Are Price Controls the Answer? Netherlands Edition

This post is co-authored with Misja Mikkers, who is Director of Strategy and Legal Affairs at the Netherlands Healthcare Authority and is affiliated with Tilburg University, the Netherlands and Copenhagen Business School, Denmark.
 

In a previous post one of us (Gaynor) examined some evidence on whether price controls are effective in slowing the rate of growth of health care spending, and how they compare with competition in private markets. In this post we examine some evidence from the Netherlands that may bear on the matter. In the previous post a data point from the Netherlands was shown as part of an international comparison, but it’s worthwhile to examine the Netherlands experience a bit further. The Netherlands is particularly interesting because they have employed rate setting in health care and subsequently deregulated much of their health sector to allow prices to be market determined. The question of whether the end of rate setting and the introduction of competition raised or lowered health care costs and prices in the Netherlands is hotly debated.

The article by Steven Brill in Time also led also to a lot of discussion in the Netherlands. Rob Wijnberg (former editor of the important Dutch Newspaper NRC-next) tweeted, “brilliant article about the reasons why competition in health care doesn’t work" (https://twitter.com/robwijnberg, Feb 26, translation M. Mikkers). 

Health reform in the Netherlands has been gradual and has had a number of different elements. Initially competition in health insurance was introduced (with an individual mandate), while maintaining rate setting for providers. A partial and gradual deregulation of provider prices followed. For hospitals some services have been deregulated (this is called the B Segment) and some services remain under price controls (the A Segment). The proportion of services in the deregulated B Segment has increased over time. In 2005 8% of hospital services were in the B-segment. This percentage then increased to 20% and 30% in 2008 and 2009 respectively. In 2012 virtually all elective care (70% of hospital services) was in the B segment.

The figure below (source: Market Scan Hospital Market 2011, Netherlands Healthcare Authority [in Dutch]) shows the percentage change in hospital prices over time in the price controlled A Segment and deregulated B Segment. As can be seen, growth in the deregulated segment, where prices are market determined, is substantially lower from about 2006-2007 onwards. In fact, from about 2008-2009 to 2010-2011 prices were falling in the deregulated segment while they were still growing in the price controlled segment. This doesn’t necessarily mean that competition controls prices and rate setting does not (lots of other things could be going on), but neither do we see what we’d expect if rate setting was doing a superior job of controlling prices.

The next figure (below, source: CBS [Dutch Central Bureau of Statistics] ) shows the growth rate in hospital spending in the Netherlands in the period before rate deregulation (2001-2005) and after deregulation began (2006-2011). The average annual growth rates between the two periods are virtually indistinguishable. Again, this isn’t scientific proof that rate setting doesn’t control costs (either in general or in the Netherlands), but there’s no slam dunk for rate setting in the patterns that we observe. We may be able to see more interesting patterns in the future. By 2014 and 2015 virtually all prices for elective care will have been deregulated and all parties in the market will be fully exposed to the consequences of their (price) negotiations.  

In sum, the Netherlands is a good place to look for the effects of rate setting versus markets on prices and spending, since they have employed both. A quick look at some descriptive statistics doesn’t yield any slam dunks for rate setting. If anything, shifting to markets may have substantially reduced price growth. However, careful study will be required in order to draw firmer conclusions. Last, while we believe that there are lessons for the US from experiences in other countries (and vice versa), we do have to be cautious in making strong inferences across very different health care systems and societies.

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.