often used by health service researchers to discuss the societal costs associated with loss of employment or productivity caused by morbidity. Because of the potential for confusion with the accounting meaning of indirect cost, the alternative term productivity cost is currently preferred.

Methodologic Issues in Medical Cost Studies

To perform a medical cost study, it is necessary to consider five major issues (Table 61-2): (1) the way cost is to be conceptualized and measured, (2) the type of study to be performed (the structural framework in which the cost analysis will be accomplished), (3) the perspectives of the analysis, (4) the importance of cost variations over time, and (5) the importance of cost variations caused by geographic and market factors.

TABLE 61-2 Major Methodologic Issues in Medical Cost Studies

Cost Measurement

In any clinical cost study, the investigators must decide at an early stage what categories of cost items they need to include in the analysis and at what level of detail they wish to focus (see Table 61-2). In practice, the types of detailed data required for marginal or incremental cost analysis are difficult to obtain unless the hospital or health system involved has a computerized cost-accounting system, and they are impractical (if not impossible) to obtain for all participants in the typical large cardiovascular multi-center trial. Therefore, rather than adding up the individual resources being consumed (which might be termed the bottom-up approach), most U.S. cost studies start with an aggregated measure of costs, such as can be obtained from hospital or physician bills (a top-down analysis). Although the top-down approach is much more practical for many cost studies, especially multi-center studies, it does reduce the ability of the investigator to control the factors that are included as costs in the analysis.

In an older, but still instructive, study examining the practical impact of using top-down versus bottom-up cost estimates, Hlatky and colleagues at Duke compared the magnitude of cost savings available by shifting from a more expensive treatment (i.e., coronary artery bypass grafting [CABG]) to a less expensive one (i.e., percutaneous transluminal coronary angioplasty [PTCA]) in 389 patients with coronary artery disease (CAD). Two bottom-up and three top-down cost estimates were examined (Table 61-3). Using only hospital charges (method 5), the cost savings was estimated at $10,000 per patient shifted to PTCA. However, if no hospital or departmental overhead were to be saved from this change in practice, then the true cost savings would be that estimated by method 1 or 2: 20% to 46% of the amount estimated from charges. Methods 3 and 4, which include varying amounts of overhead, overestimate the short-term cost savings from the CABG to PTCA shift; they are more correctly viewed as providing an estimate of the difference in average cost. Conversely, methods 1 and 2 indicate the marginal or incremental difference. Note that the difference between costs using the Medicare correction factors (method 4) and using charges (method 5) is attributed, at least in part, to the hospital’s shifting of costs from nonpaying patients to the paying segment. This “surtax” would, of course, never be recoverable by changes in patient management. For this reason alone, charges represent a poor choice for evaluating the cost implications of different clinical strategies.

TABLE 61-3 Five Estimates of the Cost Savings Available by Shifting Patients from CABG to PTCA

A true bottom-up cost analysis, sometimes referred to as a microcosting analysis, is a complex, time-consuming process that requires identification of all the inputs into a health care service and the assignment of an appropriate cost to each. This is easiest for a relatively simple service, such as the administration of an antibiotic, the performance of a radiograph, or a laboratory test. A more complex hospital laboratory procedure, such as a PCI, is a considerably greater challenge because of the large variability of inputs from one case to the next. Most complicated of all is an entire episode of care from admission to discharge because this requires detailed cost and resource-use data from virtually every major hospital department.

Of the top-down strategies for estimating costs, the most widely used in the United States involves converting hospital charges (taken from the hospital bill) to costs using the correction factors or ratios of costs to charges (RCCs) included in each hospital’s annual Medicare Cost Report. Medicare RCCs are largely a holdover from the era before prospective payment, when Medicare reimbursed hospitals on the basis of costs incurred. To do so, Medicare developed a method of deciding how to reimburse hospitals for the reasonable and necessary costs of providing care to its beneficiaries (i.e., actual hospital cost), rather than paying the full charged amount. This method involved an elaborate reporting system that required each hospital to file with Centers for Medicare and Medicaid Services (CMS) each year. In this report, which is still required, the hospital details how expenses for direct patient care, overhead, capital equipment, and so forth relate to billed charges. To provide CMS with a means of converting charges to costs for its various ancillary services, each hospital includes in its report a set of ratios, the RCCs. Although not designed for research, the Medicare RCCs represent a moderately standardized means of estimating cost across all the hospitals in the United States that file a Medicare Cost Report. Although no longer used for reimbursement, the Medicare Cost Report still serves as the primary source of government data on hospital costs. In addition, costs calculated with the RCC method are used to recalibrate diagnosis-related group (DRG) weights. Therefore, this method provides a valuable tool for multi-center cost research.

There are three important limitations to the RCC method of cost estimation that should be noted. First, this approach does not separate out overhead and most other fixed costs and therefore provides an estimate of average rather than marginal cost; hence, it may overestimate potential cost savings. Second, Medicare Cost Reports have complex, detailed instructions for how they are to be filled out; as with the complex federal income tax reporting system, this means that hospitals may choose to interpret the instructions differently (just as different people choose to fill out their income tax forms differently). For this reason, the goal of uncovering a hospital’s actual cost of providing care may be accomplished to a varying degree using this method. Finally, RCCs are themselves averages of all the cost–charge relationships within a large hospital revenue (ancillary) center such as the radiology, pharmacy, or laboratory departments. If an individual patient’s resource consumption pattern in a given cost center is not “average,” the Medicare RCCs may not be particularly accurate in converting those charges to costs. For the same reason, conversion of charges to costs for individual items on a detailed hospital bill may not be particularly accurate if the RCC for that item is not close to the average RCC of that cost center. By extension, the practice some have advocated of using one average RCC for an entire hospital may also be less accurate.

DRG reimbursement rates, which are available from CMS in the United States and are also used in some European countries, provide an alternative top-down cost estimation method. Once the patient’s DRG assignment is known, it becomes a simple matter to calculate the “hospital costs.” This system of cost estimation has a number of limitations, however. First, it is not sensitive to variations in resource-use intensity within a DRG. Thus, DRG reimbursement rates are averages in the sense that they represent the “average cost” for a particular DRG among all (elderly) patients in that DRG. Second, if CMS decides not to increase reimbursement to cover the costs of new technology, which has been the case with many new, expensive drug therapies, the DRG reimbursement is insensitive to large differences in resource costs. To take a more complex example, a patient who is admitted for unstable angina and undergoes a diagnostic cardiac catheterization, a PCI, a repeat PCI for abrupt closure, and then CABG will likely be coded out as DRG 106 (CABG with catheterization); from CMS’s point of view, the cost of the two PCIs is the hospital’s problem. For all these reasons, DRG reimbursement is not a particularly good way of estimating costs in an economic analysis unless the analysis is being done from CMS’s perspective.

The most approximate cost-estimation method used in clinical research involves counting only big-ticket items consumed (such as number of PCIs, cardiac catheterizations, or CABGs; days in the intensive care unit; and total hospital length of stay) and assigning unit prices to each item. The resulting linear formula:

is simple and inexpensive to use (hence its appeal in clinical research), but it suffers from some important drawbacks. First, the source of cost weights is often external to the resource data being analyzed and, therefore, of uncertain relevance. Such cost weights are often chosen because they are conveniently available (e.g., published in some unrelated economic study) rather than because they are well suited to the problem at hand. Second, the appropriate set of big-ticket items necessary to estimate costs accurately using this method has never been rigorously defined. For example, days in the intensive care unit (ICU) is an important driver of hospital costs but is difficult to collect accurately in a multi-center study and may be omitted in favor of total length of stay. How much inaccuracy such a decision introduces into the costing and its effects on estimates of incremental costs are important, but often ignored, questions. Third, to preserve the desired simplicity, the method usually treats the big-ticket inputs as though they were homogeneous. For example, an uncomplicated single-vessel PCI would typically be assigned the same price as a complex three-vessel PCI procedure complicated by abrupt closure. The true costs of these two procedures may, in fact, differ substantially.

Assignment of costs to physician services in a cost analysis is usually done in one of two ways. In the past, physician fees (which are charges, analogous to hospital charges) have been used. Because most patients receive care from a variety of practitioners (each billing out of a separate office), collecting actual physician bills is several times more complicated than collecting hospital bills. Increasingly, however, physician fees have become a distorted measure of true resource inputs because physicians have been forced to employ the same types of cost shifting used by hospitals to cover unreimbursed and under-reimbursed services. Furthermore, it can be reasonably argued that physician fees in the “fee-for-service” era have never properly reflected a true market price for physician services. Unlike the situation for hospitals, however, Medicare has never required physicians to disclose their true costs in a cost report.

Because of these distortions, the Medicare Fee Schedule—based on the resource-based relative-value scale (RBRVS) of Hsiao and colleagues—has been adopted as a more appropriate method for assigning costs to physician services.³ The basic concept of the RBRVS is that the price of a service should reflect the (long-term) cost of providing that service. Medicare fees are tied to the American Medical Association Physician’s Current Procedural Terminology (CPT) classification system; therefore, to estimate physician costs from these fees, some map must be created between the CPT codes and the data available in the study database about physician services.

Although the Medicare Fee Schedule is not an ideal measure of the consumption of physician work in health care, it has the strong advantage of being more objective and consistent than charges or fees. Furthermore, the Medicare RBRVS payment schedule is now being used by many private insurers and managed care organizations, although with variations. Therefore, it represents the best available national measure of the economic value of physician work.

Cost Study Structures

Medical cost studies generally fall into one of three categories: (1) randomized controlled trials (RCTs); (2) observational studies; and (3) cost-effectiveness models. Cost-effectiveness models are discussed in the next section. A cost study in a randomized controlled trial is usually ancillary to the primary objective of the trial. Typically, costs or resource consumption patterns are a secondary endpoint in a trial that has either a composite clinical or (preferably) a mortality primary endpoint. Some have argued that because RCTs are rarely performed with cost as a primary endpoint, the trials are usually not optimized to answer the economic questions of greatest interest, except insofar as these questions parallel the primary clinical ones. In addition, requirements of the clinical portion of the study may distort the economic substudy. For example, follow-up protocol angiography to define restenosis leads to repeat revascularization procedures that would not otherwise have been done. Even protocol-required clinic visits may lead to medical tests and therapies that would otherwise not have occurred.

Observational cost studies include both nonrandomized treatment comparisons and descriptive series without an intrinsic comparison group. Descriptive cost studies are useful in areas in which few empirical cost data have been published. Such data can be used to make sample size projections for RCTs or to inform cost-effectiveness and other health policy studies (in conjunction with appropriate sensitivity analyses). Little has been done, to date, with observational treatment comparisons involving cost data. As with observational comparisons of medical outcomes, statistical adjustment techniques to “level the playing field” are critical. However, because medical costs are subject to variations over time and over geographic location and practice settings, it is still uncertain what boundaries exist for defining when a nonrandomized cost comparison can provide useful, relatively unbiased information.

Cost-Effectiveness Analysis

In clinical medicine, the term cost-effective is frequently used synonymously with worthwhile to indicate an intuitive, unspecified threshold between productive and wasteful medical expenditures. However, for economists the term cost-effective has a specific technical (and not particularly intuitive) meaning. Cost-effectiveness analysis involves the explicit comparison of one option or program with at least one alternative investment of dollars, and it never indicates whether a given expenditure is worthwhile in an absolute sense but, rather, how it stands relative to other potential expenditures. Therefore, it is incorrect to speak of any medical practice in isolation as cost-effective. The primary objective of cost-effectiveness analysis is to evaluate different health care expenditure options in common terms so that policy and other decision makers can choose the most efficient method of producing extra health benefits from among the alternative ways that health care dollars can be distributed.

The general term cost-effectiveness analysis actually refers to a family of methods for economic analysis (Table 61-4). For all methods, the final measure is expressed in ratio form, with incremental costs in the numerator and incremental health care benefits or outcomes in the denominator. The distinction among the methods derives primarily from how health benefits are measured. In cost-effectiveness analysis, the measure of incremental health effects chosen is typically the difference in life expectancy between the alternative strategies being evaluated (see Table 61-4). This is the most common type of economic health care analysis performed.

TABLE 61-4 Cost Effectiveness, Cost Utility, and Cost Benefit: Sample Calculations*

In cost-utility analysis, remaining survival is adjusted for less than full quality (quality-adjusted life-years [QALYs]). With both cost-effectiveness and cost-utility analysis, informal benchmarks are used to define results considered “economically attractive” [typically less than $50,000 per life-year or QALY] and “economically unattractive” [typically $100,000 per life-year or QALY or more]. The middle zone between these two benchmarks is considered of uncertain economic attractiveness.

Cost-benefit analysis is used much less often in medicine, probably because it requires measuring all health-related benefits of a program in monetary terms (see Table 61-4). The results of a cost–benefit analysis can either be expressed as a ratio of incremental monetary benefits to monetary costs or as a difference between the two. If the benefit: cost ratio exceeds 1 or the difference of benefits minus costs is positive, the assumed interpretation is that the treatment or program is worth doing, as it provides a net gain to the decision makers. Cost-benefit analysis has the useful feature of permitting comparison of medical care expenditures with societal expenditures on education, defense, transportation, and so forth, whereas cost-effectiveness analysis is useful only in comparison of expenditures that produce the same type of outcome (e.g., QALYs). However, the difficulty of valuing health benefits in dollars in a valid and acceptable way has made this the least used method of efficiency analysis in medical economics.

Table 61-4 compares two hypothetical treatment strategies (A and B) for a particular disease and summarizes the calculations involved in the different analyses. Treatment A costs twice as much as treatment B but also improves average life expectancy by 1 year. Thus, the cost-effectiveness ratio for A relative to B is $10,000 per life-year saved. Whether switching from B to A is “worthwhile” depends on the alternative health care expenditures (aside from A) available for $10,000 or less. This is the most common sort of problem faced in cost-effectiveness analysis: whether to fund a new program that provides more health benefits than the standard therapy but at a substantially increased cost. (It is theoretically possible to go in the other direction—to give up health benefits to save substantial health care dollars—but this is rarely politically viable.)

QALYs allow us to factor in the value (to the decision maker, which may be the patient but could be someone else) of the extended survival offered by a new program or alternative therapy, as well as its quantity. In the example in Table 61-4, strategy A improves life expectancy relative to B, but the average quality of life for survivors is lower. This could come about in several ways. For example, with strategy B the sickest patients could die, leaving a relatively healthier group of survivors. In contrast, strategy A saves these sick patients from dying but cannot restore them to the same level of health as in the case of other patients with lower disease severity. These sicker surviving patients lower the average quality of life for the group. Alternatively, there could be something about strategy A that negatively affects quality of life, such as the need for chronic medication that is associated with significant side effects and that is not required with strategy B. In this example, moving from cost-effectiveness to cost-utility analysis more than doubles the cost of an additional unit of (quality-adjusted) survival with strategy A.

The underlying tenet of all these forms of economic analysis is that the analyst desires to determine the most efficient means of maximizing the net health benefits for a particular group or population under the constraint of limited resources (i.e., where it is not possible to provide every beneficial service to every potential recipient). Note that such economic analyses are neutral to the specific patients and diseases under study; the health benefits being maximized are abstractly conceptualized as belonging to a large group or population. In actual practice, however, political and other forces may play a large role in deciding where societal and other health care dollars are to be invested.

The ultimate problem in health care is that individual patients (and their physicians) wish to obtain all the health benefits that are available from modern medical technology. However, for the collection of all patients (i.e., society), the resources are not adequate to meet this need for every individual, which forces difficult and potentially divisive choices. The more we do for selected segments of the population (e.g., chronic renal failure patients receiving dialysis, patients with acquired immune deficiency syndrome [AIDS], patients with AMI), the less we are able to do for the remainder. In the next section, we examine the costs of various interventional coronary disease therapies. We then return to the issue of cost-effectiveness at the end of this chapter and examine the ways in which this tool can be applied (and misapplied) in the analysis of these difficult choices.

General Issues

An economic analysis comparing a new drug, device, or strategy with “conventional” or “usual” care starts with an exploration of the ways in which the new approach will alter costs for the patients involved. At the most basic level, this involves understanding the resource consumption patterns and associated incremental costs of the new approach or technology. For a new interventional procedure, this includes the costs of the equipment and supplies used and the personnel changes required. A careful economic analysis also must determine what diagnostic or therapeutic procedures and what complications are added or averted, along with the cost effects of these changes in practice and outcome. Understanding these relationships is often difficult in practice, and one of the major advantages of empirical data collection over armchair models for cost studies is the frequency with which actual medical practices and outcomes diverge from the expected ideal.

In general, three major patterns of cost outcomes are possible when comparing alternative medical strategies or technologies (Fig. 61-1).

Figure 61-1 Schematic representation of patterns of outcome and cost differences that may result when a new therapy or strategy is compared with an existing standard. Effectiveness is always considered first because if the new therapy is less effective, its cost is rarely of concern. If effectiveness is better, or at least equivalent, then costs are examined. If the outcomes are better but the net costs are higher, cost-effectiveness analysis is then performed. If the costs are equivalent or lower, the therapy is said to be dominant (i.e., it becomes the preferred option). If outcomes are equivalent, cost analysis is used to select the more efficient, less costly option. This form of cost analysis is sometimes referred to as cost minimization or cost-efficiency analysis.

Coronary Revascularization

In 2007, approximately 1.06 million diagnostic cardiac catheterizations, 1.18 million PCIs, and 408,000 CABGs were performed in the United States.¹ Because a hospitalization for PCI averages from $8000 to $15,000 and a hospitalization for CABG averages $30,000 or more, coronary revascularization costs in aggregate probably exceed $25 billion per year (Table 61-5). Although the procedures may be more efficient now than in the past owing to shortened hospital stays and lower complication rates, use in patients with more complex disease and advances in technology, especially for PCI, have tended to push costs back up. In this portion of the chapter, we review the available data addressing two key questions: First, what information do we have about how much these procedures cost? Second, what is the value of these procedures, where value

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