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Innovation in Diagnostic Imaging Services

Innovation in the field of diagnostic imaging is based primarily on the availability of new and improved equipment that opens the door for new clinical applications. Payments for these imaging procedures are subject to complex Medicare price control schemes, affecting incentives for appropriate use and innovation. Achieving a “dynamically efficient” health care system—one that elicits a socially optimal amount of innovation—requires that innovators be rewarded in relation to the value they add and can demonstrate with evidence. The authors examine how and whether value-based reimbursement for diagnostic imaging services might better reward innovation explicitly for expected improvements in health and economic outcomes.

In a 1984 Wall Street Journal op-ed piece, Alan Greenspan , then head of the National Commission on Social Security Reform, predicted the eventual collapse of Medicare’s newly instituted prospective payment system, which set flat fees for hospital stays in some 470 different diagnosis-related groups: “The major difficulty with the prospective payment system is that it has the faults of any price-control scheme and is therefore likely to go the way of similar initiatives of the past.…Irresistible pressures begin to create exemptions to the control system, and eventually the exemptions become the system. Then it is only a matter of time before the control system unravels.”

Well, Medicare’s two major national price-control schemes for hospital and physician-related services, both of which provide reimbursement for diagnostic imaging interpretation and equipment, have lasted 28 and 19 years, respectively, and neither appears to be on the verge of imminent failure. Greenspan was particularly wary about the ability of such “administered pricing” systems to deal with technological innovations, claiming, “The momentum of technology will require too many special cases.” But Medicare has developed ongoing processes for revisions and updating as service provision changes. Nonetheless, Greenspan was right that such price-control schemes can create distortions and incentives that may promote inappropriate care as well as hinder innovation.

Because technological innovation does not occur equally in all types of health care services, price controls may affect some areas more than others. We focus here on the rapidly growing field of diagnostic imaging procedures. They are offered in a variety of settings and are now subject to a complex set of payment schemes under Medicare. In this paper, we examine these incentives and explore the feasibility and possible impact of an alternative approach that rewards innovation by basing facility payments for imaging procedures on the expected improvements in health and economic outcomes resulting from those procedures.

The motivation for this exploration derives from three notable trends. First, total Medicare spending on imaging nearly doubled from $6.4 billion in 2000 to $12 billion in 2006, leading Congress to make broad cuts in diagnostic imaging payment levels in the Deficit Reduction Act of 2005. These are not nuanced cuts applied to specific procedures, raising concerns about the short-term impact on access to specific imaging procedures and the long-term incentives for innovation. Second, there are increasing calls for greater use of evidence-based medicine, including studies of comparative effectiveness and cost-effectiveness. Third, the ongoing debate about value-based pricing for drugs in the United Kingdom raises general issues about how best to establish payment for innovative medical interventions, leading us to speculate on lessons for innovative diagnostic imaging in the United States.

Economics of medical price controls and innovation

A key aim of medical price controls is to simulate or approximate the outcome of a market, whereby competitive forces tend to push prices to the level of “long-run marginal cost” (ie, what it costs to make something) but including a competitive rate of return for any investments made or for any risks incurred. At this quantity-price point, demand and supply are in equilibrium, and value (what consumers are willing to pay) and cost are identical.

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Diagnostic imaging technical services and the resource-based relative value scale

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About 600 of the codes apply to diagnostic imaging.

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Innovation in medicine: pharmaceuticals versus diagnostic imaging

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The impact of price controls on innovation and product quality

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Problems with reimbursement for diagnostic imaging technical services

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Complexity

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Incentives and Standards for Appropriate Use

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An economic approach to value-based reimbursement

Defining Value-based Reimbursement

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UK Debate on Value-based Pricing for Drugs

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Economic Models for the Evaluation of Diagnostic Imaging

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A way forward for considering value

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Limited Evidence Base

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High-volume and High-cost Imaging Procedures

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Table 1

Projected Spending on the Most Commonly Billed Imaging Procedures Ranked by US Total Expenditures (2007 Payment Levels Applied to 2005 Procedure Volumes)

Rank CPT Code Description 2005 Volume (×1000) Physician Component Payment ∗ Technical Component Payment ∗ Projected Spending for Procedures in 2007 (×1,000,000) ∗ Cumulative Percentage of Total US 2007 Imaging Spending 1 70450 CT of head/brain without dye 4202 $41 $185 $949.1 7.0% 2 93307 Echocardiography transthoracic (exam of heart) 4152 $47 $150 $816.6 13.0% 3 72193 CT of the pelvis with dye 2105 $55 $251 $644.8 17.7% 4 78465 Myocardial perfusion imaging; tomographic, multiple studies, at rest or stress 1363 $73 $400 $644.4 22.5% 5 74160 CT of the abdomen with dye 1975 $61 $251 $616.2 27.0% 6 71260 CT of the thorax with dye 1815 $60 $251 $563.5 31.2% 7 70553 MRI of the brain without dye, followed by dye and further sequences 848 $113 $499 $518.3 35.0% 8 71010 Chest x-ray 18,447 $9 $17 $482.4 38.5% 9 71020 Chest x-ray 12,449 $11 $24 $429.4 41.7% 10 93325 Doppler color flow add-on 4211 $4 $96 $421.3 44.8%

CPT, Current Procedural Terminology; CT, computed tomography; MRI, magnetic resonance imaging.

Source: Authors’ calculations based on Medicare public use data.

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Conclusions: developing a research agenda

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References

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