Wasting Money At the End of Life?

Peter Bach is a physician with a recent op-ed appearing in the New York Times. Dr. Bach’s piece about whether or not end-of-life health care spending is wasteful is compelling. I recommend you read his essay, but I want to hit some of the high points.

The first is that we may be making an error based on hindsight. Precisely because hindsight is 20/20, we can fall into the trap of evaluating health care decisions after the outcome is known, which is not how health care decisions are actually made. So, as Dr. Bach stresses, a tremendous amount of health care spending may be deemed warranted if it saves the life of the patient, but the same spending would be labeled wasteful if the patient dies. This is an excellent point.

Other points Dr. Bach makes relate to supposed sources of bias. For example, sick people need more health care (which costs more money) and are also more likely to die. Ergo, the link between health care spending and death is likely to be positive, but the reality is that health status is the common denominator. He also laments the fact that data on end-of-life spending is much more readily available than data on other types of health care spending. That’s also a pretty accurate assertion.

It’s in his concluding remarks that Dr. Bach gets to the real matter at hand: We don’t know who is going to benefit and who isn’t. In his essay, he describes how he saved someone’s life. As it turns out, the condition that person had is fatal in about 1 of every 8 cases. In other words, paying to intervene for that condition seems like a good bet. If, on the other hand, only 1 out of 8 people with the condition survived, it might be a tougher sell. Of course, what that scenario underscores is that the overall costs and benefits are important to understand, but so are the individual risks and benefits. For example, if you have the condition, you are probably hoping that you are the 1 person who will survive after receiving the intervention, not counting on your being 1 of the other 7.

Research that can help us understand not only which procedures are generally more effective than others, but also who the 1 person who loses or benefits (given the two scenarios above) will be, are the next frontier in improving the health care system–cutting costs without harming quality. Of course, this type of research will bring accusations of “death panels” back out of the woodwork. I just hope few people will actually take such things seriously, so that the necessary work can proceed. Without it, I’m not sure that there’s much hope.

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Using Medicare Data to Rate Physician Quality

Last week, the federal government announced that it would allow Medicare claims data to be used for the purpose of disseminating physician quality information to the public. What’s news is not that there will be attempts at creating so-called quality “report cards”–attempts at those have been around for some time–rather it’s that the Centers for Medicare & Medicaid Services (CMS) has finally agreed to let a wide range of folks access Medicare claims data for the purpose, which hasn’t happened before on this level.

But what are we to make of this new development? Is it a good thing or not? Giving the “consumer” more information on which to base their selection of a physician and their use of health care services seems like a good thing. After all, it’s essentially central to the idea of a well-functioning free market. As any health economist will tell you, the information asymmetry between consumers and providers leads to all sorts of peculiarities that cause the health care market not to behave like the market for other goods and services. This could then conceivably be a step in the direction of correcting some of those peculiarities.

The real question, though, is how good will this information be? Or, said another way, is poor information preferable to no information? Now, that doesn’t mean that there’s not a lot of excellent potential in these Medicare claims data. On the contrary, there’s much to be learned here. Of course, the realization of that potential is a function of the empirical rigor of the analyses researchers like myself undertake. No, the real worry I have is how this translates to the lay public without grossly oversimplifying things.

Let’s say a system is devised that, in true “report card” fashion, assigns physicians a grade ranging from “A” for outstanding to “F” for visit at your own risk. The public would certainly understand such a grading system, and people would be expected to show a clear preference for “A”-rated physicians over “F”-rated ones, but what about the bulk of physicians in the “B” and “C” range? It’s entirely possible, depending on the rating algorithm used, that a physician who excels in one particular area nevertheless gets a “C” rating. Would the public do its homework, or would it avoid doctor “C”? I worry that the latter may be the most likely outcome.

Again, I’m not saying that efforts to monitor quality and report that information publicly are a bad idea. Far from it. I’m merely suggesting that we must be extremely thoughtful in how we engage in such efforts, because the potential for significant unintended consequences is quite real. We must figure out how to approach these data using the most sophisticated of techniques, all the while with an eye on translating what we find in a manner that is accessible to the public without being “watered down” or less than accurate. The risks and the rewards are great.

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Adverse Events in Hospitals Higher than Expected

The hospital isn’t really ever a place you want to be. Granted, that’s changed a lot over the last several decades, back when hospitals were just places people went to die. Now, at least, hospitals are places many people go to get better, but they’re still dangerous. All sorts of bad things, known as “adverse events,” can happen within the walls of the hospital. You can catch a nosocomial (hospital-acquired) infection. You can take the wrong medication or too much or too little of the right medication. You can have the wrong leg amputated in surgery. You can be the wrong patient in surgery to begin with. You can develop pressure sores if you are stuck in bed for too long. You can slip and fall if you get out of bed too soon. The list goes on and on.

Identifying these adverse events, so that they may be prevented in the future, is a key aspect of improving health care quality. A recent Health Affairs article by Classen et al. compares several different methods for identifying adverse events. For the layperson, the take home message is: Adverse events are much more common than previously thought. It all depends on how attempts are made to measure them. As it turns out, the Global Trigger Tool developed by the Institute for Healthcare Improvement finds a lot more adverse events than any of the other widely used methods, including one developed by the Agency for Healthcare Research and Quality (AHRQ). What kind of difference are we talking about? Well, in a review of 795 patient records, AHRQ’s method identified 35 adverse events. The Global Trigger Tool identified 354 adverse events. That is what we in the business call “an order of magnitude.” And, compared to local hospital reporting systems that identified only 4 adverse events, we’re talking about two orders of magnitude (i.e., about a 100-fold difference). Just when you thought it was safe to go back in the water…..

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