Controlling Healthcare Spending on Medical Imaging: Paging Dr. Bayes
Timothy F Christian, MD, MPA
The Mossaver-Rahmani Center for Business and Government
Harvard Kennedy School
Harvard University, Cambridge, MA
Address
Timothy F Christian, MD, MPA
The Mossaver-Rahmani Center for Business and Government
Harvard Kennedy School/ 5 Belfer
79 JFK Street
Cambridge, MA 02138
Timothy_christian@hks.harvard.edu
Medicine is a science of uncertainty and an art of probability
-Sir William Osler
Spending on medical imaging has doubled since 2000. In a single year, imaging costs
accounted for 27% of the increase in overall healthcare costs (1,2). This rapid increase
cuts across public, private and managed health care systems (1). Imaging has come into
focus politically. As imagers working in dark rooms, such notoriety is novel but also
most disturbing. The Admistration’s budget for 2012 calls for across the board cuts in
imaging spending and implementation of broad prior-authorization program for
Medicare patients. The savings of this approach is a modest $820 million dollar
reduction in spending and a probable dose of provider resentment. Others would like to
put imaging under the microscope of cost-effective or cost utility analyses by the new
Patient-Centered Outcomes Research Institute created by the Affordable Care Act (3).
The current increases in healthcare spending are unsustainable, as the true cost has
been buried within deficit spending. But neither broad reductions in reimbursement
and access nor cost effective analyses studies are likely to be effective for the imaging
problem. There is a better, simpler approach.
Cost effective analyses work well for therapies because outcomes are directly linked to
the therapy. For example, there have been excellent studies (4,5) examining the cost
effectiveness of coronary bypass surgery against coronary angioplasty, the latter being a
far less invasive approach to relieving obstructed arteries in the heart. There are
objective outcomes: survival, relief of anginal chest pain, and functionality. Quality of
life questionaires (the heart of the utility analysis), reveal that quality of life is superior
for angioplasty in the first month, as surgical patients have recovering wounds but this
advantage evens out by 6 months. We also see that diabetic patients have improved
survival with bypass, so there is variability in the response depending on patient group.
CEA and CUA are thus quite effective as tools to compare and contrast therapies.
But medical imaging is quite different (6). There is no direct outcome to measure
because survival, quality of life, symptom relief, all come from a therapy, not the
diagnosis. Therapies can vary widely. In the case of coronary artery disease it can take
the form of medication, exercise programs, angioplasty, bypass surgery or nothing. The
imaging outcome cannot dictate the therapy. The final decision to apply a therapy is the
result of a chain of information which starts with a conversation and an exam. Imaging
is an intermediary step. The only way to isolate the imaging portion of this chain is to
hold the therapy constant, which is not medically feasible.
But what about quality of life measures (often assessed as quality adjusted life years or
the QALY)? Lets take an example. Many people have anxiety toward heart disease
because of its silent, common and sudden nature. Pressure for screening is fairly high,
but, even more so than most diseases, there are risk factors that are known to
predispose one to the condition. In the absence of these, screening (say in the form of
stress test imaging) is not indicated because the pre-imaging risk is so low that almost all
the results performed on such patients will be normal. The QALY does not uncover this.
Since having coronary heart disease is a negative good, a person’s QALY goes up with a
negative test! It was an inappropriate use of imaging but as far as doctor and patient are
concerned, it is a win-win strategy. The QALY is misleading in this common example.
The test will also analyze favorable in terms of outcomes because it will predict no
events, and due to the low pre-test risk of the patient, no events is what the analysis will
show.
Which brings us to back to what imagers have known for many years: the performance
and utility of an imaging test is a function of its inherent accuracy as well as the
prevalence of disease in the population being tested. This is Bayes Theorem. Additional
information has the greatest impact when uncertainty is highest. This is graphically
displayed in figure 1. The function of the clinical history and exam is to take the
probability of the presence or absence of disease from a flip of a coin to a greater
certainty. If the pre-test probability on this first go-round is very low, then nothing
more should be done. If high, then definitive diagnosis and therapy should be sought.
But often the history and physical leave the probability in the intermediate zone, where
further information is needed. Just what constitutes low, high and intermediate
probability are a function of the potential risk of the disease. Consequently, a coronary
artery disease question has higher stakes than the etiology of low back pain. But the
principles remain the same: Most of the potential incremental information of imaging
and thus the value for money of the test will lie in the intermediate group. How much
value depends on the accuracy of the test. Imaging modalities of CT, MRI, and PET
imaging generally afford better overall accuracy (at a price) but no imaging test will
provide much value for money in either the very low or high-risk groups (see figure 2).
Then how are we to control the rising costs of medical imaging? There are many forces
that contribute to this rise. These include better imaging technology and resolution.
How many of us would like to go back to earliest models of mobile phones, even though
they would be much cheaper and provide the basics (which used to be voice
communication)? Patients have come to expect a definitive answer to their concerns
and imaging offers this. Imaging provides a bump up in reimbursement for office visits.
In the face of declining payouts overall, this is a driver. Imaging buys reassurance for
physicians against malpractice claims. Finally, imaging is incredibly powerful in
diagnosing pathology. These are all potent forces so an equally potent solution is
required to counter.
The most straight-forward solution is to tie reimbursement for imaging, either on the
provider side or consumer side, to the pre-test probability of the disease in question.
Virtually all imaging modalities have appropriateness critieria agreed upon by expert
physicians (8,9). These are evidence-based and published. For example, for nuclear
stress imaging, detailed appropriateness criteria for patients based on symptoms, other
tests in hand, and risk factors have been carefully worked out (10). All of them
recommend against imaging very low pre-test risk patients. These panels have decades
of outcomes research publications to draw from. New trials confirming these facts are
not needed. What is needed is to give these criteria teeth. By weighting
reimbursement to the appropriateness classification, there will be a driving incentive to
keep imaging centered on the intermediate risk populations, where it needs to be.
Tying malpractice reform to this incentive-based strategy will be critical.
References
1. Smith-Bindman R, Miglioretti DL, Larson EB. Rising use of diagnostic medical
imaging in a large integrated health system. Health Affairs 2008; 27: 1491-1502.
2. MedPAC. A Data Book; Health Care Spending and the Medicare Program.
3. The Institute of the National Academies. 100 Initial Priority Topics for
Comparative Effectiveness Research. 2009
4. Hlatky MA, et al. Coronary artery bypass surgery compared with percutaneous
coronary interventions for multivessel disease: a collaborative analysis of
individual patient data from ten randomised trials. Lancet 2009; 373: 1190-1197.
5. Garber AM. How the Patient-Centered Outcomes Research Institute can best
influence real-world healthcare decision making. Health Affairs 2011; 30: 22432251.
6. Tatsioni A, Zarin DA, Aronson N, Samson BA, Flamm CR, Schmid C, Lau J.
Challenges in Systematic Reviews of Diagnostic Technologies. Ann Intern Med
2005; 142: 1048-1055.
7. Grundy SM, Pasternak R, Greenland P, et al. AHA/ACC scientific statement:
assessment of cardiovascular risk by use of multiple-risk factor assessment
equations: a statement for healthcare professionals from the American Heart
Association and the American College of Cardiology. J Am Coll Cardiol.
1999;34:1348 –59.
8. Patel MR, Spertus JA, Brindis RG, Hendel RC, Douglas PS, Peterson ED, Wolk MJ,
Allen JM, Raskin IE. ACCF Proposed Method for Evaluating the Appropriateness
of Cardiovascular Imaging. J Am Coll Cardiol 2005; 46:1606-1615.
9. Sistrom CL. The Appropriateness of Imaging: A Comprehensive Conceptual
Framework. June 2009 Radiology, 251, 637-649.
10. ACCF/ASNC/ACR/AHA/ASE/SCCT/SCMR/SNM 2009 Appropriate Use Criteria for
Cardiac Radionuclide Imaging. ASNC 2009.
Figure 1
Figure 1
Bayes theorem graphically represented. Individual pre-test probability (x-axis) is plotted
against the post-imaging test probability of the disease in question. Dashed
line=identity (no added value). In this type of analysis, it is assumed that the pre-test
probability is a reflection of the prevalence of disease in that patient probability
classification. The degree of convexity of the positive result and negative result curves
increase as the sensitivity and specificity of the test increase. This curve approximates a
test with 90% sensitivity and 80% specificity, which is an approximation of stress testing
with imaging. Note that at 50% pre-test probability (A), the gain in information
regarding disease probability is maximal (a positive test increases the likelihood of
disease from 50 to 85% certainty). For low risk patients (B) where presence of disease
is rare, neither a positive nor a negative test result moves a patient out of the low
probability range. This is because nearly all the positive responders in this range are
false positives. The rate of false positive do not change based on prevalence.
Figure 2
Figure 2: Comparison of two tests of different accuracy and cost for the detection of
coronary artery disease. Nuclear stress imaging (black lines) is portrayed with a
sensitivity of 90% and specificity of 80% while exercise testing without imaging
(approximately 4-5-fold less expensive) is assumed to have a sensitivity of 70% and
specificity of 60%. At low prevalence (arrows) there is still minimal added value with a
positive or negative result independent of cost. The cost portion of the analysis is more
favorable, but the value of the information is unchanged. Consequently, simply
substituting a less expensive test in low-risk patients has limited justification.