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P E R S P E C T I V E S
V A R I A T I O N S
W E B E X C L U S I V E
7 October 2004
Perspective:
More Variation In Use Of Care,
More Flat-Of-The-Curve Medicine
Why does it occur? What
should be done about it?
By Victor
R. Fuchs
ABSTRACT:
Variation in use
of health care is ubiquitous in the United States. It is attributable to
exogenous differences in supply of medical resources; to identified and
unidentified economic, social, and cultural factors; and to the idiosyncratic
beliefs of physicians. It is perpetuated by the parochial character of
much clinical practice. Patients in high-intensity areas do not appear
to have better health outcomes: Much care is “flat of the curve.” A
more robust scientific foundation for clinical decisions could help to reduce
variations, but major reform of health care financing is probably necessary
to achieve substantial improvement in the organization and delivery of care.
The three contributions that
provide the starting point for my commentary investigate variation in intensity
of services provided to Medicare beneficiaries.1 The
authors of these three papers explore the consequences of variation, try to
explain it, and offer recommendations for its reduction. The papers differ
in the units across which variation is measured: academic medical centers (AMCs),
hospital referral regions (HRRs), or “well-known” hospitals. They
also differ in the study populations: patients with serious chronic illnesses,
patients undergoing major orthopedic surgery, and patients in the last six
months of life.
Data sources. The primary sources
of data are individual records of Medicare beneficiaries. The sample sizes
are large, and the records provide much information concerning patients’ characteristics,
use of medical care, and health outcomes. I am hesitant to criticize the data
because, for many questions, they are the “only game in town.” One
caveat, however, deserves mention. The Medicare data cover only a portion of
the services provided to patients. Variation in the use of services not covered
by Medicare may not parallel variation in covered services. Sometimes the differences
can be dramatic. For example, Jonathan Skinner and Weiping Zhou found that
per capita Medicare spending in 1996–98 for seniors in the lowest income
decile was 30 percent higher than for those in the highest decile.2 Alex
Chen and José Escarce, using the more comprehensive measure of services
in the Medical Expenditure Panel Survey (MEPS) for the same period, estimated
that wealthy seniors accounted for 83 percent more spending per capita than
poor seniors.3
Certainty of variation. The
least controversial result of the three studies is that there is much variation
in the amount of care delivered to what appear to be comparable patients. This
result has been reported many times in the past, by John Wennberg and colleagues
and by others. There should be no residual doubt in the health policy community
that the variations are real phenomena; they are not the result of comparing
apples with oranges. Moreover, patterns of variation persist over time. If
A shows more intensity of care than B for a particular patient population in
a given year, there is a good chance that A will also show more intensity one
year or even ten years later.
Role of intensity. Slightly
more controversial is the conclusion that patients in high-intensity areas
do not experience better health outcomes than those in low-intensity areas.
Some readers will question this result; I find it quite credible. It accords
with my reading of aggregate data in the United States and other high-income
countries during the past forty years: Differences in intensity of care play,
at most, a minor role in explaining cross-section differences in health outcomes.
The reasons are reasonably clear. First, truly effective
interventions tend to diffuse widely and rapidly—for example, antibiotics
for combating infectious diseases and diuretics for reducing hypertension.
Second, where supplies of physicians, specialty care, or hospitals are tight,
some informed rationing occurs. Third, and most important, cross-section differences
in health outcomes are primarily determined by nonmedical factors: genetics,
the physical and psychosocial environments, socioeconomic factors, and personal
behavior.
The bottom line is that a considerable amount of the care
delivered in the United States is “flat-of-the-curve” medicine.
This term, popularized by Alain Enthoven, describes a level of intensity of
care that provides no incremental health benefit.4 This
conclusion is not refuted by two propositions that are popular in current health
policy discussions: (1) the total value of health care is greater than its
total cost, and (2) the value of the increase in health that Americans have
experienced since 1950 is greater than the increase in health care spending.
Exhibit
1 shows the relationship between health and intensity
of care at two different times, t and n years
later ( t + n ).
In both periods, the level of intensity is at the flat of the curve, points
A and B. Nevertheless, the value of health at A (HA) could
well be greater than the cost (CA). Similarly, the value
of the increase in health from time t to t + n,
measured by HB–HA, could well be
greater than the increase in cost, measured by CB–CA.
But it is the flat-of-the-curve conclusion that is particularly relevant to
health policy. At any given time, policy usually involves choosing between
more care or less; good decisions require comparing incremental benefit and
incremental cost.
One caveat to this conclusion is its reliance on survival
rates, life expectancy, or some other measure of mortality. Measures of quality
of life could show some incremental benefit in high-intensity areas. Reliable
measures of improvement in quality of life, however, are difficult to obtain.
They are not nearly as objective as mortality rates and are subject to bias.
For example, surgical patients who have experienced a great deal of trauma
and cost are often reluctant to acknowledge to themselves or to researchers
that it was all in vain.
Quality of life. An
important consideration in policy analyses of quality-of-life claims is the
source of payment for more intensive care. If payment is borne collectively,
through private or public coverage, the standard of evidence should be much
higher than if consumers are paying with their own funds. Each day Americans
spend billions of dollars for goods and services that they think will add to
their quality of life, even though experts might think otherwise—for
example, they flock to movies that professional reviewers have judged as terrible;
they consult astrologers, psychics, and faith healers; they buy brand-name
over-the-counter drugs when generic versions are available for half the price;
and the list goes on. The free expression of consumers’ preferences,
backed by their own pocketbooks, is not usually considered a major social problem.
But collective payment for medical care of questionable value is. When standard
outcome measures say that the curve is flat, it is incumbent on those delivering
care at that level to justify collective payment for it.
The strongest rationale for equal access to care is that
everyone ought to have an equal chance to live, regardless of economic circumstances.
But when quality of life is the object of high-intensity care, the egalitarian
imperative for collectively funding such care loses much of its force. Quality
of life for the poor could be improved in many other areas; disproportionate
allocation of resources to medical care does a disservice to the poor and
to society as a whole.
Reasons for variation
and flat-of-the-curve medicine. Why
is there so much variation in use and so much flat-of-the-curve medicine in
the United States? According to the three studies, the principal explanation
is that excess supply induces additional demand. In my research, I found evidence
of supply-induced demand.5 I
am not, therefore, inclined to reject this hypothesis in principle, but the
Dartmouth studies do not provide rigorous tests of it. It is not enough to
show a correlation between supply and utilization. One must explain why supply
differs and establish that the differences are independent of any differences
in demand attributable to economic, social, and cultural factors. My surmise
is that supply does matter, but not to the extent suggested by the authors
of these studies.
The authors also imply that regardless of origin, variations
in medical practice tend to persist because of the parochial approach of
much clinical practice. I agree. Consider the following hypothetical (but
not unrealistic) exchange between a new resident and a chief of surgery.
New Resident: “How
long should a patient stay in the hospital after operation X?” Chief
of Surgery: “In our hospital we keep the patient for N days.” There
would probably be no discussion of the fact that in other hospitals, the
standard might be N+1
or N–1 days. Also, there would probably
be no appraisal of the scientific basis (or lack thereof) for the answer,
and there would be no consideration of the benefits and costs of N+1
or N–1.
The story about length-of-stay could be repeated regarding
indications for surgery, use of diagnostic tests, and most clinical decisions.
Conformity with local practice is reinforced by a desire to follow the example
of physicians who command respect or authority. Conformity also flows from
patients’ expectations, which are partially formed by the treatment received
by neighbors and friends. Deviations from local standards are always suspect,
must be justified, and are more vulnerable to ex post criticism if things
turn out badly (even if the reasons for the bad outcome lie elsewhere).
Policy considerations. The
two policy recommendations that emerge from these studies are not unreasonable,
but to me they fall far short. First, the authors suggest more patient involvement
in clinical decision making. If patients want to be involved, who could object?
But some might prefer to let the physician decide. Should they be denied
that option? Also, the authors seem to assume that patient involvement will
result in less care in high-intensity situations. But it may also result in
more care when intensity is low. The overall effect on spending is not obvious.
The other major recommendation is for more research to provide a firmer scientific
foundation for clinical decisions. Amen! But no one should imagine that this
problem is easily remedied.
The technical difficulties involved in providing a more
robust scientific basis for the myriad clinical decisions that determine
the cost of medical care are formidable. Given the rapid rate of introduction
of new medical technologies and the consequent rapid growth of health spending,
there is urgent need for a large, private, nonprofit institution to assess
the cost-effectiveness of various interventions.6 It
must have a large, steady source of funding and be as free from political
and professional pressures as possible. Its primary function would be to
help develop and disseminate systematic knowledge about the cost-effectiveness
of medical technologies, including drugs, diagnostic procedures, and surgical
interventions. Its second important function would be to provide legitimacy
for the cost-effective practice of medicine. Many directors of
health plans and many physicians know that they could be practicing in a
more cost-effective way, but they are inhibited from doing so by fear of
malpractice suits and by pressure from patients and peers.
With regard to policy, the most pressing issue to address
is the role of incentives. Financial arrangements surely influence the choices
made by patients, physicians, hospital administrators, and others. Consider
the role that insurance plays in underwriting flat-of-the-curve medicine.
A fully insured patient wants care up to the point at which there is no incremental
benefit. A physician concerned only with the welfare of such a patient would
recommend care up to that point. Or consider how reimbursement affects clinical
decision making: much more than is generally appreciated because once standards
of care are established in a setting, the individual physician honestly believes
that he or she is delivering appropriate care. But reimbursement has a big
influence on the standards.
These three studies and others show the need for improvements
in the organization and delivery of care. To achieve those improvements,
however, I am increasingly convinced that the United States must undertake
major reform of health care financing.
Financial support from the Robert Wood Johnson Foundation and comments
from Ezekiel Emanuel, Alain Enthoven, and Alan Garber are gratefully acknowledged.
NOTES
1. E.S. Fisher et al., “Variations in the Longitudinal
Efficiency of Academic Medical Centers”; J.E. Wennberg et al., “Use
of Medicare Claims Data to Monitor Provider-Specific Performance among Patients
with Severe Chronic Illness”; and J.M. Weinstein et al., “Trends
and Geographic Variations in Major Surgergy for Degenerative Diseases of the
Hip, Knee, and Spine,” all available at Health Affairs, 7
October 2004, content.healthaffairs.org/cgi/content/full/hlthaff.var.104/DC1.
2. J. Skinner and W. Zhou, “The Measurement and Evolution of
Health Inequality: Evidence from the U.S. Medicare Population” (Paper
presented at a symposium in honor of Eugene Smolensky, Berkeley, California,
12–13 December 2003).
3. A.Y. Chen and J.J. Escarce, “Quantifying Income-Related
Inequality in Health Care Delivery in the United States,” Medical
Care 42, no. 1 (2004): 38–47.
4. A.C. Enthoven, “Shattuck Lecture—Cutting Cost
without Cutting the Quality of Care,” New England Journal
of Medicine 298, no. 22 (1978): 1229–1238; and A.C. Enthoven, Health
Plan: The Only Practical Solution to the Soaring Cost of Medical Care (Reading,
Mass., and Menlo Park, Calif.: Addison Wesley, 1980).
5. V.R. Fuchs and M.J. Kramer, “Determinants of Expenditures
for Physicians’ Services in the United States 1948–68,” NBER
Occasional Paper no. 117 (New York: National Bureau of Economic Research, 1973);
and V.R. Fuchs, “The Supply of Surgeons and the Demand for Operations,” Journal
of Human Resources 13 Supp. (1978): 35–56.
6. V.R. Fuchs, “Health System Reform: A Different Approach,” Journal
of the American Medical Association 272, no. 7 (1994): 560–563;
and V.R. Fuchs and A.M. Garber, “Health and Medical Care,” in Agenda
for the Nation, ed. H.J. Aaron, J.M. Lindsay, and P.S. Nivola
(Washington: Brookings Institution Press, 2003), 145–181.
Victor R. Fuchs (fuchs{at}newage3.stanford.edu)
is the Henry J. Kaiser Jr. Professor Emeritus, Stanford University, and a
research associate at the National Bureau of Economic Research in Stanford,
California.
DOI: 10.1377/hlthaff.var.104
©2004 Project HOPE–The People-to-People Health Foundation, Inc.
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