Nancy L. Reaven
President, Strategic Health Resources

Technology companies have been fighting against the inevitable—to prove that their technologies are cost-effective. Why? Because, they argue, cost-effectiveness studies add to the development costs of new products, both in time-to-market delays and financial investment in cost studies.

In reality, coverage and reimbursement decisions about new technology are often delayed when cost data is not available. This is because the decision-making bodies—ranging from the American Medical Association (AMA) Editorial Panel recommending procedure codes to the local Medicare Medical Director to a health insurer’s national Medical Director—are reluctant to issue coverage and reimbursement decisions without some understanding of the cost outcomes of new technology. A quick look at some prevailing industry trends illustrates their reasons:

Operating margins and profits of the nation’s payers and provider systems are more compressed than at any time in recent memory;

Technology costs (including drugs) are the fastest growing component of health care costs, overtaking the cost of inpatient care in some health plans;

New technology is being developed and approved faster than ever before due to FDA ‘fast-tracking’ and recent regulatory changes; and

Consumer demand for new drugs and technologies is higher and more focused than ever before due to direct-to-consumer advertising and access to information from the Internet and other sources.

In short, technologies cost more and are in greater demand within an industry that is still licking its wounds from a prolonged market share battle that resulted in lower profits for all. Is it any wonder that purchasers are demanding more information about the ‘value’ of new technology?

Technology ‘Value’

But what exactly does ‘value’ mean in today’s world? For many health plans and provider systems ‘value’ refers to the relationship between the clinical benefits of a new technology and the extent to which it adds costs (or saves costs) to their bottom line. Because a health plan’s ability to get additional premium from employers or state and federal governments is very limited, the issue of technology costs is getting more intense. Purchasers are demanding evidence of cost-effectiveness before making important decisions about coverage, reimbursement and technology use.

These decisions start with the most basic requirements for coverage and reimbursement. A new technology’s first step in market positioning (after FDA approval) is usually attaining a procedure code for the physician or other professional services related to use of the device and used for billing and reporting. Procedure codes are based on an indexing system called the Current Procedural Terminology (CPT) system. This system is managed by the AMA in conjunction with dozens of specialty medical societies. The AMA CPT Editorial Panel, consisting of representatives from specialty medical societies meets periodically to review new applications for codes, or for the inclusion of new procedures under existing codes. The Committee reviews the clinical trial data, published data concerning the clinical utility of the device, other relevant information and makes a series of recommendations regarding coding for the new procedure.

One of the more recent, but increasingly important, pieces of information the Committee requests from manufacturers is cost data demonstrating that either (1) the technology contributes to lowering overall costs associated with current approaches to treating patients for similar problems; or (2) that the clinical benefits represent a breakthrough of such magnitude that the increase in overall treatment costs can be justified.

Billing vs. Reimbursement

Many technology companies mistake getting a procedure code with automatically getting reimbursement for medical procedures using the technology. In reality, these are related but independent decisions. In general terms, a CPT code will facilitate reimbursement, but the existence of a CPT code does not guarantee reimbursement. Except on very rare occasions, Medicare leaves the conditions of payment to the local Medical Directors affiliated with the Medicare fiscal intermediary in each region. Each commercial insurance carrier and each state also makes its own decisions about coverage and reimbursement, although increasingly they follow Medicare’s lead, if there is one to follow. In every case, many of the questions asked by these entities focus on the cost-effectiveness of the technology. They want to know if the new technology is more or less expensive to provide than current approaches, and they want hard data to prove it.

In addition to getting favorable coverage and reimbursement decisions, medical providers must be persuaded to use the new technology, particularly when their reimbursement is fixed. A hospital that is reimbursed under a DRG (Diagnosis-Related Grouping--Medicare’s system for inpatient reimbursement) is reluctant to purchase new technology if it is more expensive to buy or maintain than its existing technology. This is because the DRG payment is a fixed fee covering all services required to treat patients within a grouping of diagnoses, irrespective of the technology used for a given patient. (There is some additional reimbursement available through a hospital’s Medicare cost reports, but this is limited in scope.)

So given the desire of purchasers, users and policy-makers to understand the economic as well as clinical value of new technology, how does a technology company go about demonstrating the economic value of its technology?

There are three basic tenets covering the use of health economic data in the coverage and reimbursement process. First, the data used must be credible. Secondly, it should be timely. And finally, it should be directly relevant to the decision-maker.

Credibility

The accepted gold standard of clinical evidence is the randomized-controlled trial. There is no clear corollary in the study of health economics, so decision-makers often evaluate economic data more subjectively. First, they want to know whether the data is developed internally by the manufacturer or whether an outside entity with fewer vested interests in the study outcomes performed the study. They want to know details about the source of the data: How big was the sample of patients? Does the sample come from one or two institutions, or is it representative of national trends? How were the economic costs calculated, and is the costing methodology unique to the study locations or can it be generalized? In general, coverage and reimbursement decision-makers are more comfortable with data that is developed independently from the manufacturer, is national in scope, and is based on large samples of patients.

A related question is the acceptability of data collected from a prospective economic study, a retrospective review of patient data, or statistical modeling approaches. A prospective economic study is conducted much like a clinical trial. Typically, patients are organized into two or more groups, some receiving the new technology and some receiving the conventional procedure. The costs of caring for each group of patients are collected and compared.

A retrospective study examines the historical records of individuals who have received the conventional procedure to identify the treatment pathways and costs for these patients. Sometimes, the clinical trial results for the new technology are also examined to identify the ways in which the conventional treatments are likely to change with the new technology. The study then projects the likely changes in the costs of caring for patients receiving the new technology.

Statistical modeling techniques take a variety of forms, but often focus on statistically projecting the results of a small prospective or retrospective analysis in order to generalize the study results to larger patient populations.

The advantage of a prospective study design is that it most closely resembles the accepted methodology for clinical trials. The disadvantage is that prospective studies are extremely costly and time consuming if both inpatient and outpatient data are collected and analyzed--contrary to the second tenet of economic data: timeliness to the decision-making process.

Retrospective study designs, on the other hand, provide access to large quantities of patient level data, can be performed relatively quickly and at a more affordable cost to the study sponsor. However, these studies usually project likely economic outcomes associated with the new technology. For this reason, the skills and experience of those performing the retrospective studies are very important in establishing credibility.

The advantages of statistical modeling, whether based on small prospective studies or on retrospective data is the ability to generalize from small amounts of data. Its disadvantages are two-fold. First, most statistical models are not easily comprehended by decision-makers, making them impractical for direct decision-support (as opposed to publication). Second, statistical models based on very small patient samples are often suspect due to the limited size of the original patient study.

Timeliness

The second tenet is timeliness. Coverage and reimbursement decision-makers want time to make informed decisions while manufacturers want coverage and reimbursement decisions made early in product launch. To meet the rising demand for cost-effectiveness data, savvy manufacturers are presenting cost data early in their applications for coverage and reimbursement. This means that manufacturers are planning for economic evaluation during—not after—product development. Waiting until a new product is launched can seriously undermine efforts to get favorable coding, reimbursement and purchase decisions within the timeframe needed by most manufacturers.

Relevance

A study design focused on changes in quality adjusted life years (QUALY) might make a compelling case to an employer or to the government, but does little to relieve the short-term financial concerns of a hospital interested in return on investment (ROI), or a health insurer concerned about rising per member per month (PMPM) costs and medical-loss ratios. The economic analysis must answer the pertinent questions of the decision-maker reviewing the data. This means that the authors of the study must understand the healthcare marketplace as well as economic modeling techniques.

To address many of these issues, a new breed of cost studies is being developed, called ‘value’ models. This approach involves large-scale retrospective data studies to project the cost outcomes for all the interested parties of a new technology, including insurers, medical providers, employers and patients. The results of the studies are placed in an interactive computer program to allow each interested party to change any of the assumptions in the study—customizing the analysis to fit their own situation.

From the decision-maker’s perspective, value models provide important decision-support information when it’s needed most—when the pressure is on to make the coverage or reimbursement decision. For the manufacturer, value models refocus the question of health economics from one of technology price to the overall cost impact of a new technology. This makes good economic sense for both parties.

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