The Promises and Pitfalls of Pay for Performance

There's been a great deal of discussion about health care payment reform. Prominent in this discussion is "Pay for Performance" (P4P). The idea is simple -- rather than pay providers based on volume of care (fee-for-service) or number of patients (capitation), tie their payment to a measure(s) of performance. There has been substantial concern about the quality of care delivered to patients, so pay for performance appears to make a lot of sense. Don't we want to reward providers for good performance? Shouldn't this encourage them to provide high quality care?

Unfortunately, this is not as straightforward as it might appear. While the idea of pay for performance is very appealing and intuitive, there are some major pitfalls in implementation. First, let's consider what we want to accomplish. We want to set up a system for paying providers that aligns their incentives with what's best for patients, taking into account the benefits and the costs of treatment. In practice P4P systems are set up by payers to align providers' incentives with their objectives. One question that emerges immediately is whether the payer's objectives are the right ones. If payers do not have the best interest of patients at heart a perfectly designed and effective P4P scheme may work extremely well, but may not be to the benefit of patients. This may be true regardless of whether the payer is public or private.

Aside from the issue of the payer's motivation, there are a number of design issues that are critical for the effectiveness of P4P. This is truly a situation where "the devil is in the details."

A number of issues revolve around how performance is measured. First, "you get what you pay for." Providers will respond to the incentive, but this may come at the cost of less of those things which are not measured and therefore not rewarded. For example, this means that aspects of quality that are hard to measure may suffer. If P4P is at the individual provider level, then informal consults or other aspects of being a "team player" may decline. Second, if the performance measure can be manipulated, then P4P may actually generate perverse incentives. For example, suppose performance is measured by patient outcomes incompletely adjusted for patient severity (as is certainly the case). Then providers may attempt to see only patients who are easy to treat and avoid difficult cases. Third, if the performance measure isn't very accurate then chance will play a large role in measured performance. In this case, provider effort won't play a large role in determining payment, so providers will have little incentive to try hard. In addition, rewards can be perceived as unfair -- some providers who aren't so good will receive rewards and some good doctors won't. How accurate the performance measure is depends (among other things) on the size of a provider's practice. A larger practice with a larger patient population will have more statistically reliable measures of the performance metric. Unfortunately, statistical reliability may be hard to achieve in practice. An article by Nyweide et al. finds that "Relatively few primary care physician practices are large enough to reliably measure 10% relative differences in common measures of quality and cost performance among fee-for-service Medicare patients."

The figure below illustrates the problem with chance and fairness. (Note: This figure and the one below are borrowed from Tom McGuire. His original presentation at the Third International Jerusalem Conference on Health Policy, which I highly recommend, is here.) The "bell curve" to the left represents the performance distribution of "not so good" doctors. Some do better than others on the performance measure just by pure chance. The curve to the right represents the performance distribution of "good doctors." They clearly do better as a group than the "not so good" doctors, but purely by chance some of them will do worse than the "not so good" group. Given a target, a proportion A of the good doctors will end up falling below the target and not getting rewarded. Similarly, a proportion B of the not so good doctors will end up being rewarded. First, if the proportion of good doctors who will fall below the target just by chance is high enough, even good doctors won't bother trying. Second, given that a large proportion (in this example) of good doctors will not be rewarded and some not so good ones will, the system is likely to be perceived as unfair.

Another important factor is the amount of money at stake. If the amount at risk isn't large enough then it won't get providers' attention -- the incentive will be too weak (Ashish Jha has a nice blog post on this, and some other aspects of P4P, here). On the other hand, if the amount at risk is too high, then providers can be placed in the position of bearing too much risk -- a bad event can put their practice under water. This is not only undesirable for providers, it can have undesired consequences -- providers will have strong incentives to avoid difficult patients or to "teach to the test," i.e., distort treatment decisions to ensure meeting measured performance goals. In addition, payers that impose a large amount of risk on providers will have to pay more to have them see their patients and take on that risk.

One way to mitigate accuracy problems in performance measures and risk is to use P4P for groups of providers instead of individuals. Performance measures for groups will have better statistical properties than for individuals and groups of providers can spread risk (pdf). Unfortunately, there's no free lunch. Using P4P for groups weakens individual incentives -- the well known "free rider problem." The larger the number of providers in the group, the weaker is the incentive for individuals (pdf). The weakening effect on incentives can be substantial.

Third, most P4P programs use targets -- there's a measured performance goal and payments depend on reaching that target. Using targets in P4P presents a number of issues. First, how well P4P will work, or if at all, depends critically on where the target is set. Set the target too high and no one will be able to reach it, so no one will try. Set the target too low and everyone will be able to reach it, so no one will have to try. As a consequence, P4P schemes which use targets are very fragile -- how well they will work depends critically on where the target is set. This requires a lot of information on the part of the payer to get this right, especially because where the target should be set will change over time and also across providers. How much providers differ in their responsiveness or abilities to reach the target is also critical.

For example, consider the figure below. Each angled line represents a different provider, e.g. a primary care physician. The horizontal axis is each provider's immunization rate for their patients and the vertical axis is their marginal cost of improving the immunization rates for their patient populations. The lines slope up, indicating that the cost of getting more patients immunized increases with the immunization rate -- it's pretty easy to get the first patients immunized, they're aware and compliant, but getting the last few patients immunized can be difficult. A fixed target for immunization is set, e.g., 75%, and providers receive a performance payment if they are at or over the target. Now consider four different providers. Provider A is so far below the target that she will never reach it no matter how hard she works, so P4P gives her no incentive for performance. Provider D is so far beyond the target that she will reach it no matter what she does. She also has no incentive for performance. It's only Providers C and D who have any incentive to respond to this P4P scheme -- the rest of the providers will ignore it.

Last, P4P with a target can be wasteful. In the figure above, only Providers B and C respond to the incentive. Nonetheless, they plus Provider D and all of the providers to the right of Provider D will earn a reward, even though only B and C responded to the P4P incentive. This is clearly wasteful.The effect of P4P is small relative to the cost. The extent to which this is true depends on how much providers differ, and where the target is set. For example, in the figure above if all providers were like B or C, then P4P using the target in the figure would work quite well. If the target were set substantially above or below B or C, however, then P4P would likely fail.

In sum, incentives matter, but the problems with P4P are substantial enough that simply using high powered pay for performance schemes may not be a practical or desirable way to try to improve quality or lower costs. Pay for performance has potential, but it has to be used carefully to avoid its pitfalls. It's important to realize that addressing health care quality and costs requires multiple tools and provider pay is merely one of them.

Are Price Controls the Answer?

A recent article in Time magazine by Steven Brill, "Bitter Pill: Why Medical Bills Are Killing Us," is a brilliantly written expose of the excesses and outrages of health care pricing. In reaction to the story, some have suggested the price controls are the appropriate (or the only) way to rectify the situation. A recent story in the Washington Post's Wonkblog, "Steven Brill’s 26,000-word health-care story, in one sentence," suggests that US health care costs and cost growth are so high because we do not use rate setting, i.e., price controls.

In fact, I think it's not easy to establish whether that is indeed the case. We don't get to use randomized controlled trials for health policies or systems, so it's difficult to figure out how effective a policy like rate setting is. Let me start with some simple examinations of patterns in data to see if something jumps out that strongly supports (or contradicts) the assertion that price controls reduce health care costs.

Starting at the most aggregate level, we can compare the growth rates of spending across countries that use price controls for health care with those that don't. The figure below shows the growth rates of health spending for OECD countries from 2000-2009. The US is the main country with a substantial part of its health sector not subject to price controls. Spending by the privately insured in the US is about 50% of the total, so about one-half of our health spending is not subject to price controls. The Netherlands deregulated prices in their hospital sector starting at 10% in 2005 and moving to 34% in 2009, and also for many physician practices, although it's not clear whether the 2000-2009 growth rate reflects any effects.

There does not appear to be a revealing pattern here -- there are some countries that use rate setting, such as Australia, France, Israel, and Italy that have lower growth rates than the US, and some such as Canada,  Finland, and the UK that have higher growth rates. The US is below the OECD average, whereas Finland is above, as is The Netherlands. While I wouldn't put much weight on anything we see in cross-country differences (there are way too many differences across countries besides price controls), nonetheless nothing striking emerges from these numbers.

Another possible source of information on the effect of price controls on spending is the Medicare program. Medicare fixes the prices it pays doctors and hospitals, so it controls prices. The figure below shows per enrollee growth rates for personal health care expenditures from 1970-2011, as calculated by CMS for services covered both by Medicare and by private insurance (Source here, Table 21).



While examining this figure is clearly not a scientific test (there are many other things undoubtedly driving growth rates of spending), nonetheless, if we see Medicare growth rates consistently lower than private growth rates that would lend at least some preliminary support for the notion that rate setting controls costs. As can be seen, sometimes Medicare spending per enrollee grows faster than private spending, and sometimes the opposite. In particular, Medicare spending slowed dramatically in the mid-1980s after the introduction of the Prospective Payment System for hospitals. Private spending growth fell below Medicare in the early to mid-1990s, most likely due to managed care. More recently Medicare spending has grown more slowly than private spending. Over the entire period the average Medicare growth rate is 8.02%, while private is 9.34%. The patterns here are mixed, but the long run average growth rate for Medicare is lower.

The US does have quite a bit of experience with price controls for medical care at the state level, so we can look at evidence on the effectiveness of these programs. Many states used all-payer rate regulation for hospitals during the 1970s and 1980s. The evidence from these state hospital rate regulation programs indicates a mixed pattern of success. The setup and administration of the program played a large role in whether they were effective. Nonetheless, there is evidence that fi nds that mandatory rate regulation program in a number of states did reduce the rate of growth of hospital expenses (by a little more than 1%). I provide a few references here, for those who are interested. While a 1% reduction in spending growth rates isn't very dramatic, if such an effect occurred and was sustained over time it would lead to a substantial decrease in spending over time.This is probably the most relevant evidence, since if rate setting were to be revived it would almost certainly happen at the state level.

This effect of rate-setting pales, however, compared to the estimates of the impact of managed care from a prominent study, "How Does Managed Care Do It?," which found 30-40% lower expenditures (not growth rates) due to managed care in Massachusetts in the mid 1990s. Another prominent study, "Price and Concentration in Hospital Markets: The Switch from Patient-Driven to Payer-Driven Competition," finds that hospital markups fell substantially in California in the 1980s, primarily due to the growth of managed care.

So what do we conclude? My answer is that we don't know what the impact of rate setting (price controls) would be on health care spending in the US. It's possible that rate setting could prevent some of the most egregious practices recorded in the Brill article, but that depends on what's enacted and how it's enforced. Whether rate setting would substantially slow the rate of growth of health care spending isn't clear. Further, the question that must be asked is what is the alternative? There's evidence to suggest that robust price competition, such as we had with managed care during the 1990s, can perform very well in controlling costs. Unfortunately there has been a tremendous amount of consolidation in health care markets since the 1990s, raising serious challenges to competition. Whether the US decides to go with competition or with regulation, we have some serious work to do to make the system we choose work effectively.

Health Insurers Should Not Become Too Big to Care

This post is co-authored with Rein Halbersma and Katalin Katona, who are economic policy advisors at the Netherlands Healthcare Authority and affiliated with Tilburg University, the Netherlands.

A recent Economix blog article in the New York Times (“Health Insurance Exchanges MayBe Too Small to Succeed”), raises the concern that encouraging competition in health insurance exchanges could lead to health insurers that are too small to succeed. It is clear that the bargaining leverage of insurers, which is determined by their size and the presence of alternative insurers, lowers provider prices, and that high provider prices are a serious problem. However, there are several important arguments against letting health insurance markets gravitate towards higher levels of concentration.

First, allowing insurer market power only makes a bad problem worse. Insurer market power often has the political repercussion of leading to a cry for even more “countervailing power” by lobbying health care providers. The end result of such a process can be the worst possible outcome for consumers: market power for both providers and insurers. This process occurred in the U.S. during the 1990s in what was called the “Managed Care Backlash”, and the phenomenon has also occurred in other countries with private health insurers, such as the Netherlands.

Second, if providers have too much market power, that problem is best dealt with directly. Encouraging insurer market power is not an efficient policy for dealing with antitrust issues in provider markets. Provider market power is best dealt with through vigorous antitrust enforcement. The Federal Trade Commission and the Antitrust Division of the Department  of Justice  have renewed their efforts in this area in recent years.

Third, allowing insurer concentration can lead to too much insurer market power and substantially higher premiums. The economic reasoning is that insurer market power limits the incentives to pass on to consumers the discounts they obtain via their buyer power. In extreme cases of insurer monopoly power, the generated savings on provider prices can be more than offset by dramatically higher  premiums.

Fourth, the consequences of too much insurer market power are worse than the consequences of too little. Once insurers have obtained market power, the situation is typically irreversible. Small insurers may always decide to merge –subject to antitrust control-, but “unscrambling the eggs” is impossible in practice.

As the New York Times article rightly points out, one may worry whether some health insurers are too small to succeed against powerful providers. And excessively high prices set by powerful health care providers are a serious problem for health policy. However, when it comes to passing on provider discounts to consumers, we should also ask whether allowing insurer market power simply makes them too big to care.

Health Care Competition Saves Lives

Evidence has been mounting on the impacts of health care competition on the quality of care. The vast majority of these studies look at hospital competition and use mortality risk as a measure of the quality of care. Two recent studies I've done with colleagues analyze the impact of reforms in the English NHS designed to promote competition among hospitals.

In one study, Rodrigo Moreno-Serra, Carol Propper and I examine the impact of these reforms on mortality for heart attack patients and overall mortality. We find that mortality declined substantially more after the reforms for hospitals facing more potential competition than for those that did not. Specifically, we estimate that the reforms led to a 0.3% drop in the mortality rate for heart attack victims, saving nearly 1,000 lives per year. A recent article in Vox (an online economic policy journal) summarizes the results.

In a followup study, Carol Propper, Stephan Seiler and I analyze the impact of the NHS reforms on patient choice of hospitals for heart bypass surgery (CABG). This is summarized in a recent Vox article. We find that NHS patients are much more responsive to quality differences across hospitals (measured as risk-adjusted mortality rates) after the reforms. As a consequence, hospitals are driven to be responsive to patients and compete on the basis of the quality of care. We find that the reform reduced mortality for bypass surgery patients by 3%, via patients choosing better quality hospitals.

While there is still a lot of work to do to better understand the nature of competition in health care markets and impacts on quality, these two studies do add to a growing body of evidence on the topic. The weight of the evidence, in my opinion, is showing that health care competition saves lives. For more general overviews on the evidence, see a recent synthesis piece on hospital consolidation that Robert Town and I wrote for the Robert Wood Johnson Foundation, an earlier synthesis piece by Town and William B. Vogt, a chapter that Town and I wrote for the Handbook of Health Economics , and a recent survey piece on hospital competition under fixed prices by Hugh Gravelle, Rita Santos, Luigi Siciliani, and Rosalind Goudie.

Nothing Focuses the Mind Like a Fiscal Cliff

The Federal government is facing a self imposed deadline to reduce the budget deficit or face a mandated combination of large tax increases or budget cuts. Everyone agrees that allowing the mandated cuts to occur could cause serious harm to the economy, so the White House and Congress will have to come to some agreement (soon) to avoid driving the economy off the fiscal cliff.

The process of confronting this problem provides an opportunity to reform the U.S. tax code. No one likes our current income tax system -- there are a bewildering array of deductions, exclusions, and loopholes. This not only makes it hard for people to complete their income tax forms, it leads to serious distortions in incentives -- costing our economy billions of dollars.

A straightforward solution is to simply eliminate all exclusions, deductions, and loopholes. Under this reformed tax code all income will be taxed -- period. Both earned (wages and salaries) and unearned (capital gains) income will be taxed at the same rate.  Higher incomes will be taxed at higher rates. This will be a truly progressive income tax because individuals with high incomes won't be able to avoid taxes via the shelters and loopholes that are present in the current tax system.  Further, resources will flow to their highest valued use, rather than those that allow the avoidance of taxes.

Pursuing this option will mean eliminating some tax exclusions and deductions that are popular -- the mortgage interest deduction and the tax exclusion of employer sponsored health insurance benefits for example. But not taxing these inefficiently distorts incentives and also means that income tax rates have to be higher. For example, it's estimated that these two features of the tax code alone will cost the Federal government over $1 trillion in foregone tax revenues over the next five years. Further, since the value of these features of the tax code rises with an individual's tax rate, they are regressive. High income individuals derive more benefit from these (since they avoid more tax) than do lower income individuals.

As a practical matter, it's unlikely that we'll actually eliminate all exclusions and deductions from the tax code. It may be desirable to retain some, such as deductions for charitable donations (although even that can be debated), and we'd likely gradually phase out those features to be eliminated. Of course, pursuing tax reform doesn't eliminate the need to address spending, but it can help. It's also important to point out that there still may be a need to increase tax rates, even if serious tax reform is undertaken.

I am not being particularly original or innovative in calling for this kind of tax reform. Many economists, politicians, and others have proposed this kind of tax code simplification over the years. What I do think is that our current set of fiscal challenges, not just the looming fiscal cliff, may finally provide our politicians with the stimulus they need to pursue meaningful tax reform.

While moving from our current system to a simplified tax code would be a big change, I think it's a change that most people would welcome. The current system is widely perceived as arbitrary, byzantine, and unfair. A tax code where all income is taxed would be simpler, more efficient, and fairer. The looming fiscal cliff provides our government with an opportunity for real tax reform -- one they shouldn't pass up.

Is The 716 Billionth Cut The Deepest?

One of the primary items of contention in Wednesday night's Presidential debate was the $716 billion in cuts to Medicare through the Accountable Care Act (ACA). Mitt Romney claimed that this would harm Medicare beneficiaries, while Barack Obama claimed just the opposite, that this would help them. Let's consider these claims.

First Romney's claim. First, let's be clear that there are no payment cuts to doctors included, so there should be no impact of the cuts on physician supply of services. It is possible that if Medicare spending exceeds the target growth rate that the Independent Payment Advisory Board (see nice explanation of the IPAB) could recommend payment cuts to physicians as part of its recommendations to reduce cost growth, but that is not mandated.

The main cuts are payment cuts to Medicare Advantage (MA) plans and cuts in the rate of increase to hospitals and a few other providers. It is possible that cuts in payment rates to MA plans will lead to some of those plans leaving the market. A recent paper by Austin Frakt, Steve Pizer and Roger Feldman finds large impacts of payment cuts on plan participation in MA (blog post by Austin Frakt on the findings, with link to the paper). Medicare beneficiaries who lose their MA plans could simply enroll in traditional Medicare or in another MA plan (if available), so they won't lose coverage entirely or access to services. Studies have shown, however, that beneficiaries in MA plans prefer them to traditional Medicare, so they would suffer a loss from having to switch. In a separate paper, Frakt, Pizer, and Feldman estimate that what beneficiaries lose due to the exit of their MA plan is more than outweighed by the savings from reduced MA payments (blog post on this with link to paper).

There are two main questions regarding the impacts of reductions in the growth of hospital payments. Will hospitals reduce their supply of services to Medicare beneficiaries, and will they reduce the quality of care they provide? The first question is basically about the elasticity of supply -- how responsive are hospitals to changes in Medicare reimbursements? There is surprisingly little direct evidence (so far as I'm aware) on how hospitals' supply of services to Medicare beneficiaries responds to Medicare payment levels. There is a lot of research evidence on hospitals' response to the introduction of the Prospective Payment System (PPS) in 1983, which did have impacts on payment levels. The evidence there seems to be that the PPS payment changes did affect intensity of care, but not volume. More recent work on changes in Medicare reimbursements for different DRGs also finds no volume response (but upcoding: "How Do Hospitals Respond to Price Changes?"Leemore S. Dafny, The American Economic Review , Vol. 95, No. 5, Dec., 2005, pp. 1525-1547). Deductively, we know that Medicare beneficiaries make up a large part of hospital patients. Therefore it's unlikely that hospitals could replace them with more remunerative patients or activities, even if they want to. As a consequence, it does not appear likely that hospitals respond to Medicare payment cuts by seeing fewer Medicare patients.

There is evidence, however, that hospitals do respond to Medicare payment reductions by reducing the intensity of care they provide to Medicare patients. David Cutler ("Empirical Evidence on Hospital Delivery under Prospective Payment," unpublished Paper, 1990) finds reductions in the intensity of treatment by hospitals in response to reimbursement reductions due to PPS. Vivian Wu and Yu-Chu Shen (2011) find evidence that hospitals that experienced large cuts in Medicare payment rates had increased mortality rates for heart attack patients relative to hospitals that had smaller cuts.

So it's possible that hospitals could respond to the reduced growth in their Medicare payment rates mandated by the ACA by cutting back on things that end up harming the quality of care. It is important to realize, however, that the ACA also introduces quality incentives into Medicare payments for hospitals. These are based on clinical quality measures and patient surveys, and penalties for readmissions. As a consequence, it's not clear that slower increases in payments will end up leading to lower quality.

So, it's far from clear that Mitt Romney's claim that the ACA's reductions in Medicare payments will harm Medicare beneficiaries is correct. There is some evidence that points in this direction, but my personal opinion is that it's unlikely.

What about Barack Obama's claim that the Medicare payment cuts will help beneficiaries by keeping Medicare solvent? It extends the solvency of Medicare from 2016 to 2024.  There has been some controversy about the impacts on Medicare spending and the Federal budget. Charles Blahous has claimed that extending the solvency of Medicare actually ends up increasing the government's spending and increases deficits. He assumes that benefits would be cut when the Medicare trust fund is exhausted (the law is that it can't spend more than comes in) and there would be no Medicare deficits. Therefore the Medicare savings from the ACA aren't really savings -- they wouldn't have been spent anyway.

Peter Orszag says this isn't correct -- roughly speaking, the government would have continued to pay for Medicare absent the ACA, so that's not the right basis for comparison and there are true savings. Jeff Brown does yeoman's work trying to sort out the controversy. These views lead to very different conclusions about the impacts of the ACA on Medicare and the federal deficit. My own view is that Peter Orszag is right. I seriously doubt if the government would carry out the benefit cuts that would be required.

Obama's claim about the solvency of Medicare is correct. The implications for spending and deficits are subject to some (very wonky!) debate, but I think the most likely scenario is one in which these are true savings.