Car inspections and repairs take a small fraction of our total spending on cars, gas, roads, and parking. But imagine that we were so terrified of accidents due to faulty cars that we spent most of our automotive budget having our cars inspected and adjusted every week by Ph.D. car experts. Obsessed by the fear of not finding a defect that might cause an accident, imagine we made sure inspections were heavily regulated and subsidized by government. To feed this obsession, imagine we skimped on spending to make safer roads, cars, and driving patterns, and our constant disassembling and reassembling of cars introduced nearly as many defects as it eliminated.

This is something like our relation to medicine today. Our public today is like a king of old whose military advisors spent most of their time and budget reading omens and making sacrifices, to gain the gods’ favor, instead of hiring soldiers and talking battle strategy. These advisors knew omens and sacrifices mattered little, but they saw the king was comforted, and feared losing favor by talking of battle strategy. A truly loyal advisor would have told the king what he did not want to hear: “You are obsessing about the wrong thing.”

King Solomon famously threatened to cut a disputed baby in half, to expose the fake mother who would permit such a thing. The debate over medicine today is like that baby, but with disputants who won’t fall for Solomon’s trick. The left says markets won’t ensure everyone gets enough of the precious medical baby. The right says governments produce a much inferior baby. I say: cut the baby in half, dollar-wise, and throw half away! Our “precious” medical baby is in fact a vast monster filling our great temple, whose feeding starves our people and future. Half a monster is plenty.

Am I being too allegorical? Then let me speak plainly: our main problem in health policy is a huge overemphasis on medicine. The U.S. spends one sixth of national income on medicine, more than on all manufacturing. But health policy experts know that we see at best only weak aggregate relations between health and medicine, in contrast to apparently strong aggregate relations between health and many other factors, such as exercise, diet, sleep, smoking, pollution, climate, and social status. Cutting half of medical spending would seem to cost little in health, and yet would free up vast resources for other health and utility gains. To their shame, health experts have not said this loudly and clearly enough.

Non-health-policy experts are probably shocked to hear my claims. Most students in my eight years of teaching health economics have simply not believed me, even after a semester of reviewing the evidence. Heroic medicine is just too central to our culture, a culture where economists like me have far less authority than doctors. Worse, even most standard textbooks in health economics fail to make the point clearly.

Children are told that medicine is the reason we live longer than our ancestors, and our media tell us constantly of promising medical advances. Millions of doctors are well aware that most medical journal articles describe gains from particular medical treatments, and these doctors usually give patients optimistic views about particular treatments.

In contrast, few doctors know that historians think medicine has played at best a minor role in our increased lifespans over the centuries. And only a few health policy experts now know about the dozens of studies of the aggregate health effects of medicine. Worse, these studies can seem muddled, with some showing positive, some showing negative, and some showing neutral effects of medicine on health.

So I want to say loudly and clearly what has yet to be said loudly and clearly enough: In the aggregate, variations in medical spending usually show no statistically significant medical effect on health. (At least they do not in studies with enough good controls.) It has long been nearly a consensus among those who have reviewed the relevant studies that differences in aggregate medical spending show little relation to differences in health, compared to other factors like exercise or diet. I not only want to make this point clearly; I want to dare other health policy experts to either publicly agree or disagree with this claim and its apparent policy implications.

By “variations” I mean the large changes in medical spending often induced by observable disturbances, such as changing culture or prices, and by “aggregate” I mean studies of the health effect on an entire population of disturbances that affect a broad range of medical treatments. In contrast, the vast majority of medical studies look at the effects of particular categories of treatments on particular classes of patients.

Note that a muddled appearance of differing studies showing differing effects is to be expected. After all, even if medicine has little effect, random statistical error and biases toward presenting and publishing expected results will ensure that many published studies suggest positive medical benefits.

Let me illustrate. (A general review is found in Fuchs, Health Affairs, 2004 . A contrarian review is Hadley, Medical Care Research and Review, 2003.) The first study known to me was by Auster, Leveson, & Sarachek, Journal of Human Resources, in 1969 . It found that variations across the 50 U.S. states of 1960 age-sex-adjusted death rates were significantly predicted by variations in income, education, fractions of white collar and female workers, and the existence of a local medical school, but not by variations in medical spending, urbanization, and alcohol and cigarette consumption.

Later studies using robust controls to compare similar regions tend to give similar results. For example, a Byrne, Pietz, Woodard, & Petersen Health Economics 2007 study found no significant mortality effects of funding variations across 22 U.S. Veterans Affairs regions over six years. And a Fisher, et al. Annals of Internal Medicine 2003 study of 18,000 patients confirmed a Fisher et. al. Health Services Research 2000 study, and a related Skinner and Wennberg 1998 study, which together used the largest dataset I know of: five million Medicare patients in 1989 and 1990 across 3,400 U.S. hospital regions.

Regions that paid more to have patients stay in intensive care rooms for one more day during their last six months of life were estimated, at a 2% significance level, to make patients live roughly forty fewer days, even after controlling for: individual age, gender, and race; zipcode urbanity, education, poverty, income, disability, and marital and employment status; and hospital-area illness rates. This same study, using the same controls, also estimated that a region spending $1,000 more overall in the last six months of life gave local patients somewhere between a gain of five days of life and a loss of twenty days of life (95% confidence interval). (I’m using a fifty days lost per 1% added mortality rule of thumb.)

The tiny effect of medicine found in large studies is in striking contrast to the large apparent effects we find even in small studies of other influences. For example, a 1998 Lantz, et al. study in the Journal of the American Medical Association of 3,600 adults over 7.5 years found large and significant lifespan effects: a three year loss for smoking, a six year gain for rural living, a ten year loss for being underweight, and about fifteen year losses each for low income and low physical activity (in addition to the usual effects of age and gender).

Note that someone willing to pay $1,000 to gain 2.5 days of life should be willing to spend about $1,000,000 to gain six years by living rurally, and $2,000,000 to gain fifteen years via high exercise. These figures seem to me to overestimate the observed eagerness to live rurally or to exercise.

Of course all of these studies look at correlation, not causation, between health and medicine. So they all leave open the possibility that someday studies with better controls will show stronger effects. For this reason, discussion of the health effects of medical spending variations usually turns eventually to our clearest evidence on the subject: the RAND health insurance experiment.

From 1974 to 1982 this experiment spent about $50 million to randomly assign over two thousand non-elderly families in six U.S. cities to three to five years of a specific medical price, ranging from free to full price, provided by the same set of doctors. (See the 1983 Brook et. al. New England Journal of Medicine article, and the 1996 Newhouse et. al. book Free for All?) The experiment’s random assignments allowed it to clearly determine causality. Being assigned a low price for medicine caused patients to consume about 30% (or $300) more in per-person annual medical spending, though less for hospital spending and more for dental and “well care.”

The RAND experiment was not quite large enough to see mortality effects directly, and so the plan was to track four general measures of health, combined into a total “general health index,” and also 23 physiological health measures. Their main result: “For the five general health measures, we could detect no significant positive effect of free care for persons who differed by income … and by initial health status.” This summary isn’t fully forthcoming, however. At a 7% significance level they found that poor people in the top 80% of initial health ended up with a 3% lower general health index under free medicine than under full-priced medicine.

Among their many specific findings, the most significant was at the 0.1% level: people with free eyeglasses could see better. But it has long been obvious that eyeglasses help people see, and eyeglasses are basically physics, not “medicine.” The second most significant specific finding was that at a 1% significance level those with free medicine had about one and a half fewer days per year when they could do their normal activities. This effect was also to be expected, due to time needed for doctor appointments.

The third most significant specific finding, and strongest unexpected one, was that people with free medicine had lower blood pressure, at a 3% significance level. But a study that looks at thirty measures in total should, just by chance, find one unexpected finding that seems significant at the 3% level. So taking data mining (i.e., searching for results) into account, this blood pressure result should be set aside.

Many summaries of the RAND experiment, however, trumpet a “risk-of-dying” index result that ignores data mining effects. After seeing the experimental results, researchers choose an index based on smoking, cholesterol, and blood pressure. While overall those with free medicine did not have a significantly lower risk of dying, researchers found an index threshold such that those initially above this threshold later had a 20% lower risk under free medicine.

Some say this result is significant at the 3% level, but this calculation completely ignores data mining effects. And even if this overall risk-index effect were real, it would represent about fifty days of life gained for the average patient, paid for by roughly 30% more medical spending over a lifetime.

The RAND experiment most clearly addressed the health value of the extra medicine consumed by those with free medicine. But it gave hints about the health value of the common medicine all patients consumed: there were no significant differences in either severity of diagnosis or appropriateness of treatment between common and extra medicine. If common medicine is healthier than extra medicine, it is not because common medicine deals with more serious cases or uses more appropriate treatments.

Let us now summarize and interpret these results. Medicine is composed of a great many specific activities. Presumably some of these activities help patients, some hurt patients, and some are neutral. (Don’t believe medicine can hurt? Consider the high rate of medical errors, and see the Fisher & Welch Journal of American Medical Association 1999 theory article.)

We have observed many kinds of disturbances which change the distribution of medical activities, such as variations in local medical culture, local wealth levels, medical prices, and so on. Taken at face value, our inability to see much health impact from the disturbances we have observed suggests that such disturbances increase or decrease helpful and harmful medicine in roughly equal amounts.

This in turn suggests that if we were to reduce medical spending via a disturbance similar in character to the types of disturbances we have observed, such a spending reduction would also reduce helpful and harmful medicine in roughly equal amounts. The claim is not that there would be no harmful health effects of such a policy, but rather that harmful effects would be roughly balanced by helpful effects. And the claim is not that harmful and helpful effects would exactly balance, but rather that any net health harm will be small compared to the health gains possible by spending the savings on other health influences, and to the utility gains possible from spending the savings in other ways.

How much could we cut? For the U.S. it seems reasonable to project the 30% cut in the RAND results to a 50% cut, since the U.S. spends so much more than other nations without obvious extra health gains. I thus claim: we could cut U.S. medical spending in half without substantial net health costs. This would give us the equivalent of an 8% pay raise.

How should we cut medical spending? There are many possibilities, and I may prefer some possibilities to others. But I do not want such preferences to distract from the main point: most any way to implement such a cut would likely give big gains. The obvious first place to cut would be our government and corporate subsidies for medicine, including direct payments, tax exemptions, and regulatory requirements. Socially, we should also try to give medicine far less prestige than we now do. After these one could consider taxing medicine, limiting it by law, or nationalizing the industry and using agency budgets to limit spending.

Yes, I know, these are not politically realistic proposals. But at least health policy experts should publicly contradict those who overemphasize medicine, including politicians whose “health policy” is mainly medical policy, and newspapers whose “health” news is mainly medical news. Furthermore, health policy experts should not themselves mainly research and teach medicine.

If health policy experts hesitate on my proposals due to doubts about how much we can rely on the RAND experiment and correlation studies, then they should at the very least immediately and fully support channeling available funding into repeating the RAND experiment today, ideally with more patients treated longer. Treating ten thousand patients should cost only one part in forty thousand of annual U.S. medical spending, an incredible bargain if it has any substantial chance of overcoming resistance to cutting medical spending.

Do you have little voice in health policy or research? Then at least you can change your own medical behavior: if you would not pay for medicine out of your own pocket, then don’t bother to go when others offer to pay; the RAND experiment strongly suggests that on average such medicine is as likely to hurt as to help.

Let me now consider some objections.

What about studies suggesting larger benefits in particular areas, e.g., immunization, infant care, and emergency care?

Yes, there are categories of medicine where larger benefits seem plausible, and where empirical studies support such claims. (See, for example, Filmer & Pritchett Social Science and Medicine 1999 and Joseph Doyle 2007.) And I have no problem supporting policies to increase spending for such medicine relative to other kinds of medicine. But if your argument is that we must spend lots on medicine overall in order to gain benefits from these particular categories, then I think you have missed the whole point here. We already knew some medicine is more helpful and some more harmful than average. It doesn’t really help to know which part is which unless we are willing to somehow act on that information, and treat better parts differently from worse parts.

What about health and innovation externalities?

Your health may give positive benefits to others, but most medicine on the margin seems to have little to do with health. We can (and do) subsidize health directly, such as by paying people for each extra year of life they live. Medical innovation may well increase the possibly high value of the first half of medical spending, and I do not suggest cutting research budgets. I am, however, skeptical about the innovation benefits of the second half of medical spending practice; how much can a practice environment that tolerates as much harmful as helpful medicine really encourage practice to become more helpful?

What if everything has changed recently?

Overreliance on medicine seems to be quite ancient and widespread; historians suggest that until recently our ancestors would have been better off avoiding doctors. Yes, perhaps most of the apparently useful treatments we know were developed in recent years. But we should expect this from the fact that medical spending as a fraction of GDP has been doubling roughly every thirty years, mostly via spending on new treatments. At all times during such an expansion most treatments should be new. But if there are any doubts, please, let us redo the RAND experiment.

How could we be this wrong about medicine?

If you wonder how the usual medical literature could give such a misleading impression of aggregate medical effects on health, I will point to funding and publication selection biases, statistical tests ignoring data mining, leaky placebo effects, differences between lab and field environments, and the fact that most treatments today have no studies. If you wonder how medicine could suffer so much more from such problems than other subjects, I’ll point you to my forthcoming Medical Hypotheses article, wherein I suggest humans long ago evolved a tendency to use medicine to “show that we care,” rather than just to get healthy.

Briefly, the idea is that our ancestors showed loyalty by taking care of sick allies, and that, for such signals, how much one spends matters more than how effective is the care, and commonly-observed clues of quality matter more than private clues. So today we spend enough to distinguish ourselves from people who don’t care as much as we do, and we pay little attention to private clues about the health effectiveness of medicine. Since loyalty signals can be privately beneficial and yet socially wasteful, my proposal to cut medical spending in half could still be a good idea.

Robin Hanson’s essay has some good features, some bad features, and some undeveloped themes.  Let me pick up on each.

First the good.  Hanson is correct that a lot of medical spending doesn’t add much value.  Research by Elliott Fisher and colleagues at Dartmouth shows that Medicare spending varies by a factor of two across parts of the country, without comparable health benefits.  Fisher et al. estimate that if the high spending areas were brought to the level of the lower spending areas (or more accurately, the 25th percentile of the distribution of spending), we could save 25 to 30 percent of Medicare spending.  No one doubts that the same is true about non-Medicare spending as well.

On top of this excessive provision of medical care is administrative waste.  Insurance administration is 14 percent of premiums in the United States, compared to less than 5 percent in countries with a single payer and perhaps 10 percent in countries with mixed public-private systems.  A good share of this additional administrative expense is for screening groups to determine who is healthy and sick, and for marketing.  It is not for computerization, practice reminders, or other valuable services.  Thus, we could happily eliminate it.  In addition, there are further administrative expenses in physicians’ offices and hospitals.  Dealing with multiple insurers adds considerably to expense, not to mention headaches.  Woolhandler and Himmelstein estimate that total administrative expenses are 31 percent of health care in the United States, compared to half that in Canada.  Without a single payer-system we could not get to Canadian levels, but 10 percent savings is not unreasonable.

If one takes the 25 percent of care that needn’t be provided and 10 percent in unnecessary administrative expense, that’s 35 percent of the nation’s medical bill that could be eliminated without loss.  Allow for further savings from information technology, reduced errors, investment in disease management, or generation of comparative effectiveness information, and the savings could approach 50 percent.  The potential savings are as high as Hanson guesses.

As a sociological observation, I am surprised by Hanson’s argument that this hasn’t been much noted.  The work of the Dartmouth team has received enormous media attention, including front page coverage in the New York Times, for example.  My book suggests large possible savings as well.  And the knowledge that non-medical factors are important for health has been amply documented in many contexts.  If Hanson wants to add his agreement to this array of research, I’m all for it.

Medical care is currently 16 percent of GDP.  In 1975, it was half that amount.  Reading Hanson’s essay, one might conclude that we were better off in 1975 than today.  This is wrong, and it gets to a key part of why Hanson’s analysis is too simplistic.  The most important reason why medical costs increase over time is because we develop new ways of treating patients and provide that care to ever more people.

Consider the most expensive part of medical care: care for people with cardiovascular disease.  In 1950, a person with a heart attack received bed rest and morphine (to dull the pain).  That was how Dwight Eisenhower was treated when he had a heart attack in 1955.  This therapy is not very expensive, but it is also not very effective.  Today, such a person receives clot-busting drugs and other medications, and intensive interventions such as bypass surgery or angioplasty.  These technologies are certainly costly.  Spending in the few months after a heart attack is about $25,000 per patient.  And yet the care provides enormous benefits.  Mortality in the aftermath of a heart attack has fallen by three-quarters since the 1950s.  The average person aged 45 will live 3 years longer than he used to solely because medical care for cardiovascular disease has improved.  Virtually every study of medical innovation suggests that changes in the nature of medical care over time are clearly worth the cost.

If you don’t want to take this on faith, take a quick quiz: would you rather get today’s medical care at today’s prices, or be given $5,000 per year but only get medical care at the 1975 level — doctors trained at that level, hospitals with equipment from that level, and drugs available then?  The $5,000 is the increase in real per person medical spending in the past three decades, so this is asking you to choose today’s care at today’s prices or 1975’s care at 1975’s prices.  I have yet to encounter more than a handful of people who want the standard of care in 1975, even with the money back.  My quasi-market test always confirms that the care is worth it, taken as a whole.

Reconciling this finding with the fact that there is a lot of waste is not hard conceptually.  We provide a lot of valuable care and a lot of care with little value.  Over time, the technology profile is dominated by the valuable care.  But like the fat man at the buffet, we cannot stop ourselves from too many helpings.

The problem in medical care is how to separate the good from the bad.  What can we do to maintain the services that are very effective but get rid of the waste?  Hanson’s essay reads as if a meat cleaver is appropriate (“most any way to implement such a cut would likely have big gains…”).  For a delicate operation, though, I prefer a scalpel.

Broadly speaking, there are two approaches to eliminating waste in medical care.  The first is the demand-side approach: give patients more information about what is effective, raise the price that they pay for using care, and rely on informed choices to produce efficient outcomes.  The alternative is the supply-side approach: invest in information technology, monitor what physicians do, and pay providers more for better care than for less good care.

As befits a Cato Institute argument, Hanson implicitly favors the demand-side approach.  I suspect this is wrong.  To spark discussion, let me state my sense of the literature fairly strongly (too strongly, perhaps): we have no evidence that consumers facing higher cost sharing will make the right medical care decisions.  Indeed, the evidence suggests the opposite.

It is very clear that medical care consumers respond to prices of medical services.  The Rand experiment shows this, as do countless experiments since then (for a review, see my chapter with Richard Zeckauser in the Handbook of Health Economics.) But consumers appear to cut back indiscriminately.  Consider prescription medications.  When cost sharing was raised in the Rand experiment, people stopped taking anti-hypertensive drugs.  When firms raise the price of one medication in a class, some consumers switch to cheaper drugs in that class, but others stop taking the medication entirely (see here and here and here).  Although this is not known with absolute certainly, the people who stop taking the medication do not appear to be those who benefit from it the least.  Rather, price sensitivity seems to vary in the population, just as family history of cholesterol and hypertension do.  It is not unlike saving for retirement.  Some people save and some do not, and the two groups are not particularly associated with need.

In light of these findings, the demand-side approach is wrong for many, perhaps most of the population.  Rather, I see no alternative to thinking clearly, systematically, and expansively about the potential role for supply-side policies to improve the delivery of medical care.  Carefully targeted evaluations of what is done and how to pay for it better are the fundamental way that we can eliminate the waste in medical care but still retain the valuable core.

—

David M. Cutler is the Otto Eckstein Professor of Applied Economics, Harvard University and a Research Associate of the National Bureau of Economic Research.

Robin Hanson wants to cut the medical baby in half — an ironic twist given that he is half right.

He is certainly correct that the role of medicine has been overstated. Health care can only do so much to improve population health. Much of the dramatic mortality improvements over the last century can be attributed to changes outside of the medical system: better nutrition, improved sanitation, reduced smoking (at least over the last half century), and other public health improvements are but a few examples.

Diseases like hypertension, cancer, and diabetes explain much of a person’s longevity and quality of life, and medicine can do little to prevent them. Onset is mainly linked to genes, socioeconomic status, environment, viral exposure, and perhaps most importantly, health-related behaviors. The rest is random luck.

He is also correct that the strongest evidence comes from the RAND Health Insurance Experiment (HIE). The HIE randomized families to health insurance plans of varying generosity. One of the main findings of this experiment was that families in the least generous plan (95 percent coinsurance) spent nearly 30 percent less on medical care with little or no difference in health. Although there was not compelling evidence that higher cost-sharing led to worse health outcomes, low-income participants who were in poor health appeared to be more vulnerable to adverse outcomes. For example, poor people with high blood pressure had slightly higher mortality rates if they had higher cost-sharing. While Robin Hanson rightly questions the statistical criteria in this study, the relationship between insurance and outcomes has been corroborated in other settings — for example, when Medi-Cal suddenly and unexpectedly removed eligibility for certain hypertensive patients, and their blood pressure control worsened.

However — and where Robin Hanson goes astray — is that once someone has a disease, the health care system now offers valuable therapeutic choices. The HIE is more than three decades old, and in that time period many new therapies have emerged. Drugs in particular can be very effective when used properly. For patients with HIV, congestive heart failure, high cholesterol, diabetes, and schizophrenia, evidence is emerging that drugs are providing long-term health benefits. One study found that HIV drugs alone have generated more than one trillion dollars in value. In fact, better treatment and drugs are responsible for approximately half the decline in U.S. deaths from coronary heart disease from 1980 to 2000.

There is a lot of waste in the system, as the evidence cited by Hanson and others makes clear. But how do we eliminate it? Asking people to spend more out-of-pocket clearly isn’t enough. The HIE found that participants in the high cost-sharing group were as likely to reduce “appropriate” as “inappropriate” care, as defined by groups of medical experts. The reality is that there is enormous variability in the benefits that different patients receive from treatment, and hence much of the medical care we buy gets wasted. So, while on average, care is worth it, the marginal dollar may not buy much. There is both overtreatment and undertreatment at the same time. The lesson, then, is that we should be spending a lot less in some areas, but also spending a lot more elsewhere.

I agree with Robin Hanson’s view that the health policy debate is preoccupied with the wrong issues — for example, covering the uninsured. The real challenge for society is deciding how we can best buy better health, not just health care. In fact, I suspect that early child development, education, clean air, and medical research may offer better returns than health insurance and more medical services. But it may also turn out society should be spending more, not less, on medical care — just doing so in a more prudent manner.

—

Dana Goldman is the RAND Chair in Health Economics and the Founding Director of the Bing Center for Health Economics at RAND.

It is much easier to achieve consensus about the claim that there is too much waste in the U.S. health care system than it is to find someone who is willing to take responsibility for this state of affairs. Finger-pointing is inevitable in a health care system with so many players whose interests are diverse and often conflicting. For people fed up with the health care system, and daunted by its complexity, simple solutions — like those that Robin Hanson proposes– are tempting. The principles that should guide health care reform in the United States are simple and universal, such as preserving choice, getting the incentives right, and ensuring markets work the way they should. But successful implementation will not be simple. It will require better information, rational incentives, and public education.

Hanson may well be guilty of overstating his indictment of health care in the United States. As he recognizes, we should not place too much credence in the finding that health outcomes at the national level bear little relationship to national health expenditures. Even better-designed studies, such as analyses of individual observational data, are themselves subject to methodological flaws that call their findings into question. Very few convincingly correct for the effects of unmeasured but important aspects of health. But some of the best studies, such as those of Elliott Fisher and his Dartmouth colleagues, show that regions in which Medicare patients are treated more aggressively have higher expenditures and no better outcomes. These findings do not establish that more aggressive styles of medical care provide no benefit. They lack sufficient detail about the underlying health of patients in different parts of the country to support such a sweeping claim. Nevertheless, if there is anyone who is still complacent about the state of the U.S. health care system, he or she must engage in some heavy rationalization. How to explain, for example, why it is so difficult to find any evidence that more spending generates better outcomes across regions of the United States? And why do disease-specific analyses comparing citizens of different countries fail to show that their greater medical expenditures routinely buy Americans better health outcomes?

Hanson’s diagnosis, therefore, is not particularly controversial. His solution is. Like any attempt to trim excessive spending, it must confront one paramount fact: the benefits of medical care are highly variable. The antibiotic ampicillin is life-saving for meningitis and of dubious value in treating a child’s bacterial ear infection. It only offers the benefits of a placebo, along with adverse reactions like diarrhea, when used to treat the common cold. Ampicillin is frequently used for each of these conditions, and the benefit of ampicillin when used to treat the common cold can be markedly different from the average benefit, which includes its use in far more serious conditions. Of course, variability in benefits is not limited to inexpensive treatments like ampicillin.

Take biological treatments for cancer, which cost tens to hundreds of thousands of dollars per course of therapy. Tarceva increases median survival by about two months in previously untreated non-small-cell lung cancer but only by about 10 days in pancreatic cancer patients. The doses of Avastin used to treat lung and breast cancer are much higher than the dose in colorectal cancer, in which it is more effective. Preventive interventions, which are used in predominantly healthy populations, are expensive in aggregate because they are so widely used. Their benefits are greatest when used by people at high risk of the disease that they are designed to prevent; heart attack survivors can expect large survival benefits from cholesterol reduction, while cholesterol reduction in young women who have high cholesterol levels but no other risk factors for heart disease confers benefits so small that they have never been measured directly. Heterogeneous benefits are the rule, not the exception, in medical care, and they pose both a challenge and opportunity for reducing inefficiency: it is hard to develop policies to reduce selectively the care that is of low marginal benefit, but if we succeed in doing so, we should be able to preserve health outcomes while reducing expenditures.

Hanson’s statement that “most any way to implement [a 50%] cut would likely give big gains” is an invitation not only to ignore the variability in benefits but to encourage policies that would heedlessly cut high-value benefits along with the low-value marginal benefits. Average benefits can be very large, as David Cutler’s work suggests. Yet as Hanson argues, aggressive cuts may well be possible and desirable. How can we selectively cut care that is of low marginal value?

The first step is better information. Many economists and policy analysts downplay the importance of better information, even though economists are almost all familiar with Kenneth Arrow’s explanation that the unique characteristics of markets for medical care – and the reasons that market failure is inherent in this sector – stem from informational failure. The information needed to make decisions about eliminating low-value care is relatively straightforward: we need more information about what works and, in particular, about the value of specific interventions. It’s not difficult to identify interventions at the extremes of value. Think of basic care for high blood pressure as very high value, and perhaps the use of left ventricular assist devices for heart failure as an expensive, very low value intervention. But choosing where to cut means making decisions in the vast gray area between these extremes, and this requires comprehensive information that is simply unavailable today. My co-authors and I, among others, have argued that this information would be best provided by a well-funded federal program that would ensure that the necessary studies are carried out (Emanuel, E., V. R. Fuchs, A.M. Garber (2007). “Essential elements of a technology and outcomes assessment initiative.” JAMA 298(11)). One of its important functions would be to make the information available in accessible form to the general public, so that patients would have the information they need to evaluate management options themselves.

Information can be a powerful tool but it will have little impact if the incentives to use it are inadequate. Hanson identifies government and corporate subsidies to medical care as the problem, but these are incentives for the overconsumption of medical care generally, not for the overuse of low-value care. The changes needed to promote high-value care are inevitably more complex, and we are still learning which approaches will work best. Health plans are gaining experience with health insurance benefit design, such as benefit-based copayments and the selective use of disease management programs, to promote high-value care. They could also improve incentives by adjusting reimbursement rates in fee-for-service settings, which often promote too much of the wrong kinds of care. Medicare changed its rules for reimbursing chemotherapy after recognizing that many oncologists made most of their money by dispensing chemotherapy, simply because their margins on the drugs were so large (and the reimbursement rates for their services were so low). I do not mean to downplay the complexity of correcting reimbursement rates, or to dismiss the outcry that will occur if incomes of some specialists drop as a result. But we should find better ways to compensate physicians than by paying them to perform unnecessary procedures.

Finally, the full costs of an insured individual’s medical care have been so effectively hidden for so long that many Americans have been lulled into a sense that medical care is, or should be, nearly free. Employers have adopted high deductible health plans as a tool to control expenditures on health insurance for their employees, but equally important for them has been a belief that these plans make their employees more sensitive to the costs of care. Certainly employees will be much more reluctant to spend funds drawn from their own health savings account than from an insurance plan. They are much more likely to make prudent decisions if they take costs into account, and if they have good information to guide their choices.

This is true for people who are enrolled in any kind of health insurance plan. As David Cutler and Dana Goldman have noted, observational studies appear to show that patients cut back indiscriminately on the drugs they use when copayments rise. For the most part, these findings are inconsistent with the results of the Rand Health Insurance Experiment, a study with a superlative design that was carried out in a different era, long before pharmacy benefits managers and pervasive tiered copayments. I don’t believe that increased cost-sharing inevitably leads to poor decisions about health care. But too often health plans have simply increased copayments and deductibles without making it easy for patients to decide where to spend their own money. If we want them to succeed, let’s give them the tools they need.

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Alan Garber is the Henry J. Kaiser Jr. Professor and professor of medicine at Stanford University, and is a staff physician at the VA Palo Alto Health Care System.

It is said you can trap a monkey by putting a nut through a small hole in a gourd. The monkey reaches in and grabs the nut, but then his fist won’t fit back through the hole. Greedy monkeys will literally let themselves be caught rather than let go of the nut. So far, no commenter on my essay seems willing to let go of the nut of effective medicine, held in the gourd of the second half of medical spending.

As an analogy, imagine you ran a software company, whose many offices had different wage levels and work cultures, with average work hours ranging from seven to fourteen per day. Surprised to see these offices were equally productive, you randomly changed wages, inducing changes in work hours. You again found offices that worked more did not produce more; after seven hours people got tired and added as many bugs as they fixed. If instead of just cutting wages to get only seven hours of work, you just told everyone “watch out for bugs,” you would be in a monkey trap, refusing to let go of the nut of productive work in the gourd of extra work hours.

I challenged health policy experts to “publicly agree or disagree” that “it has long been nearly a consensus” that since “variations in local medical culture … [and] prices” produce spending variations with little apparent relation to aggregate health, “if we were to reduce medical spending via a [similar large] disturbance … [this would] reduce helpful and harmful medicine in roughly equal amounts.”

Cutler seems at first to agree, saying “if the high spending areas were brought to the level of the lower spending areas … we could save 25 to 30 percent of Medicare spending.” But then he says higher prices are “wrong” because they do not “separate the good from the bad” as “consumers appear to cut back indiscriminately,” such as stopping drugs. Instead Cutler wants “carefully targeted evaluations” of better “supply side policies.”

Goldman agrees “the role of medicine has been overstated,” but also rejects higher prices because it “isn’t enough” to eliminate waste, as patients are “as likely to reduce appropriate as inappropriate care.” Instead, “we should be spending a lot less in some areas, but also spending a lot more elsewhere.”

Garber says my “diagnosis … is not particularly controversial” but rejects “policies that would heedlessly cut high-value benefits along with the low-value marginal benefits.” He instead wants “changes … to promote high-value care” though these “are inevitably more complex, and we are still learning which approaches will work best.”

Shannon Browlee’s Overtreated, published today, argues “between 20 and 30 cents on every health care dollar we spend goes towards useless treatments and hospitalizations.” Yet even she will not support crude price increases or spending caps (personal email).

Bloggers Matt Yglesias and Ezra Klein reject higher prices because “patients … will just cut care indiscriminately.” Tyler Cowen similarly shrugs “I’m not sure what mechanism will get rid of the bad half” of spending. (Arnold Kling, Bryan Caplan, and Seth Roberts seem more sympathetic, but take no explicit position.)

I’m all for finding better ways to favor helpful over harmful medicine, but since we have no consensus on how to do this, why must this distant possibility stop us from publicizing and acting now on our consensus that we expect little net health harm from crude cuts?

Critics seem to me to suffer a “leave no man behind” obsession that makes the best the enemy of the good. No one seems to have denied that there would be little or no average health cost from simple crude cuts, like price increases and spending caps, which could, for example, give us all a 4% raise by cutting one quarter of medical spending. “Indiscriminately” cutting helpful and harmful medicine in equal measure can save big on spending. Yet no one seems willing to endorse such gains. It looks like a medical monkey trap; the option to run free of the extra spending gourd seems intolerable compared to the hope of extracting the nut of effective medicine from that gourd.

But apparently I stand alone; what am I missing? Help me see your reasoning. Please, pick one or add another:

• Do you claim aggregate studies on balance do show spending increases from observed disturbances, e.g., price variations and local practice variations, give substantial health gains, relative to other possible spending options?

• Do you claim the existence of identifiable treatments with positive benefits, which are cut when spending is cut, shows that aggregate spending variations do give substantial aggregate health gains?

• Do you claim disturbances from simple crude cuts like price increases or spending caps are not in fact similar to disturbances seen in aggregate studies, in the sense of similarly changing the mix of helpful versus harmful medicine?

• Do you argue that savings from medicine cuts would in fact be spent on general utility gains, instead of health gains, which matter little relative to health gains?

• Do you claim that implementing simple crude policies like price increases or spending caps today would make it much harder to implement other policies later, policies likely to be implemented soon and which offer larger gains?

• Do you argue that it is immoral to ever “leave a man behind” to disease, even if this tends to hurt as many in the attempt as it helps?

One last comment: In the RAND experiment all patients got common medicine while only patients who faced lower prices got extra medicine. Since extra medicine had little health value, if medicine has a big average value, then common medicine must be more valuable than extra medicine. Since patient choices determined common versus extra medicine, patients were by definition able to distinguish them. Doctors, however, could not distinguish them. For example, doctors rated both types of medicine as the same appropriateness of care and severity of diagnosis. Thus patients were actually better quality discriminators than doctors.

My head is dizzy from all the metaphors! What was the monkey doing working in a software company anyway?

There is a very simple identification problem. We know that on average our medical care is worth it — as David Cutler and others have shown. If we cut half — without knowing what to cut — we will likely cut half the value. Yes, we will eliminate some waste, but we also will eliminate some valuable care.

Raising prices does not solve the problem. It is true that the lesson of the Health Insurance Experiment was that we could reduce spending about 30% with minimal loss of health for those with generous coverage. But that was only for those with generous insurance, and it only applied to late 1970’s medical care. We know patients will reduce use of very efficacious care when you raise prices—even care that will save the patient money down the road.

So where do I think Robin Hanson goes astray? He asks:

Do you claim the existence of identifiable treatments with positive benefits, which are cut when spending is cut, shows that aggregate spending variations do give substantial aggregate health gains?

My response is yes. The research on the use of ACE inhibitors by diabetics is a good place to start, but there are many more drug therapies that qualify. And the reason the variations literature ignores it is because they look at treatment after a serious event occurred — when many of the dollars are spent. Heart attacks are the canonical example. And price sensitivity is non-existent once you have had a heart attack. Raising patient cost-sharing will not affect whether the doctor does angioplasty or not, because any reasonable effort to apply prices will have a marginal cost of zero once a catastrophic cap is met.

The irony, though, is that some of these heart attacks could have been prevented if patients took their prescriptions, and here is where aggregate spending variation gives substantial gains. We know patients under-use many medications for primary and secondary prevention.

So I really question whether higher prices will trim the right kind of care. Furthermore, doctors do not behave in a vacuum. We know from studies of C-sections, for example, that when doctors are faced with a loss of income — perhaps due to a decline in fertility rates — they respond by doing more of these expensive procedures. So there is a supply response that we need to consider as well.

Robin Hanson also asks:

Do you argue that savings from medicine cuts would in fact be spent on general utility gains, instead of health gains, which matter little relative to health gains?

If medicine — and insurance against its costs — have no value, why does everyone purchase so much? In our voluntary system, people have the option of avoiding health care except in extreme emergency situations. They can work for an employer who doesn’t provide insurance, and they can choose as much or as little care as they like. Why is this rare? It is precisely because medicine — and insurance against its financial risk — is so valuable. There is no grand conspiracy that has deluded 250 million people into thinking that health care has value, when in fact it does nothing. Clearly there is a lot of value overall; the issue is how we trim the fat, as David Cutler and Alan Garber point out.

I asked:

Do you claim the existence of identifiable treatments with positive benefits, which are cut when spending is cut, shows that aggregate spending variations do give substantial aggregate health gains?

Dana Goldman replied:

My response is yes. The research on the use of ACE inhibitors by diabetics is a good place to start, but there are many more drug therapies that qualify. And the reason the variations literature ignores it is because they look at treatment after a serious event occurred ­ when many of the dollars are spent. Heart attacks are the canonical example. And price sensitivity is non-existent once you have had a heart attack. Raising patient cost-sharing will not affect whether the doctor does angioplasty or not, because any reasonable effort to apply prices will have a marginal cost of zero once a catastrophic cap is met.

Dana, since you did not mention the first or fifth options I offered, I’ll presume you grant that simple crude cuts would produce disturbances similar to those seen in aggregate studies, and that such aggregate studies appear, at least to the untrained eye, to show little relation between health variations and medical spending variations. So the question is: why exactly do you think aggregate studies are misleading?

You complain the variations literature ignores ACE Inhibitors “because they look at treatment after a serious event occurred.” Yes, such studies often focus on post-event variations, to better control for initial patient health variations, but many variation studies also consider completely aggregate health and medical spending. Could you explain why you think these other aggregate studies are misleading, in our usual statistics terminology? That is, for example, are specific important controls missing, are the models mis-identified, or is there publication selection bias? Also, if you accept the post-event results, then you should at least support crude cuts to reduce post-event spending.

And how could the existence of a few “identifiable treatments with positive benefits” be much evidence against the claim that extra medicine has roughly equal amounts of helpful and harmful medicine, relative to the claim that it has more helpful than harmful medicine. Perhaps if you had randomly sampled from possible treatments, then the fact that a sample turned out to be helpful might be weak evidence. But surely you aren’t claiming ACE inhibitors are randomly sampled.

Also, you complain the RAND HIE “was only for those with generous insurance, and it only applied to late 1970’s medical care.” But this at least suggests cutting spending for those with generous insurance, at least to the extent that the relative mix of helpful and harmful medicine has not changed. And what would be the evidence that this mix has changed? Again, it cannot simply be the existence of some known-to-be-helpful medicine, right?

What is the effect of a price increase?  The conventional answer that quantities consumed will decrease along a demand curve doesn’t always apply in health care.  The question isn’t even answerable without describing which price is being increased and specifying the mechanism for the price increase.  The price of a medical product or service usually means the price paid by insurers, along with the copayment and other out-of-pocket payments by the consumer.  But the copayment may be a small part of the total, and the copayment may not rise at all with a price increase.  An increase in the price the manufacturer charges for a drug, whether it’s branded or generic, typically will not result in an increase in the out-of-pocket cost to the consumer if the drug remains in the same tier of a tiered formulary.  It is the very lack of price sensitivity that is able to support such high prices for biological products used to treat cancer.

The price that matters for consumption, of course, is the price faced by consumers.  High-deductible health plans are designed to increase the price directly paid by individuals for the care they consume.  It is still too early to know whether high-deductible plans drive consumers to make better health care choices.  But price increases of this kind are generally unachievable in traditional Medicare.   If Medicare copayments are increased in an effort to increase the price faced by the consumer, much of the time there will be no effect on consumption, because most Medicare beneficiaries have supplemental insurance that pays for a large share of Medicare copayments and deductibles.  It’s not clear how the Centers for Medicare and Medicaid Services could raise the prices faced by beneficiaries who have a second layer of insurance coverage.

Capping or eliminating the tax exclusion for health insurance would be one way to increase the effective price of insurance, and it would undoubtedly diminish the incentives to consume too much care.  But considering the uproar over any attempt to limit the tax exclusion for health insurance — think of President Bush’s proposal to cap the exclusion at $15,000 for families and $7,500 for individuals — this does not seem to be a policy that will go anywhere.  It would have better chances if it were linked to features that ensured that patients who could derive a great deal of benefit from high-value treatments would continue to get them.  And that requires changes in benefit design, not changes in the tax treatment of health insurance.

So developing a policy to raise prices faced by consumers, and to ensure that it has the desired incentive effects, is a tall order.  It’s better to do it the right way — selectively — than crudely.  I don’t think that changes in reimbursement that would lead to selective reduction in low-value services are beyond our reach, even though we are still learning about the best approaches.  I’m not sure what “simple crude cuts” mean, but if they’re not well placed, adverse health effects are inevitable and could be large, and are likely to be experienced first by the most vulnerable populations.  The complexity of redesigning insurance is a small problem compared to the consequences of ill-directed spending cuts.

Per capita income in the United States is 30 percent higher than in Sweden, and yet Americans are no happier than Swedes; indeed, Swedes report greater levels of happiness. Based on these data, can one conclude that cutting income in the United States by 30 percent across the board would leave Americans unaffected? Of course, this is folly. It cannot be the case that 30 percent of U.S. GDP is not contributing to material improvement. Rather, Sweden makes up for lower average incomes with a more equal income distribution and with the provision of more social goods. The net effect on happiness is a wash.

Let me translate the analogy to medical spending. We observe that areas that spend 30 percent more than other areas do not get better outcomes. It is possible that randomly cutting 30 percent of spending in high spending areas would not affect outcomes. But this is not likely. Cutting spending by 30 percent would almost surely eliminate some valuable services as well as some less valuable ones. One could make up for the loss of valuable services by providing other services that are low cost but not currently provided. Overall health might not be affected, but money would be saved. Better still would be to selectively eliminate the care that has little value and provide the other services that are valuable but are not currently provided. This would leave us spending less and with better health. I believe we can do this, but the task is harder than it seems at first pass. We don’t do ourselves any favors by pretending it is easy.

I said I’m “all for finding better ways to favor helpful over harmful medicine” but I asked “why must this distant possibility stop us from publicizing and acting now on our consensus that we expect little net health harm from crude cuts?” and I suggested six possible reasons one might offer.

Dana Goldman pointed to one of those reasons, specific identifiable beneficial treatments, and he and I are now discussing that line of reasoning. Alan Garber seems to endorse at least one large crude cut:

Capping or eliminating the tax exclusion for health insurance would be one way to increase the effective price of insurance, and it would undoubtedly diminish the incentives to consume too much care.

Alan notes that such policies seem politically unrealistic now, a point I grant. My goal here has been to get health policy folks to publicly admit that their data suggests little net health harm from crude cuts.

David Cutler responded with an analogy to data on happiness and GDP. He asks: if studies suggest aggregate variations in happiness seem unrelated to aggregate variations in GDP,

can one conclude that cutting income in the United States by 30 percent across the board would leave Americans unaffected? Of course, this is folly. … Sweden makes up for lower average incomes with a more equal income distribution and with the provision of more social goods. The net effect on happiness is a wash.

David, in this case you seem to be suggesting that aggregate happiness studies are missing adequate controls, i.e., you suggest that happiness studies which controlled for income equality and social goods would in fact show that aggregate variations in GDP are substantially related to aggregate variations in happiness. This is exactly the kind of specific critique that I request for aggregate studies on medicine and health. Please, why, specifically, are such studies misleading? For example, what particular controls are missing?

Robin Hanson asks David Cutler what is missing from aggregate studies. I can’t help but provide just one example. Suppose some physicians in Region A are better at surgery, while those in Region B do a better job with medical management of heart attacks. And further assume that surgery is more expensive — hardly a stretch. Health outcomes may be very similar across the areas, but costs are higher in Region A because they tend to use more surgery.  If we forced physicians in Region A to practice medical management instead of surgery, we might save some money, but we would adversely impact patient health because the doctors in Region A aren’t as good at medically managing patients as those in Region B.

Now, I am not arguing that differential practice styles explains all — or even much — of the geographic variation we see.  But this example points out that there is much that we need to understand before we take the scalpel to health care spending. The effort to better assess outcomes is a necessary first step in that direction.

Let me ask the following of Robin Hanson. Suppose we could design a catastrophic health plan, with first dollar coverage exceptions for those therapies that can be shown to be clinically efficacious and reduce total health care spending. (I am thinking of exceptions like free anti-hypertensives which prevent heart disease, but not services like “well-baby” care, which — while very popular with young families — are hardly cost-saving.) Would you be in favor of subsidizing this coverage for the low-income uninsured, with the subsidies paid for by eliminating the tax exclusion for health insurance premiums above the cost of this limited plan?

Dana Goldman suggests “a catastrophic health plan, with first dollar coverage exceptions for those therapies that can be shown to be clinically efficacious and reduce total health care spending” and asks if I’d “be in favor of subsidizing this coverage for the low-income uninsured, with the subsidies paid for by eliminating the tax exclusion for health insurance premiums above the cost of this limited plan?”

Yes, relative to the status quo.  But the role of health policy experts is not to say what policies we personally favor, but rather to make policy consequences clear to the public.  Once we tell the public that such a policy would not much harm the health of the non-low-income, then it is up to them to decide if they want to use the savings to subsidize low-income coverage.

Dana gives an example of “what is missing from aggregate studies”: some regions could spend more to get the same health outcomes because their physicians specialize in more expensive treatments, and that if we forced those physicians to instead specialize in the cheaper (but equally effective) treatments done in other regions, “we would adversely impact patient health because the doctors in [expensive regions] aren’t as good at” cheap treatments.   Well, yes, any industry must pay transition costs to switch from less to more efficient technologies, and for this transition physicians would have to retool to get good at cheaper treatments.  But the fact that change is costly is not by itself a reason not to change.   And this consideration is only “missing” from aggregate studies if you thought they implied zero costs of change.

Dana says “this example points out that there is much that we need to understand before we take the scalpel to health care spending.”  There is always more we could understand about any policy choice, so that by itself is not a reason to delay action.

I suggested supply-side differences might explain aggregate differences in how we ‘produce’ health. Robin Hanson replied that he would like to see the system become more efficient. But what if the regional differences are due to demand-side factors? Maybe people in Miami prefer surgery, and people in Minnesota prefer medical management. In that case, retooling by fiat would result in a loss of welfare (although not necessarily in health). The point is that we need to understand whether it is demand or supply-side factors driving these differences in aggregate outcomes.

If it is merely a matter of retooling the system to get cheaper at producing health, then I suspect we are all in agreement. The real question is how we get there. If we merely raise prices, what is to prevent us from returning to this equilibrium a few years later? I believe insurance and tax reform — of the sort already mentioned — combined with financial incentives linked to outcomes assessment could get us there.

Consider a health policy issue like child car seats in the U.S., mosquito netting in the third world, preschool education for poor children, or the immunization of immigrants. Imagine that for this issue there were many good studies over several decades, including some recent studies. Imagine that after controlling for many factors, these studies usually found that variations in spending or usage were significantly, substantially, and positively related to variations in health. Furthermore, imagine this result was confirmed by a thirty year old randomized experiment.

In this situation I predict most health policy experts would clearly and publicly say that we should act now to promote, e.g., child car seats or mosquito netting, via crude policies like subsidies or mandates. Such experts would not say we should wait for more studies to examine other possible explanations, or to better identify more when, e.g., child car seats or mosquito netting are the most useful, to better target policy.

But when many good studies over decades, and a thirty year old randomized experiment, show little or no relation between aggregate variations in health and medical spending, we see a different reaction. None of the diverse health policy experts commenting here or on other blogs will accept my challenge to say clearly to the public “simple crude cuts, such as price increases or spending caps, would produce little or no net health harm.” None will even join my call to redo the thirty year old RAND experiment again today. Instead, they say we should wait to clarify the health-medicine relation, and focus instead on better policies to distinguish helpful from harmful medicine.

When asked what reasons they have for doubting that existing aggregate studies suggest crude medical cuts will not hurt health, the three commentators here at Cato Unbound do not point to the same reasons. At first none of them will even consider simple crude cuts, but when pushed David Cutler suggests aggregate studies are missing important controls (which he does not identify). Alan Garber dismisses simple cuts as politically infeasible, but does seem willing to endorse lower tax-based subsidies. Dana Goldman first points to “the existence of identifiable treatments with positive benefits, which are cut when spending is cut.” Instead of responding to questions about this, he switches to suggesting high spending region doctors have invested more in learning expensive treatments, and when questioned about this he switches to unmeasured differing preferences; “Maybe people in Miami prefer surgery, and people in Minnesota prefer medical management.”

If aggregate studies had suggested medical spending helps health a lot, I can’t imagine health policy experts being nearly as reluctant to endorse simple crude spending increases. This seems a double-standard.

David, Alan, and Dana’s first comments focused on how to better promote helpful over harmful medicine, and I tried to steer them back to the effect of simple crude cuts.  But on this our last discussion day, let me address this quality issue, by describing a completely supply-based approach to Medicare quality.

Imagine each person has a health plan responsible for paying all his medical expenses.  (Each person still pays his non-medical expenses, like for diet or exercise.)  Each year, the government pays his health plan a dollar amount based on his quality of life figure for that year.  This might be $100,000 if he is healthy, $50,000 if he is disabled, $30,000 if he is in great pain, and so on.  These health evaluations are based on random visits.

Since these payments usually far exceed medical expenses, an auction is used to assign health plans.  The plan willing to pay the government the most gets the (tradable) right to be that person’s health plan forever more.   A full record of his medical history is made available to auction bidders.

In this scenario net payments from the government equal expected medical spending.  Each health plan would have an ideal supply-based incentive to trade medical spending for quality and quantity of life gains, at least for gains reflected in official quality of life payment schedules.  Plans would also have ideal incentives to advise each person on health choices, and people have little reason to distrust such advice.  The government would acquire a financial incentive to hurt health, but public monitoring could prevent them from acting on such incentives.

Employer-provided medical coverage could use a similar mechanism, if a distant third party, unable to harm employee health, was paid up front to become responsible for making annual payments to health plans.  Employers might adjust value of life figures to employee details, and might allow employees to contribute to raise those figures.