Radiology technology purchasers face a growing dilemma: evaluating the huge number of new technologies being approved and aggressively marketed in today’s environment and making the right decisions about technology purchase and use.

Because of the sheer volume of new technology, this challenge is getting progressively trickier. Changes in the FDA Modernization Act of 1997 have shortened review and approval times for a host of new technologies. And as technology manufacturers take lessons from the pharmaceutical industry and start promoting their products directly to the public, patient demand is taking unexpected turns. Patients now request the latest new technologies, from multislice CT scans to innovative, lifestyle-enhancing medications. Information about new technology is ubiquitous, with sources ranging from a multitude of consumer-oriented Internet sites to celebrity doctors discussing the newest technology on nationally syndicated talk shows. (The Los Angeles Times, for example, runs a new technology column in every Monday’s health section.)

At the same time, the profit and operating margins of the nation’s technology purchasers — hospitals, physicians, and public and private insurers — have rarely looked worse. Several years of market share competition and recent surges in health care costs have left insurers struggling for profitability. Providers who receive fixed or declining reimbursements from payors are even worse off. Hospitals are facing deep cuts from Medicare due to provisions in the Balanced Budget Act of 1997 (BBA) mandating reductions in Medicare spending. Risk-taking physician groups are failing across the United States because of the growing gap between the cost of providing medical care and the reimbursement for services.

Drugs and devices are tagged as a primary reason for declining profitability. Drug prices are expected to increase nearly 20% in the coming year, and while projections are not as available for medical equipment costs, the use and cost of medical technologies are at an all-time high. In this business environment, technology purchasers are understandably leery of expensive new technology without first being confident of its potential economic and clinical benefits.


Narrower business time frames and higher expectations for profitability further complicate decisions about technology purchase and adoption. In the not too distant past, a hospital might have several years to build a new service and establish profitability. In today’s world, that time frame is significantly shorter due, in part, to the transition from not-for-profit to for-profit status of many of the nation’s hospitals, although the nonprofit hospitals are not far behind. Hospital CFOs are being held accountable for quarterly returns, and are shifting that pressure to each clinical unit.

At the clinic or department level, the situation often appears futile. Many hospitals engage in bud-geting and accounting practices known as silo bud-geting. As cost pressures mount, each department is responsible for reducing expenditures and improving the department margin. For many departments this works well, but for radiology departments this situation can spell disaster. Unlike most other hospital departments, radiology is better able to reduce expenditures in other clinical areas than in its own. Since the radiology department often functions as the diagnostic gateway to medical procedures, improvements in technological capabilities or productivity can dramatically reduce or eliminate unnecessary services in clinical service areas throughout the hospital. Silo budgeting, by definition, makes it difficult to reward (or even acknowledge) savings that are generated by one department that directly affect another. In fact, if operating costs increase within the originating department, its administrator is often chastised for exceeding operating budget limitations, even if savings were generated elsewhere in the hospital.

Despite these pressures, department administrators are often critisized for short-term thinking by focusing on the immediate acquisition price of new technology instead of its longer-term potential. But given the current system’s rewards and punishments and the dearth of data proving cost-savings, can they really be expected to act any other way? Most technology manufacturers provide information about technology features, benefits, and clinical value. Lacking are studies that identify the cost-benefit of technology with enough detail that the true cost-savings potential can be documented and verified over time.

The medical device industry has only recently begun to seriously invest in studies of cost-effectiveness and cost-benefit. The good news is that the industry is leaning toward study designs that emphasize practical answers to the questions posed by technology purchasers and users. These practicalities include a focus on potential medical cost offsets over an episode of treatment as opposed to a patient’s lifetime, the desire to understand technology costs within the context of current reimbursement, and the presentation of study results in the language of cost accounting instead of biostatistics. The bad news is that the majority of technology manufacturers still do not fully appreciate the pressures of the purchasing community and are not providing enough information on the economic and clinical value of new technologies.


The newest approaches to assessing the economic and clinical contributions of new technologies are called value models, which project the cost outcomes of a new technology for all the interested parties and from their unique perspectives. These parties might include insurers, medical providers, employers, public policy-makers, and patients. For example, a hospital purchaser might be most interested in whether a new technology can improve clinical outcomes during the hospital stay while at the same time:

  1. decreasing procedure-related expenses;
  2. reducing hospital length of stay;
  3. reducing the expensive complications of conventional approaches; or
  4. decreasing or eliminating the need for additional unreimbursed diagnostic services or medical procedures.

A health plan evaluating the same technology may want to know how the new technology impacts:

  1. outpatient care, including professional and technical services;
  2. outpatient pharmaceutical use and cost; and
  3. home care or rehabilitation services, in addition to the possible inpatient efficiencies.

Value models examine these practical questions in detail by investigating a technology’s cost outcomes in a variety of ways. Typically, the analysis begins with an examination of large databases of patient medical service history. By linking together individual patients’ records covering all medical services over entire episodes of care, researchers can identify the patterns of care that define the conventional treatment pathways. Both the usage of services and their related costs are identified and quantified by service category, including the costs associated with the procedure itself, typical follow-up care, and care required to treat unexpected complications of the procedure.

Once these care components have been identified and quantified, researchers turn their attention to the clinical data pertinent to the new technology being studied. How did the use of the technology change the typical treatment pathway in the clinical trial population? Is there other clinical literature that can shed light on how the clinical characteristics of the new technology will likely change both the process and outcome of conventional treatments? What is the likely time frame governing the migration of the new technology’s use through the practicing physician community? How will the dynamics of direct-to-consumer advertising affect patient demand for the technology and its spread through the professional community?

On the basis of the results of these investigations, the models are programmed to calculate the changes in clinical and cost outcomes associated with the technology’s use and to compare these outcomes to existing treatments. In some cases, the interactive computer programs allow each interested party to modify many of the data points and assumptions in the study — including patient demographics, payor mix, reimbursement amounts, operating cost and utilization, and patient demand characteristics — customizing the analysis for each situation. In this way, technology purchasers can project the potential clinical and cost outcomes of a new technology within their own patient, market, and business environment.


As an example, consider the economics for a new contrast agent. Put simply, a value model might study the extent to which use of the new contrast agent could result in:

  1. modification of existing diagnostic pathways to reduce duplicative or unnecessary follow-on testing;
  2. increased diagnostic efficacy specific to certain diseases that are responsive to early intervention; and
  3. reduced utilization of diagnostic services with substandard financial returns.

Step one of the analysis would involve reviewing historical patient data to isolate the diagnostic pathways — meaning the sequence of diagnostic tests until a diagnosis is established and treatment begun — common to selected diseases of interest. The results of this analysis can be visualized as a decision-tree with the most common diagnostic pathways represented by each branch of the tree. The usage of each service and the related cost are identified at each point through the diagnostic pathway. The sum of these costs represents the average cost-to-diagnosis for each pathway.

The clinical literature associated with the new contrast agent may identify the clinical improvements the agent furnishes to conventional diagnostics. Using these data, the value model predicts the potential change in the diagnostic pathway (and projected time frame for change) associated with the use of the new agent and related changes in diagnostic costs as well as projected changes in the overall mix of diagnostic modalities used in a given hospital facility. By comparing the operating costs associated with various diagnostics to reimbursement for the diagnostic procedures, the value model can pinpoint the return on investment (ROI) associated with the conventional diagnostic pathways and compare that to the expected returns generated through the use of the new contrast agent. These results are displayed by type of equipment, by disease-specific diagnostic pathway, and in aggregate.


In addition to quantifying potential reductions in diagnostic costs, the model can identify for health plans possibilities for earlier diagnosis in certain diseases that may respond to earlier intervention. In this case, the analysis would continue through actual treatments to identify possible long-term savings and clinical outcome improvements for patients with certain disease characteristics, as well as the shorter-term savings in diagnostic efficiencies. These data provide compelling decision-support information for coverage and reimbursement decisions.

Alternatively, the model may predict that anticipated modifications to diagnostic pathways are unlikely to produce cost-savings that sufficiently offset the cost of the contrast agent itself along with the extra costs of personnel and procedure time required to administer the agent. At a minimum, radiology administrators and their colleagues are able to evaluate the contrast agent on the basis of its true cost and benefit to an institution, as opposed to the price of the agent alone. This provides important information to weigh against the clinical benefits of the new technology.

Possibly most important to radiologists and radiology administrators, value models can pinpoint where in a hospital’s clinics, departments, and services the cost-savings are to be found and how much to expect, given the specific population and business inputs that have been fed into the model. Once identified, these sites can be monitored over time to see whether expected efficiencies and cost-savings actually occur and are sustainable over time. These data reinforce radiology’s value to the overall institution and provide valuable benchmarks to support medical management and future technology decision-making. By lessening the uncertainty regarding costs and benefits and establishing benchmarks for ongoing evaluation, value models can make decision-making a bit less risky.


Nancy L. Reaven is president of Strategic Health Resources, La Canada, Calif